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It has been recently reported that a side population of cells in nasopharyngeal carcinoma ( NPC ) displayed characteristics of stem-like cancer cells . However , the molecular mechanisms underlying the modulation of such stem-like cell populations in NPC remain unclear . Epstein-Barr virus was the first identified human tumor virus to be associated with various malignancies , most notably NPC . LMP2A , the Epstein-Barr virus encoded latent protein , has been reported to play roles in oncogenic processes . We report by immunostaining in our current study that LMP2A is overexpressed in 57 . 6% of the nasopharyngeal carcinoma tumors sampled and is mainly localized at the tumor invasive front . We found also in NPC cells that the exogenous expression of LMP2A greatly increases their invasive/migratory ability , induces epithelial–mesenchymal transition ( EMT ) -like cellular marker alterations , and stimulates stem cell side populations and the expression of stem cell markers . In addition , LMP2A enhances the transforming ability of cancer cells in both colony formation and soft agar assays , as well as the self-renewal ability of stem-like cancer cells in a spherical culture assay . Additionally , LMP2A increases the number of cancer initiating cells in a xenograft tumor formation assay . More importantly , the endogenous expression of LMP2A positively correlates with the expression of ABCG2 in NPC samples . Finally , we demonstrate that Akt inhibitor ( V ) greatly decreases the size of the stem cell side populations in LMP2A-expressing cells . Taken together , our data indicate that LMP2A induces EMT and stem-like cell self-renewal in NPC , suggesting a novel mechanism by which Epstein-Barr virus induces the initiation , metastasis and recurrence of NPC .
Nasopharyngeal carcinoma ( NPC ) is the most frequent head and neck tumor in Guangdong , South China , where the incidence peaks at 50 per 100 , 000 , but is rare in the Western world ( 1 per 100 , 000 ) [1] , [2] . NPC is a highly malignant cancer which often invades adjacent regions and metastasizes to regional lymph nodes and distant organs . Thirty to 60 percent of patients with NPC will eventually develop a distant metastasis . Although NPC tumors are sensitive to radiotherapy and chemotherapy , treatment failure is high due to local recurrence and distant metastases , which are the key contributors to NPC mortality [3] . However , the underlying cellular and molecular mechanisms of NPC metastasis and recurrence remain poorly understood . The epithelial–mesenchymal transition ( EMT ) is characterized as a switch from a polarized , epithelial phenotype to a highly motile fibroblastoid or mesenchymal phenotype . EMT is critical to metazoan embryogenesis , chronic inflammation and fibrosis , and has been demonstrated to be a central mechanism in cancer invasiveness and metastasis [4] . Recently , Weinberg and colleagues reported that EMT generates cells with stem cell-like properties [5] , which suggests that metastases are sometimes caused by cancer cells that acquire stem cell characteristics . Recent studies have also suggested that cancer stem cells ( CSCs ) represent a small proportion of the cells in a tumor mass and contribute to tumor initiation , metastasis and recurrence . It has been further reported that cancer stem cells are enriched in side population ( SP ) cells which can efflux the DNA binding dye , Hoechst 33342 , from the cell membrane [6] , [7] , [8] . Most recently , Wang and colleagues have reported that SP cells in the human NPC cell line CNE2 display stem cell characteristics [9] . However , the molecular mechanisms underlying the regulation of SP cells in NPC remain unclear . Epstein-Barr virus ( EBV ) , which ubiquitously infects more than 90% of the world's population , was the first human tumor virus identified to be causally associated with various lymphoid and epithelium malignancies [10] . However , the underlying mechanism of how EBV contributes to cancer is still poorly understood . NPC , particularly the undifferentiated type , is the most commonly known EBV associated cancer [11] and three EBV latent proteins are expressed in these tumors [12] , [13] . EBNA1 , whose primary role is to enable replication of the viral episomal genome [14] , is the most widely expressed protein in NPC . However , although both LMP1 and LMP2A are detectable in NPC samples , much of the recent research focus has been on LMP1 because of its known oncogenic properties in B cells [15] , [16] . However , LMP2A has been detected in more than 95% of NPC samples at the mRNA level , and about 50% of these specimens at protein level , whereas LMP1 could be detected in only about 65% or 35% of NPC samples at mRNA or protein level , respectively [17] , [18] , [19] , [20] , [21] . In addition , the high LMP2A expression in NPC samples has been reported to correlate with a poor survival outcome , although this study was carried out using only a small cohort [22] . Functional studies indicate that LMP2A plays an important role in the maintenance of EBV latent infection in B cells but is dispensable for EBV-driven B-cell transformation [23] . In epithelial cells , LMP2A has been reported to have transforming properties i . e . to alter cell motility and inhibit cell differentiation [22] , [24] , [25] , [26] . Activation of the PI3K/Akt , NF-κB , β-catenin , STAT and Syk Tyrosine Kinase pathways has been suggested to contribute to the various functions of LMP2A in epithelial cells and B cells [26] , [27] , [28] , [29] , [30] . ITGα6 is thought to be involved in the enhancement of cell migration mediated by LMP2A [22] . Most recently , LMP2A has been reported to induce promoter hypermethylation of the pten gene in gastric carcinoma [31] . In addition , some of the above functions and pathways modulated by LMP2A have been reported to play roles in regulating the proliferation and self-renewal properties of CSCs [32] , [33] , [34] . These findings thus raise the possibility that LMP2A may affect oncogenic processes by modulating the CSC population in NPC . We report in our current study that the stable expression of LMP2A in NPC cells induces cell invasion and EMT-like molecular alterations . More importantly , the overexpression of LMP2A increases the size of the stem-like cell population and the number of tumor initial cells . Our data thus represent the first indication that LMP2A has an effect on stem cell-like populations and provides additional clues to elucidating the role of LMP2A in NPC progression .
To detect LMP2A protein expression , monoclonal antibodies ( MoAbs ) was raised against a glutathione-S-transferase-fused full-length LMP2A protein ( Proteintech Group Inc . ) . After primary selection by ELISA , five clones were obtained from the Proteintech Group and further characterized by western blotting and immunofluorescence staining . To establish stably expressed LMP2A cell lines , CNE2 and SUNE1 cells were infected with virus expressing either LMP2A in the pBabe vector or with empty vector alone , followed by selection in puromycin . No differences in the efficiency of selection between vector and LMP2A-infected cells were observed . RT-PCR analysis showed that LMP2A mRNA was expressed in both of the LMP2A-infected cell lines ( Figure 1A ) . The expression of LMP2A protein was detectable by immunoblotting with four different LMP2A MoAb clones . Representative results for clone 4A11B3A3 are shown in Figure 1A . In contrast to LMP2A-infected cells , there was no detectable LMP2A mRNA or corresponding proteins in the vector control cells . The membrane localization of LMP2A in the LMP2A-infected NPC cells was confirmed by specific detection with 4A11B3A3 using immunofluorescence staining ( Figure 1B ) . To further determine whether MoAb 4A11B3A3 could detect LMP2A protein in archival NPC patient's biopsies , we tested this antibody in paraffin-embedded nude mice xenograft samples . In accordance with our western blot results , by immunohistochemical analysis we found strong membrane staining of LMP2A in CNE2-LMP2A inoculated samples . No specific staining was observed in CNE2-vector inoculated samples or in IgG detected controls ( Figure 1C ) . We then analyzed endogenous LMP2A expression in NPC patient biopsies with the same MoAb by immunohistochemical analysis and obtained similar results ( Figure 1D ) . Hence , the specificity and sensitivity of this antibody for endogenous and exogenous LMP2A expression by immunoblotting , immunohistochemistry and immunofluorescence analysis was verified . To further investigate the status of LMP2A expression in NPC biopsies , immunohistochemical analyses were carried out and revealed that 19 of 33 ( 57 . 6% ) paraffin-embedded samples showed moderate ( Figure 1D , right panel ) to strong ( Figure 1D , middle panel ) staining of LMP2A in most of the tumor cells and in some scattered infiltrated lymphocytes . No positive staining was detected in adjacent noncancerous epithelial cells . As shown in Figure 1B and E , LMP2A is mainly expressed on the tumor cell membrane and preferentially located at the tumor invasive front . We then tested six archival relapse patient samples and found that were strongly positive for LMP2A expression . These data suggest that LMP2A is expressed in NPC samples at variable levels , that its localization at the invasive front is indicative of a potential role in promoting tumor invasion , and that the LMP2A protein levels may positively correlate with NPC recurrence . It has been reported previously that LMP2A can promote the migratory/invasive properties of different epithelial cell types [35] . As determined by immunostaining , dissected tumor tissue samples from nude mice inoculated with CNE2-LMP2A cells showed a level of LMP2A that was comparable to that found in the NPC biopsies ( Figure 1C and D ) . Hence , the established stable LMP2A expressing NPC cell line was found to contain physiological levels of LMP2A , and could thus be used in further studies of LMP2A function . Consistent with previous reports , the expression of LMP2A could enhance the migratory and invasive ability of NPC cells ( data not show ) . Since the enhanced migratory/invasive ability of epithelial cells is often caused by EMT , we analyzed a panel of representative epithelial and mesenchymal markers by immunoblotting to determine whether this process occurs in LMP2A-expressing NPC cells . The results showed that the overexpression of LMP2A caused an EMT-like marker shift in the cells , including a dramatic downregulation of the epithelial markers E-cadherin and α-catenin , and upregulation of the mesenchymal markers fibronectin and the EMT-associated transcription factor snail , although the change of vimentin was moderate with about 2-fold increase in CNE2-LMP2A cells as analyzed by Quantity One software ( Figure 2A ) . Immunofluorescence staining further revealed that the expression of E-cadherin and α-catenin , which shows membrane localization in control cells , dramatically decreased in LMP2A-expressing cells ( Figure 2B , upper two panels ) . In contrast , the levels of fibronectin , vimentin and snail were strongly induced in LMP2A-expressing cells ( Figure 2B , lower three panels ) . These results thus demonstrate that LMP2A induces EMT-like molecular alterations in NPC cells . However , similar to a previously reported observation in squamous epithelial cells [35] , LMP2A did not induce any obvious morphological changes in NPC cells in monolayer cultures . To exclude the potential effects of selection , we then examined the representative EMT markers after transient tranfection of LMP2A in NPC cells . As shown in Figure S1 , EMT-like molecular alterations were induced by transient expression of LMP2A in both CNE2 and SUNE1 cells . To further investigate whether endogenous LMP2A contributes to the EMT phenomenon , we tested whether NPC cells lacking this endogenous expression demonstrated any EMT-like cellular marker reversal as compared with LMP2A-expressing cells . Following the knockdown of LMP2A in C666 cells ( Figure S2A ) , we found by immunofluorescence staining that the expression of the epithelial marker E-cadherin was up-regulated , whereas the mesenchymal marker vimentin was down-regulated on the membranes of the cells ( Figure S2B ) . These results indicate that LMP2A is necessary for the EMT-like marker shift in NPC cells . It has been reported recently that EMT generates cells showing the properties of stem cells [5] . We thus determined whether stable expression of LMP2A could induce such stem cell-like phenotypes in NPC . Representative stem cell markers were thus analyzed by RT-PCR or western blot . As shown in Figure 3A ( left panel ) , in comparison with the vector control , LMP2A expression up-regulates the stem cell markers ABCG2 , Bmi-1 , Nanog , and SOX2 at the transcriptional level . The increases in ABCG2 , Bmi-1 , SOX2 and Nanog were further confirmed at the protein level ( Figure 3A , right panel ) . As expected , transient expression of LMP2A could also induce stem cell markers , as demonstrated by the increased expression of ABCG2 and Bmi-1 at both transcriptional and protein levels ( Figure S3A ) . Side populations ( SPs ) among NPC cells have been reported to exhibit cancer stem cell characteristics [9] . We wished therefore to determine whether the increased expression of stem cell markers we observed in LMP2A-expressing cells was caused by an increase in the size of the stem cell-like SPs . As shown in Figure 3B , the stable expression of LMP2A dramatically increases the size of the SP in the CNE2 ( from 1 . 04% to 8 . 32% ) and SUNE1 ( from 3 . 38% to 13 . 72% ) cell lines . Importantly , SPs were also increased in transient LMP2A expressing cells CNE2 ( from 1 . 56% to 3 . 65% ) and SUNE1 ( from 3 . 91% to 11 . 37% ) ( Figure S3B ) . Interestingly however , we did not observe any SPs in either wild type or LMP2A knockdown C666 cells . Previously , we have reported that the side population ( SP ) cells , isolated from CNE2 NPC cell line , exhibited cancer stem cell characteristics [36] . Thus , we sorted the SP fraction in CNE2-Vector , CNE2-LMP2A , SUNE1-Vector and SUNE1-LMP2A cells , respectively , and then performed colony formation assay . As shown in Figure S4 , SP fraction from either LMP2A or vector control cells form larger and more colonies compared with the non-SP fraction , confirmed that the stem cell population is indeed within the SP fraction in NPC cell lines . Taken together , our results demonstrate that LMP2A could induce expression of stem cell markers and increase the stem cell population in NPC cells . We next analyzed whether the increase in the sizes of the SPs in NPC is due to the enhanced self-renewal properties of the stem-like cells therein . LMP2A and control cells were cultured in suspension to generate spheres , the number and sizes of which reflect both the quantity and ability of cells to self-renew in vitro [36] . As shown in Figure 3C , LMP2A-expressing cells formed more and larger spheres than vector controls cells did in both NPC cell lines ( CNE2 , P = 0 . 04; SUNE1 , P = 0 . 03 ) . We conclude from this that LMP2A can indeed enhance stem cell self-renewal properties , and thereby increase the size of these populations . To investigate whether LMP2A can enhance the transforming ability of NPC cells , we used both a colony formation and anchorage-independent growth assay in soft agar . We plated 200 NPC cells in triplicate wells of six-well plates for the colony formation assay . After 14 days of culture , LMP2A-expressing cells formed colonies that were significantly larger than those of the vector control cells ( Figure 4A ) . There were also more LMP2A-expressing than vector control colonies . Statistical analysis showed significant differences in the number of colonies between the LMP2A-expressing and vector control cell lines ( P<0 . 05; Figure 4A , right panel ) . In addition , the transforming ability of LMP2A expressing cells was also determined by soft agar assay . As shown in Figure 4B , LMP2A-expressing cells formed significantly more and larger colonies compared to the vector cells in soft agar assay . As SPs are enriched for tumor initiating cells , we next assessed the effects of LMP2A upon the tumorigenicity of NPC cell lines in nude mice . As shown in Figure 5 , when injected with 1×106 cells , the palpable tumors formed by LMP2A cells and control cells appeared at a similar time and grew at a comparable rate . As the injected cell number was reduced however ( cell numbers at 1×105 , 1×104 or 1×103 ) , the growth rates of the LMP2A tumors were found to be higher than those of controls injected with the same cell numbers . The data in Figure 5B show that when injected with 1×105 , 1×104 or 1×103 LMP2A-expressing NPC cells , 96% of the nude mice ( 27/28 ) developed tumors , whereas only 61% of these mice ( 17/28 ) did so when injected with the control cells . When 1×103 cells were injected , the control cells formed only small tumors in 5/10 mice after 20 days whereas LMP2A-expressing cells formed tumors in 10/10 mice . In addition , the first palpable tumor in the LMP2A groups injected with 1×103 cells appeared within 13 days , six days earlier than the control . Mice were sacrificed at 14 , 17 or 20 days after injection , and the tumors were then weighed and photographed ( Figure S5A and B ) . In all cases , the sizes of the tumors formed by the LMP2A-expressing NPC cells were larger than the vector control cells except in the 1×106 cell inoculation groups . This difference was most apparent in the 1×103 cell group ( P = 0 . 004 ) . Hence , LMP2A increases the number of tumor initiating cells in NPC . To determine whether any correlation existed between LMP2A expression and the representative markers of EMT and stem cell in NPC biopsy samples , we obtained RNA from 15 inflammatory samples and 18 NPC samples and analyzed LMP2A , ABCG2 , Bmi-1 , E-cadherin ( E-cad ) and Fibronectin ( FN1 ) expression using real-time RT-PCR . LMP2A , Bmi-1 and ABCG2 transcripts were found to be low or undetectable in the 15 inflammatory samples but extremely high in the NPC tumor tissue ( Figure 6A ) . We also found that LMP2A expression positively correlates with ABCG2 , Bmi-1 and Fibronectin , and negatively correlates with E-cadherin ( Figure 6B ) . In addition , we also detected LMP2A , Bmi-1 , E-cadherin proteins in another 42 NPC biopsies . As shown in Figure 6C and Figure 6D , LMP2A correlated positively with Bmi-1 , and negatively with E-cadherin . As previously shown , the expression of LMP2A in B lymphocytes and HaCaT cells induces the activation of Akt in a PI3K-dependent manner [26] , [28] . To investigate the Akt status in NPC cells in our current study , western blot analysis using an antibody that detects Thr308 phosphorylation of Akt was performed to detect activated Akt ( Figure 7A ) . Phospho-Akt ( Thr308 ) was found to be up-regulated at least 2 . 5 folds in LMP2A-expressing cells compared with control cells as analyzed by Quantity One . Phospho-GSK3β , a direct target of Akt GSK3β [37] , was further found to be induced in LMP2A cells ( Figure 7A ) . After treatment with Akt inhibitor ( V ) at 4 µM for 12hours , the phosphorylation of Akt was suppressed in both the LMP2A and vector control NPC cells ( Figure 7B ) . It is noteworthy , however , that the SPs were dramatically reduced in NPC cell lines in the presence of Akt inhibitor ( V ) , particularly in LMP2A-expressing cells . As shown in Figure 7C , the size of the SP decreased from 31% to 13 . 3% in CNE2-LMP2A cells and from 7 . 1% to 2 . 4% in SUNE1-LMP2A cells . Thus , the Akt pathway seems to play a role in the LMP2A-mediated increase of NPC SP cells , although this will need to be further confirmed using dominant negative mutants or shRNAs that target Akt in LMP2A-expressing cells . Most importantly , these results were further confirmed by transient expression of LMP2A . Figure S6A , phospho-Akt ( Thr308 ) and phospho-GSK3β were upregulated in LMP2A positive cells consistent with the above results . And after treatment with Akt inhibitor ( V ) , we observed the similar results ( Figure S6B ) . Moreover , as shown in Figure S6C , the size of the SP decreased from 13% to 7 . 06% in transient CNE2-LMP2A cells .
We show for the first time herein that the EBV latent membrane protein LMP2A can induce EMT and increase the number of tumor initiating cells . Our data first indicated that LMP2A strongly up-regulates the cancer stem cell-like population in NPC , which may explain the onset of metastases and high rate of recurrence for these tumors . This raises the possibility that this viral protein plays a key role not only in EBV latency and persistence but also in the progression of NPC . Based on our novel findings , we believe that the pathologic diagnosis together with detection of LMP2A in tumor tissue will aid in predicting NPC progression , and that LMP2A can be considered to be a novel therapeutic target for this cancer .
All animal work was conducted under the institutional guidelines of Guangdong Province and approved by the Use Committee for Animal Care . Approval from the Sun Yat-sen University Institute Research Ethics Committee was obtained , and written informed consent was provided by each human subject . Two poorly differentiated nasopharyngeal carcinoma cell lines ( CNE2 , SUNE1 ) were maintained in RPMI 1640 medium ( Life Technologies , Carlsbad , CA ) supplemented with 10% fetal bovine serum ( FBS ) in a humidified 5% CO2 incubator at 37°C . To generate stable cell lines , recombinant retroviruses expressing either vector pBabe or pBabe subcloned with LMP2A were generated as previously described [64] and used to infect CNE2 and SUNE1 cells [65] . Pooled CNE2 and SUNE1 cell populations expressing either pBabe or pBabe-LMP2A were selected with 0 . 5 µg/mL of puromycin ( Sigma-Aldrich , St Louis , MO ) . C666 , the only well-known nasopharyngeal carcinoma cell line consistently carrying EBV , was chosen to perform the stable knockdown of LMP2A expression . Retroviral particles were generated and used to infect the target C666 cells as described previously [66] . The successful knockdown of LMP2A was verified by RT-PCR and immunofluorescence . An LMP2A monoclonal antibody was obtained from Proteintech Group Inc . ABCG2 ( Cat . 3380 ) and Nanog ( Cat . 21603 ) antibodies were obtained from Abcam ( Cambridge , UK ) . Antibodies raised against E-cadherin ( Cat . 610181 ) , α-catenin ( Cat . 610193 ) , fibronectin ( Cat . 610077 ) , and vimentin ( Cat . 550513 ) were purchased from BD Biosciences ( Franklin Lakes , NJ ) . Mouse anti-Bmi-1 ( Upstate Biotechnology , Lake Placid , NY ) , and rabbit-anti-GSK-3β , p-GSK-3β , Akt ( Cell Signaling , Beverly , MA ) and p-Akt ( Santa Cruz Biotechnology , CA . ) primary antibodies , and FITC or rhodamine-conjugated goat anti-rabbit IgG or goat anti-mouse IgG ( Jackson Laboratory , West Grove , PA ) or Peroxidase-conjugated goat anti-rabbit IgG or goat anti-mouse IgG ( Amersham Pharmacia Biotech , Piscataway , NJ ) secondary antibodies were used for western blot or immunofluorescence analysis . Freshly frozen biopsied tissues from a total of 18 NPC patients and 15 normal controls , and 81 paraffin-embedded NPC samples which had been histologically and clinically diagnosed were collected from the archives of the Department of Sample Resources , Cancer Center , Sun Yat-sen University ( Guangzhou , China ) . Prior informed consent from the patients and approval from the Institute Research Ethics Committee was obtained . Cells were analyzed by FACS when the cells had reached a logarithmic growth phase ( 24 hours after replating ) . Cells were digested with 0 . 25% trypsin ( Sigma-Aldrich , St . Louis , MO ) , washed twice with calcium/magnesium-free PBS , resuspended in ice-cold RPMI 1640 culture ( supplemented with 2% FBS ) at a concentration of 1×106 cells/mL , and incubated at 37°C in a 5% CO2 incubator for 10 min . The DNA binding dye , Hoechst 33342 ( Sigma-Aldrich , St . Louis , MO ) , was then added at a final concentration of 5 µg/mL and the samples were incubated for 90 min in the dark with periodic mixing . The cells were then washed twice with PBS , 1 µg/mL propidium iodide ( Sigma-Aldrich ) was added , and the cells were kept at 4°C in dark prior to sorting by a Moflo XDP ( Beckman Coulter , Fullerton , CA ) . Because Hoechst 33342 extrudes from cells treated with verapamil ( a calcium ion tunnel antagonist ) -sensitive ABC transporters , a subset of the cells were incubated with 50 µmol/L verapamil for 30 min at 37°C before the addition of Hoechst 33342 to determine whether this would block the fluorescent efflux of SP cells in the CNE2 and SUNE1 populations . Total RNA extracts from LMP2A-overexpressing cells and pBabe vector control cells were prepared using a Trizol reagent ( Life Technologies , Grand Island , NY ) according to the manufacturer's instructions . The RNA was then treated with DNase , and 2 . 5 µg aliquots were used for cDNA synthesis using random hexamers . The primers used for the amplification of the indicated genes are listed in Table S1 . The expression levels of LMP2A , ABCG2 , BMI-1 , E-cadherin and Fibronectin mRNA was determined by SYBR green real-time reverse transcription-PCR ( RT-PCR ) . Total RNA from different human nasopharyngeal tissues were extracted using Trizol reagent ( Invitrogen , Carlsbad , CA ) . Quantitative dertermination of RNA levels were performed in triplicate in three independent experiments . Real-time PCR and data collection were performed with an ABI PRISM 7900HT sequence detection system . The housekeeping gene GAPDH was used as an internal control to normalize the expression levels of different genes . The primers used for the amplification of the indicated genes are listed in Table S2 . Western blotting analysis was performed as previously described [67] . Where relevant , the blots were probed with antibodies as labeled in the figures , and the signals were detected using enhanced chemiluminescence ( ECL ) ( Amersham Pharmacia Biotech , Piscataway , NJ ) . The membranes were stripped and probed with an anti-alpha tubulin mouse monoclonal antibody ( Santa Cruz Biotechnology , Santa Cruz , CA ) to confirm equal loading of the samples . Immunofluorescence analysis was performed as described previously [67] . Cell lines were plated on culture slides ( Costar , Cambridge , MA ) and after 24 hours were rinsed with phosphate-buffered saline ( PBS ) and fixed in ice-cold methanol-acetone for 5 min at -20°C . The cells were then blocked for 30 min in 10% BSA ( Sigma-Aldrich St . Louis , MO ) in PBS and then incubated with primary monoclonal antibodies in PBS for 2 hours at room temperature . After three washes in PBS , the slides were incubated for 1 h in the dark with secondary goat anti-mouse , or goat anti-rabbit antibodies ( Invitrogen , Carlsbad , CA ) . After three further washes , the slides were stained with 4- , 6-diamidino-2-phenylindole ( DAPI; Sigma-Aldrich St . Louis , MO ) for 5 min to visualize the nuclei , and examined using an Olympus confocal imaging system ( Olympus FV100 ) . Six-well plates were coated with a layer of 0 . 6% agar in medium supplemented with 20% fetal bovine serum . Cells were prepared in 0 . 3% agar and seeded in triplicate . The plates were then incubated at 37°C in a humid atmosphere of 5% CO2 for two weeks until colonies had formed . Each experiment was repeated at least three times . Colonies were photographed between 18–24 days ( final magnification 20 X ) under a phase contrast microscope , and colonies larger than 50 µm in diameter were counted under a light microscope . Cells were counted , plated in triplicate at 200 cells for the pooled population or 100 sorted cells per well in six-well plates , and cultured with RPMI 1640 complete culture for 10 days . After most of the colonies had expanded to more than 50 cells , they were washed twice with PBS , fixed in methanol for 15 min , and dyed with crystal violet for 15 min at room temperature . After washing out the dye , the plates were photographed . To quantify the colonies objectively , the software Quantity One was used and colonies that lager than the averaging parameter of 3 or 1 and the minimum signal intensity of 1 . 0 were counted . At least three independent experiments were carried out for each assay . Nude mice were purchased from the Shanghai Slac Laboratory Animal Co . Ltd and maintained in microisolator cages . All animals were used in accordance with institutional guidelines and the current experiments were approved by the Use Committee for Animal Care . Tumor cells were suspended in 200 µl RPMI 1640 complete culture with 25% Matrigel ( BD Biosciences ) and inoculated subcutaneously into the left flanks of 4- to 5-week-old nude mice . The mice were monitored daily for palpable tumor formation and tumors were measured using a Vernier caliper , and also weighed and photographed . The Entrez Gene ID for genes and proteins mentioned in the text are 3783751 ( LMP2A ) , 2597 ( GAPDH ) , 10376 ( α-Tubulin ) , 999 ( E-cadherin ) , 2335 ( Fibronectin ) , 7431 ( Vimentin ) , 1495 ( α-Catenin ) , 9429 ( ABCG2 ) , 648 ( Bmi-1 ) , 79923 ( Nanog ) , 6657 ( SOX2 ) , NG_012188 ( Akt ) , NG_012922 ( GSK-3β ) . | Epstein-Barr virus ( EBV ) infects about 90% of people worldwide and persists benignly as a latent infection . However , EBV is associated with different types of human cancer . Nasopharyngeal carcinoma ( NPC ) is the most commonly known EBV associated cancer and expresses a well defined set of latent viral genes , including LMP2A , which has been detected in the majority of NPC samples . Several studies indicated this latent viral protein drove cellular invasion and metastasis . For this study , enforced LMP2A expressing NPC cell lines were generated . We show here that LMP2A induces an Epithelial–Mesenchymal Transition and increases the Stem-like Cancer Cells in NPC . Our results suggest that LMP2A supports tumor initiation and recurrence of the infected nasopharyngeal epithelial cells . For the first time we report a virus protein that functions in the initiation and progression of cancer by inducing the cancer stem-like cells . These findings permit a more detailed understanding of function and contribution to viral pathogenesis and provide a novel therapeutic target for NPC therapy . | [
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] | 2010 | Epstein-Barr Virus-Encoded LMP2A Induces an Epithelial–Mesenchymal Transition and Increases the Number of Side Population Stem-like Cancer Cells in Nasopharyngeal Carcinoma |
Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell’s fate , such as disease therapeutics and stem cell reprogramming . Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets . Formulated in a logical dynamic scheme , our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state . We illustrate our method’s potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells . We find that the predicted control targets are effective in a broad dynamic framework . Moreover , several of the predicted interventions are supported by experiments .
An important task of modern molecular and systems biology is to achieve an understanding of the dynamics of the network of macromolecular interactions that underlies the functioning of cells . Practical applications such as stem cell reprogramming [1–3] and the search for new therapeutic targets for diseases [4–6] have also motivated a great interest in the general task of cell fate reprogramming , i . e . , controlling the internal state of a cell so that it is driven from an initial state to a final target state ( see references [7–13] ) . Theoretically derived control methods are based on simplified models of the interactions and/or the dynamics of cellular constituents such as proteins or mRNAs . Some of these models only include information on which cell components ( e . g . molecules or proteins ) interact among each other , i . e . , the structure of the underlying interaction network . Other models , known as dynamic models , include the structure of the interaction network and also an equation for each component , which describes how the state of this component changes in time due to the influence of other cell components ( e . g . how the concentration of a molecule changes in time due to the reactions the molecule participates in ) . Although the topic of network controllability has a long history in control and systems theory ( see , for example , [14–17] ) , most of this work is not directly applicable to large intracellular networks . There are several reasons for this: ( i ) combinatorial complexity and the size of the matrices involved makes control theory applicable to small networks only , ( ii ) linear functions are used for the regulatory functions and it is unclear how the switch-like behavior of many biochemical processes [18 , 19] will affect these results , and ( iii ) the notion of controllability in control theory , i . e . control of the full set of states [14–16] or complete controllability , is different from that in the biological sense , which commonly encompasses only the biologically admissible states[8] . In recent work on network controllability [7 , 9–13 , 20–22] some of the limitations of standard control theory approaches are addressed . For example , Akutusu , Cheng , Tamura et al . [20–22] extend the framework of control theory to systems with Boolean ( switch-like ) dynamics and provide some formal results in this setting . In the work of Liu et al . [7] the size limitation of linear control theory is overcome by using a maximal matching approach to identify the minimal number of nodes needed to control a variety of real-world large scale networks . Specifically , for some gene regulatory networks , Liu et al . find that control of roughly 80% of the nodes is needed to fully control the dynamics of these networks [7] . In contrast , experimental work in stem cell reprogramming suggests that for biologically admissible states the number of nodes required for control is drastically lower ( five or fewer genes [1–3 , 8] ) . Fiedler , Mochizuki et al . [12 , 13] use the concept of the feedback vertex set , a subset of nodes in a directed network whose removal leaves the graph without directed cycles ( i . e . without feedback loops ) . They show that , for a broad class of regulatory functions , controlling any feedback vertex set is enough to guide the dynamics of the system to any target trajectory of the uncontrolled network [12 , 13] . As one of their examples , the authors use a signal transduction network with 113 elements and show that the minimal feedback vertex set is composed of only 5 elements . Since systems whose interaction networks and dynamics are known equally well are rare , current control strategies are based on either the network structure [7 , 9 , 10 , 12 , 13] or its dynamics ( function ) [11 , 20–22] . Yet , as manipulating the activity of even a single intracellular component is a long , difficult , and expensive experimental task , it is crucial to reduce as much as possible the number of nodes that need to be controlled . We hypothesize that integrating network structure with qualitative information on the regulatory functions or on the target states of interest could yield control strategies with a small number of control targets . Qualitative information about the regulatory functions is commonly known ( e . g . positive/negative regulation , cooperativity among regulators , etc . ) , and relative qualitative information on the desired/undesired states also exists ( e . g . upregulation or downregulation of mRNA levels in a disease state with respect to a healthy state ) . Thus , we choose a logical dynamic framework as our modeling method [23] . This framework is well suited for modeling intracellular networks: discrete dynamic models have been shown to reproduce the qualitative dynamics of a multitude of cellular systems while requiring only the combinatorial activating or inhibiting nature of the interactions , and not the kinetic details [24–30] . Logical dynamic network models [31–38] consist of a set of binary variables {σi} , i = 1 , 2 , … , N , each of which denotes the state of a node ( also referred to as node state ) . The state ON ( or 1 ) commonly refers to above a certain threshold level , while the state OFF ( or 0 ) refers to below the same threshold level . The vector formed by the state of all nodes ( σ1 , σ2 , … , σN ) denotes the state of the system ( or system/network state ) . To each node vi one assigns a Boolean function fi which contains the biological information on how node vi’s inputs influence σi; these functions are used to evolve in time the state of each element . We use the general asynchronous updating scheme [33 , 34 , 36] ( see Methods ) , a stochastic scheme which takes into consideration the variety of timescales present in intracellular processes and our incomplete knowledge of the rates of these processes . In a logical ( Boolean ) model , every temporal trajectory must eventually reach a set of system states in which it settles down , known as an attractor . The attractors of intracellular networks have been found to be identifiable with different cell fates , cell behaviors , and stable patterns of cell activity [24–30 , 39 , 40] . In general , the task of finding Boolean network attractors is limited by combinatorial complexity; the size of the state space grows exponentially with the number of nodes N . To address this , we recently proposed an alternative approach to find the attractors of a Boolean network which allowed us to identify the attractors of networks for which a full search of the state space is not feasible [41] . This attractor-finding method is based on identifying certain function-dependent network components , referred to as stable motifs , that must stabilize in a fixed state . A stable motif is defined as a set of nodes and their corresponding states which are such that the nodes form a minimal strongly connected component ( e . g . a feedback loop ) and their states form a partial fixed point of the Boolean model . ( A partial fixed point is a subset of nodes and a respective state for each of these nodes such that updating any node in the subset leaves its state unchanged , regardless of the state of the nodes outside the subset . ) It is noteworthy that stable motifs are preserved for other updating schemes because of their dynamical property of being partial fixed points . For more details on the attractor-finding method and the identification of the stable motifs see S1 Text and ref . [41]; for a more formal and mathematical discussion see S2 Text section A or Appendix A of ref . [41] . Once a network’s stable motifs and their corresponding fixed states are identified , a network reduction technique [36 , 42–44] is used for each stable motif by tracing the downstream effect of the stable motif on the rest of the network ( see S1 Text ) . Repeating this procedure iteratively for each separate stable motif until no new stable motifs are found yields the attractors of the logical model . Formally , the result is a set of network states called quasi-attractors , which capture steady states exactly and are a compressed representation of complex attractors [41] . The network control method we propose here builds on the concept of stable motifs and its relation to ( quasi- ) attractors [41] and takes it much further by connecting stable motifs with a way to identify targets whose manipulation ( upregulation or downregulation ) ensures the convergence of the system to an attractor of interest . The use of quasi-attractors in our method does not compromise its general applicability , but it does require that certain networks with special types of complex attractors are treated with care when our method is applied . None of the networks we discuss in this work nor any intracellular network models we are aware of fall in this category; for more details see S1 Text , S2 Text , and ref [41] . ) As an illustration , consider the logical network shown in Fig 1 ( a ) . This logical network has four stable motifs ( Fig 1 ( b ) ) : ( i ) {A = 1 , B = 1} , ( ii ) {A = 0} , ( iii ) {E = 1} , and ( iv ) {C = 1 , D = 1 , E = 0} . Network reduction for each of these stable motif yields four reduced networks , each of which has its own stable motifs , all of which are shown in S1 Fig . For example , the reduced logical network obtained from the first stable motif consists of two nodes ( D and E ) and has two stable motifs: {E = 1} and {E = 0} . The stable motifs of the remaining three reduced logical networks are , respectively: {E = 1} and {D = 1}; {A = 1 , B = 1} and {A = 0}; {A = 1} and {A = 0} . Repeating the same network reduction procedure with each of the new stable motifs leads to either a new reduced network or one of four attractors ( 𝒜i , i = 1 , … , 4 ) . The stable motifs obtained from the original network and from each reduced network , and the attractors they lead to are shown in Fig 2 . This diagram is a compressed representation of the successive steps of the attractor finding process , which include the original network , the stable motifs of the original network , the reduced networks obtained for each stable motif , the stable motifs of these reduced networks , and so on ( see S1 Fig ) . We refer to such a diagram as a stable motif succession diagram , and we note that it is closely analogous to a cell fate decision diagram . We propose to use this stable motif succession diagram to guide the system to an attractor of interest .
The stable motifs’ states are partial fixed points of the logical model , and as such , they act as “points of no return” in the dynamics . Normally , the sequence of stable motifs is chosen autonomously by the system based on the initial conditions and timing . We propose to use our knowledge of the sequence of stable motifs to guide the system to an attractor of interest . We refer to this network control method as stable motif control . The basis of the stable motif control approach is that a sequence of motifs from a stable motif succession diagram like Fig 2 uniquely determines an attractor , so controlling each motif in the sequence must prod the system towards this attractor . We give the proof of this statement in Lemma 4 and Proposition 6 of S2 Text section B . The number of nodes that need to be controlled can be minimized by removing motifs that do not need to be controlled and by finding a subset of nodes in a motif which can fix the whole motif’s state . A step by step description of the stable motif control algorithm is given in Methods . For more details on the motif-removal step involved in minimizing the number of control nodes , see S1 Text; for a justification of the steps involved in minimizing the number of control nodes , see S2 Text . S3 Text presents a discussion of the complexity of our methods and mitigation techniques for the most time consuming parts of our methods . As an example , consider the network in Fig 1 ( a ) and choose 𝒜2 in Fig 2 as our target attractor . There are two sequences of stable motifs that lead to 𝒜2: ( {C = 1 , D = 1 , E = 0} , {A = 1} ) and ( {A = 1 , B = 1} , {E = 0} ) . For motif {C = 1 , D = 1 , E = 0} in the first sequence , fixing E = 0 is enough to fix the whole motif’s state; for motif {A = 1} in the same sequence there is only one node , so the only choice is to fix A = 1 . The control set obtained from the first sequence is then {E = 0 , A = 1} . For the second sequence , a similar reasoning leads to the same control set , {E = 0 , A = 1} ( E = 0 from {E = 0} , and A = 1 from {A = 1 , B = 1} ) . The result is a single set of network control interventions for attractor 𝒜2 , C𝒜2 = {{A = 1 , E = 0}} . For a step by step description of the stable motif control algorithm applied to this example see S1 Text . Using our approach with each of the remaining attractors we obtain the following network control interventions: C𝒜1 = {{A = 1 , E = 1}} , C𝒜2 = {{A = 1 , E = 0}} , C𝒜3 = {{A = 0 , E = 1}} , and C𝒜4 = {{A = 0 , E = 0}} . Inspecting these network control interventions we conclude that controlling nodes A and E is enough to guide the system to each of the four possible attractors , with the exact combination being given by the C𝒜i’s . In order to gauge the potential improvement in the control set’s size brought about by our method , we compare our network control set with the feedback vertex set , the subset of nodes whose removal leaves the network without directed cycles . This set was demonstrated to be an effective control target and set an upper limit in the size of the control set in references [12 , 13] . Because removing the feedback vertex set from the network must destroy all cycles , including self-loops , there are two possible minimal feedback vertex sets , {A , B , D , E} and {A , C , D , E} . The number of nodes that need to be controlled in our method is half of the size of the feedback vertex set , a substantial improvement . It should be noted that our method does not guarantee that the resulting control sets are small nor that the control sets are the smallest possible , though our case studies suggest that the resulting control sets tend to be relatively small ( between one and five nodes out of more than fifty , see Tables 1 and 2 , and ref [45] ) . In many situations the main interest is to prevent the system from reaching an unwanted state ( e . g . the proliferative cell state encountered in tumors ) . Based on the motif-sequence point of view provided by the stable motif succession diagram ( Fig 2 ) , we hypothesize that blocking the stable motifs that lead to an attractor will either prevent or make it less likely for the system to reach this attractor . We refer to this network control method as stable motif blocking . The algorithm for the method is given in Methods . The interventions obtained from this method are negations of node states of the target attractor , and as such , have the property of eliminating the intended attractor . However , new attractors can arise that are similar to the destroyed attractor . In biological situations ( like in our test cases ) one commonly has certain molecular markers of cell fate which specify the attractor to a large degree but not at the level of every node . Thus the final state obtained after stable motif blocking may still be consistent with the biological specification of the undesired attractor , making the intervention unsuccessful . We also adopt a stricter definition for a successful intervention: if a long-term but not permanent intervention ( i . e . a transient intervention ) reduces the number of network states or trajectories that lead to the unwanted attractor , then the intervention is considered to be long-term successful . The best-case scenario would be that the manipulated network has only the desired attractors of the original network ( i . e . , any but the unwanted attractors ) , in which case the network will stay in these attractors even if the intervention is stopped . Consider , for example , the network in Fig 1 ( a ) and the attractor 𝒜3 in Fig 2 . From the stable motif succession diagram ( Fig 2 ) , the stable motifs involved in the sequences that lead to 𝒜3 are {A = 0} , {D = 1} , and {E = 1} . Our approach proposes blocking these motifs to obstruct the system from reaching 𝒜3 , that is , it provides ℬ𝒜3 = {{A = 1} , {E = 0} , {D = 0}} or a combination of these node states as intervention candidates . To verify the effectiveness of the interventions , we analyze the dynamics of the manipulated network with each individual intervention . The first intervention ( A = 1 ) causes the system to have 𝒜1 and 𝒜2 as its only attractors , and thus , the network is driven towards these attractors and away from the unwanted attractor 𝒜3 . Furthermore , the network stays in those attractors even after the intervention is stopped , as they are also attractors of the original network , so the intervention is long-term successful . Similarly , the second intervention ( E = 0 ) causes the system to have 𝒜2 and 𝒜4 as its sole attractors , so it is also a long-term successful intervention . The third intervention ( D = 0 ) only leaves attractor 𝒜1 intact , and also gives rise to two new attractors . To evaluate if this intervention is long-term successful we compare the probabilities that an arbitrary initial condition ends in 𝒜3 with and without the intervention . For the intervened case , we set D = 0 for a long time , then stop the intervention and wait for the network to reach an attractor . We find that the intervention makes it more likely for an arbitrary initial condition to reach 𝒜3 , so this intervention is not long-term successful . The network control framework we propose is applicable to any cell fate reprogramming process for which a logical dynamical model can be constructed . This is a broad and increasing domain of application: refs . [24–28] are examples of recent logical models that had experimentally validated predictions , while other examples can be found in the review articles [29 , 30] . To demonstrate the potential of our framework , we choose two types of cell fate reprogramming processes: disease therapeutics and cell differentiation . More specifically , we use our network control framework to predict network control interventions on previously developed logical dynamic models for a leukemia signaling network and for the network controlling the differentiation of helper T cells . We confirm the effectiveness of the predicted stable motif control interventions using dynamic simulations , an independent verification of the result we prove in S2 Text . For the case of stable motif blocking interventions , whose effectiveness is not guaranteed , we use dynamic simulations to test the effectiveness of the predicted interventions . The network control approach we propose is formulated in a Boolean framework , which brings up the question of whether the control targets identified are dependent on the logical modeling scheme . To address this , we translate the studied Boolean network models into ordinary differential equation ( ODE ) models using the method described by Wittmann et al . [49] . In the ODE models the node state variables σ ˜ i can take values in the range [0 , 1]; the differential equations of the translated model have the form σ˜ . i= ( 1/τi ) [f˜i ( σ˜i1 , … , σ˜iki ) −σ˜i] , where f ˜ i is a smooth Hill-type function parameterized by Hill coefficients and threshold parameters , and τi is a time-scale parameter . The function f ˜ i is such that it matches the Boolean function fi whenever its inputs σ ˜ i 1 , … , σ ˜ i k i are either 0 or 1 . Thus , the fixed point attractors of the Boolean model are preserved in the ODE model . We test the effectiveness of the stable motif control interventions in the translated ODE models by comparing the probability for an uniformly chosen initial condition to reach the target attractor with and without the intervention ( see S6 Text ) . We find that the stable motif control interventions are still 100% effective or very close for both permanent and transient interventions ( S3 Table and S4 Table ) . We also find that the effectiveness of the interventions is mostly unchanged by varying the Hill coefficients ( S5 Table ) , varying the the time-scale parameters τi and thresholds ( S6 Table ) , or fixing the intervened node variables close to but not exactly at the intervention-prescribed values ( S7 Table ) . We finally test single interventions and find that they still underperform combinatorial interventions ( S3 Table and S4 Table ) . To further validate the successful control targets we identified , we searched the literature for experimental support for these targets . We find that several of the single interventions predicted to be successful in inducing apoptosis of leukemic T cells or in inducing specific T cell types were found to be successful experimentally . The control targets for which experimental support was found , the attractors they lead to , and the references are shown in Table 3 . Collectively , these results strongly suggest that the control targets identified by our approach transcend the logical framework .
Identifying control targets for intracellular networks is of crucial importance for practical applications such as disease treatment and stem cell reprogramming . Despite recent advances in network controllability approaches , most of them rely solely on the topology [7 , 9 , 10 , 12 , 13] or the dynamics [11 , 20–22] of the network . Thus , potentially important effects that depend on the interplay between structure ( topology ) and function ( dynamics ) , such as combinatorial interactions , are not considered . In this work we proposed a network control approach that combines the structural and functional information of a discrete ( logical ) dynamic network model to identify control targets . The method builds on the concept of stable motif and its relation to finding attractors [41] , and takes it much further by connecting stable motifs with a way to identify targets whose manipulation ( upregulation or downregulation ) ensures the convergence of the system to an attractor of interest . We illustrated our method’s potential to find intervention targets for cancer treatment and cell differentiation by applying it to network models of T-LGL leukemia and helper T cell differentiation . The control interventions identified by our method have many desirable characteristics . For example , stable motif control interventions are guaranteed to drive an initial state to the target attractor state with 100% effectiveness , regardless of the initial state , a general result which we prove in S2 Text and corroborate in our test cases ( see S1 Table and S2 Table ) . They are also long-term successful , meaning that the intervention only needs to be applied transiently for the network to reach and stay in the desired state , a general result which we also verify in our test cases ( see S1 Table and S2 Table ) . We attribute these properties to the use of the natural ( autonomous ) dynamics of the network to control its dynamics . Another noteworthy characteristic of our stable motif control method is the combinatorial nature of the multi-target interventions . As shown in S1 Table and S2 Table , only one single-node intervention ( namely , Ceramide = ON in the T-LGL leukemia network ) was able to match the 100% effectiveness of the multi-target interventions . This agrees with recent clinical studies on the advantages of combinatorial over single target interventions [50–52] . Finally , the stable motif control interventions for our case studies target only a few nodes ( between one and five out of more than fifty ) , which matches what is expected from stem cell reprogramming experiments [1–3 , 8] . The framework presented in this work is formulated and applied in the context of logical network modeling of cell fate reprogramming processes but its applicability is not restricted to it . Indeed , our control approach is applicable to any dynamic process that can be captured qualitatively by a Boolean dynamic network model such as ecological community dynamics [53] , social dynamics [54 , 55] , or disease spreading [56 , 57] . The validity of the control targets on the translated ODE models of our two case studies and the experimental support found for several of these targets demonstrates the broader , potentially model-independent reach of our method . Further work is needed to address exactly how to extend the concept of stable motif and our network control approach to continuous models; formalizing our framework to admit an arbitrary number of discrete states and other updating schemes may prove a valuable step in this direction . Taken together , our results provide a novel framework for the control of the dynamics of intracellular networks that combines realistically obtainable structural and functional information of the network of interest . As such , we expect this framework to be significant to a variety of practical applications and to also provide a new avenue to better understand how the complex behaviors of cells in living organisms emerges from the underlying network of biochemical interactions .
The simulations of the logical model were done with the BooleanDynamicModeling Java library , while the attractor-finding method and the analysis of the stable motif succession diagrams were performed using the StableMotifs Java library , both of which are freely available on GitHub ( on http://github . com/jgtz/BooleanDynamicModeling/ and http://github . com/jgtz/StableMotifs/ , respectively ) . The source code of a Java project that allows the user to reproduce the stable motif succession diagrams and control sets for the test cases analyzed is also freely available on GitHub under the examples folder of the StableMotifs Java library . The generation of the ODE model from the logical model was done using the MATLAB implementation of the method of Wittman et al . [49 , 58]; the numerical integration of the ODE models was performed using MATLAB’s ode45 function ( see S6 Text for more details ) . The networks in all figures were created using the yEd graph editor ( http://www . yworks . com/ ) . In the general asynchronous scheme , the state of the nodes is updated at discrete time steps starting from an initial condition at t = 0 . At every time step , one of the variables is chosen randomly ( uniformly ) and is updated using its respective function and the state of its regulators at the previous time step σ j ( t + 1 ) = f j ( σ j 1 ( t ) , σ j 2 ( t ) , ⋯ , σ j k j ( t ) ) , ( 1 ) while the rest of the variables retain their state . In this way , every possible update order is allowed , and thus , all relative timescales of the processes involved are sampled . For an attractor of interest 𝒜 , the steps of the stable motif network control method are the following: - Step 1: Identify the sequences of stable motifs that lead to 𝒜 . These can be obtained from the stable motif succession diagram ( see Fig 2 ) by choosing the attractor of interest in the right-most part and selecting all of the attractor’s predecessors in the succession diagram . - Step 2: Shorten each sequence 𝒮 by identifying the minimum number of motifs in 𝒮 required for reaching 𝒜 and removing the remaining motifs from the sequence . This minimum number of motifs can be identified from the stable motif succession diagram ( Fig 2 ) ; they are the motifs after which all consequent motif choices lead to the same attractor 𝒜 . - Step 3: For each stable motif’s state ℳ = ( σm1 , σm2 , … , σml ) , find the subsets of stable motif’s states O = {Mi} , Mi ⊆ ℳ that , when fixed in the logical model , are enough to force the state of every node in the motif into ℳ . At worst , there will only be one subset , which will equal the whole stable motif’s state ℳ . If any of these subsets is fully contained in another subset , remove the larger of the subsets . In each stable motif sequence 𝒮 = ( ℳ1 , … , ℳL ) , substitute every stable motif ℳj with the subsets of the stable motif’s states obtained , that is , 𝒮 = ( O1 , … , OL ) . - Step 4: For each sequence 𝒮 = ( O1 , … , OL ) create a set of states 𝒞 by choosing one of the subsets of stable motif’s states Mkj in each Oj and taking their union , that is , 𝒞 = Mk1∪⋯∪MkL , Mkj ∈ Oj . The network control set for attractor 𝒜 is the set of node states C𝒜 = {𝒞i} obtained from all possible combinations of subsets of stable motif’s states Mkj’s for every sequence 𝒮 . To avoid any redundancy , we additionally prune C𝒜 of duplicates and remove each set of node states 𝒞i which is a superset of any of the other sets of node states 𝒞j ( i . e . 𝒞j ⊂ 𝒞i ) . For a pseudocode of each step of the stable motif control algorithm see S7 Text . Given an attractor 𝒜 one is interested in obstructing , the steps to identify potential interventions are the following: - Step 1: Identify the sequences of stable motifs that lead to 𝒜 . This step is the same as the first step in the stable motif control algorithm , and can be obtained from the stable motif succession diagram ( Fig 2 ) . - Step 2: Take each stable motif’s state ℳi in the sequences obtained in the previous step . Create a new set M𝒜 with all of these stable motif states , M𝒜 = {ℳi} . - Step 3: Take each node state σj ⊂ ℳi of the stable motif’s states ℳi in M𝒜 . Create a new set ℬ𝒜 with the negation of each node state , ℬ 𝒜 = { σ ¯ j } . The node states in ℬ𝒜 and any combination of them are identified as potential interventions to block attractor 𝒜 . For a pseudocode of each step of the stable motif blocking algorithm see S7 Text . To validate an intervention target , we fix the node states prescribed by the intervention , choose a random ( uniformly chosen ) initial condition , and evolve the system using the general asynchronous updating scheme for a sufficiently large number of time steps so that the system reaches an attractor . We find that , for our test cases , temporal evolution for 10 , 000 time steps ensures reaching an attractor from any initial condition considered with stable motif control intervention or without an intervention; to be safe , we choose to evolve for 50 , 000 time steps in all cases . We repeat this for a large number of initial conditions ( 100 , 000 ) and calculate the probability of reaching each attractor from an arbitrary ( uniformly chosen ) initial condition . We also look at the probability of reaching each attractor when the intervention is not permanent ( i . e . it is transient ) , that is , we fix the prescribed node states for a large number of time steps , then stop fixing these states and wait for another large number of time steps for the system to reach an attractor . For our test cases , we find that using 10 , 000 time steps for each evolution stage ( with and then without prescribed node states ) is enough to preserve the first three digits of the estimated probabilities pAttr of reaching the attractor of interest , consistent with what is expected from the standard deviation of the estimated probability pAttr . To be safe , we choose to evolve for 50 , 000 time steps for each evolution stage . The number of initial conditions we use is chosen to give three significant figures in the estimated probabilities pAttr . For our test cases , we find that 100 , 000 initial conditions are enough to estimate the probabilities pAttr of reaching the attractor of interest with an error ( standard deviation of the estimated probability pAttr ) of 3⋅10−3[pAttr ( 1−pAttr ) ]1/2 . Equivalently , if pAttr is expressed as a percentage ( which we denote as %pAttr for clarity ) , the error in it is estimated as 3⋅10−3[%pAttr ( 100%−%pAttr ) ]1/2% ( e . g . 0 . 03% for a %pAttr of 1% , and 0 . 15% for a %pAttr of 50% ) . The number of time steps we use is enough to show no changes in pAttr beyond what is expected from the standard deviation of the estimated probability pAttr , and is also found to be enough for the initial conditions to reach the attractors when no interventions are applied . | Practical applications in modern molecular and systems biology such as the search for new therapeutic targets for diseases and stem cell reprogramming have generated a great interest in controlling the internal dynamics of a cell . Here we present a network control approach that integrates the structural and functional information of the network . We show that stabilizing the expression or activity of a few select components can drive the cell towards a desired fate or away from an undesired fate . We demonstrate our method’s effectiveness by applying it to a type of blood cell cancer and to the differentiation of a type of immune cell . Overall , our approach provides new insights into how to control the dynamics of intracellular networks . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | Cell Fate Reprogramming by Control of Intracellular Network Dynamics |
Rat-borne leptospirosis is an emerging zoonotic disease in urban slum settlements for which there are no adequate control measures . The challenge in elucidating risk factors and informing approaches for prevention is the complex and heterogeneous environment within slums , which vary at fine spatial scales and influence transmission of the bacterial agent . We performed a prospective study of 2 , 003 slum residents in the city of Salvador , Brazil during a four-year period ( 2003–2007 ) and used a spatiotemporal modelling approach to delineate the dynamics of leptospiral transmission . Household interviews and Geographical Information System surveys were performed annually to evaluate risk exposures and environmental transmission sources . We completed annual serosurveys to ascertain leptospiral infection based on serological evidence . Among the 1 , 730 ( 86% ) individuals who completed at least one year of follow-up , the infection rate was 35 . 4 ( 95% CI , 30 . 7–40 . 6 ) per 1 , 000 annual follow-up events . Male gender , illiteracy , and age were independently associated with infection risk . Environmental risk factors included rat infestation ( OR 1 . 46 , 95% CI , 1 . 00–2 . 16 ) , contact with mud ( OR 1 . 57 , 95% CI 1 . 17–2 . 17 ) and lower household elevation ( OR 0 . 92 per 10m increase in elevation , 95% CI 0 . 82–1 . 04 ) . The spatial distribution of infection risk was highly heterogeneous and varied across small scales . Fixed effects in the spatiotemporal model accounted for the majority of the spatial variation in risk , but there was a significant residual component that was best explained by the spatial random effect . Although infection risk varied between years , the spatial distribution of risk associated with fixed and random effects did not vary temporally . Specific “hot-spots” consistently had higher transmission risk during study years . The risk for leptospiral infection in urban slums is determined in large part by structural features , both social and environmental . Our findings indicate that topographic factors such as household elevation and inadequate drainage increase risk by promoting contact with mud and suggest that the soil-water interface serves as the environmental reservoir for spillover transmission . The use of a spatiotemporal approach allowed the identification of geographic outliers with unexplained risk patterns . This approach , in addition to guiding targeted community-based interventions and identifying new hypotheses , may have general applicability towards addressing environmentally-transmitted diseases that have emerged in complex urban slum settings .
Leptospirosis is a leading zoonotic cause of morbidity and mortality , and is estimated to cause one million cases and more than 50 , 000 deaths each year , at a cost of over 2 . 90 million DALYs lost per year [1 , 2] . The disease has traditionally been associated with occupational exposures and rural-based subsistence farming settings [3 , 4] . However , it has emerged as an important urban health problem in the developing world due to the rapid and disorganized expansion of urban centers , which in turn has created the ecological conditions for rat-borne transmission [4 , 5] . At present , more than one billion of the world’s inhabitants live in slum settlements . In these settings , large epidemics of leptospirosis have increasingly been reported [6–8] . The infection is caused by a spirochetal bacterium from the genus Leptospira . It produces clinical manifestations that range from asymptomatic or mild febrile illness to severe disease [9 , 10] . Fatality rates for severe disease forms , such as Weil’s disease and pulmonary hemorrhage syndrome , are higher than 10% and 50% , respectively [4 , 11] . Infections in the urban setting are largely due to a single serogroup , L . interrogans serogroup Icterohaemorrhagiae , which is acquired during contact with soil or water contaminated with urine of the rat reservoir from which the pathogen is shed [5 , 6 , 12–14] . Urban epidemics of leptospirosis are associated with heavy rainfall events . They predominantly affect slum inhabitants due to environmental conditions of inadequate sanitation and drainage infrastructure , and heavy infestation by rodent reservoirs [3 , 5 , 15–18] . Previous epidemiological studies of leptospirosis have identified the occurrence of severe cases and infections in association with specific sanitation deficiencies in slums , such as household location in proximity to open sewers and accumulated refuse , in flood-risk areas , and in areas infested by rats , such as Rattus norvegicus , which persistently shed leptospires once infected , and are a particularly important reservoir in the urban setting [12 , 19–23] . Although high-risk urban areas for leptospirosis transmission are generally characterized by low social status and poor sanitation infrastructure , previous studies have shown that they are also highly heterogeneous , with wide spatial variability in social and environmental characteristics that are associated with risk for transmission of Leptospira [12 , 21] . In addition , leptospirosis incidence may vary temporally due to local variability in rainfall , humidity and temperature; however , no studies have evaluated the temporal influence of Leptospira transmission in a prospective community-based setting . Finally , annual transmission intensity may be further influenced by unmeasurable factors such as informal interventions by residents that either reduce rodent density or modify drainage patterns , which in turn , influence the frequency and spatial distribution of flooding events . Spatial variation of leptospirosis risk across the study area may reflect a similar spatial distribution of micro-environmental differences that increase the risk of local environmental contamination with Leptospira , or human contact with potentially contaminated environmental compartments . These potential spatial differences may include environmental features such as vegetation , drainage , soil characteristics and rodent reservoir habitability . Likewise , identification of small-scale areas whose risk for transmission is significantly higher or lower than expected , based on measured characteristics , allows further investigation to develop novel hypotheses about leptospirosis transmission risk and identify targets for further study or intervention . The aim of this study , therefore , was to perform rigorous prospective examination of the risk factors for leptospiral transmission in a high-risk urban slum community in Brazil , accounting for spatial and temporal heterogeneity over and above that attributable to measured risk factors . Herein , we describe findings from four years of prospective study using annual serological and household risk factor surveys . We fit a spatiotemporal multivariable model with both fixed and random effects to identify risk factors and to quantify their effect on Leptospira infection . We also examined the spatiotemporal distribution of the random effects , to assess whether the unexplained variation in the pattern of infection exhibits spatiotemporal structure that would require further investigation . Thus , a spatiotemporal approach provides an improved understanding of the underlying epidemiology and the drivers of leptospirosis transmission . It can also identify potential opportunities for effective intervention and control strategies in this and similar slum communities .
The prospective cohort study was conducted in the community of Pau da Lima ( 13°32’53 . 47” S; 38°43’51 . 10” W ) . This slum ( favela ) settlement ( Fig 1 ) is situated in the periphery of Salvador ( population , 2 , 892 , 625 inhabitants ) , Brazil [24] . The study site , which has been previously described , [21] has conditions of poverty , land use and climate that are similar to other slum settlements in Brazil and tropical regions in the developing world . In 2003 , a study census identified 14 , 122 inhabitants residing in 3 , 689 households within the four-valley site with area 0 . 46 Km2 . The majority ( 85% ) of inhabitants were squatters who did not have legal title to their domiciles . Median household per capita income was US$1 . 30 per day . The mean annual incidence of hospitalized leptospirosis at the site was 57 . 8 cases per 100 , 000 population between 1996 and 2002 [21] . A one-year seroincidence study of 2 , 003 residents identified a Leptospira infection rate of 37 . 8 per 1 , 000 person-years at the study site [21] . A sample of 684 ( 18% of 3 , 689 ) households of all inhabited domiciles was selected using a computer-based random number generator . The sample size of this cohort was informed by seroprevalence surveys [12] and by case-control investigations [19] , which found that the frequency of identified risk exposures for anti-Leptospira antibodies and leptospirosis is between 20–40% among community individuals . Based on these data , the study was powered to detect a risk ratio of at least 2 . 0 for potential risk exposures . All subjects aged five years or more who slept three or more nights per week in the sampled households were eligible for enrolment in the cohort study . Subjects were enrolled between February 2003 and May 2004 , according to written informed consent procedures approved by the Institutional Review Boards of the Oswaldo Cruz Foundation and Brazilian National Commission for Ethics in Research , Brazilian Ministry of Health , Weill Medical College of Cornell University , and Yale University School of Public Health . During cohort enrollment and annually thereafter during the three subsequent seasonal periods of heavy rainfall and leptospirosis epidemics ( May-July ) from 2005 to 2007 , the study team of 10–15 community health workers , nurses and physicians visited households to interview subjects and administer standardized questionnaires . Interviewers were trained on the study tool and interviewing techniques , and pilot questionnaires were administered in the community prior to initiating data collection . Information was obtained on demographic and socioeconomic indicators , health seeking , employment and occupation , exposures to sources of environmental contamination and presence of potential reservoirs in the household and workplace . We collected information on ethnicity by self-reporting , which is used in Brazil as a marker of socioeconomic status [25 , 26] . The head-of-household , defined as the member who earned the highest monthly income , was interviewed to determine sources and amounts of income for the household . The study team evaluated literacy according to the ability to read standardized sentences and interpret their meaning . Informal work was defined as income-generating activities for which the subject did not have legal working documents . Exposures to contaminated environment were evaluated by eliciting the subjects’ responses on contact with mud , floodwater , garbage , or sewage during the seasonal period of heavy rainfall . Subjects were asked to report the highest number of rats sighted within the household property and workplace site in the preceding one-month period . The study team surveyed the area within 10 meters of the household to determine the presence of dogs , cats , chickens and vegetation . In addition , the study team surveyed the study site to map the location of open sewage and rainwater drainage systems , identify sites of open accumulated refuse , and measure the area of these deposits . Geographic Information Systems ( GIS ) were used to obtain tridimensional distance from subject households to the nearest open drainage systems and accumulated refuse , as well as household elevation [12] . The study team collected blood samples from participants during household visits at cohort enrollment and once a year during the seasonal period of low rainfall ( November-February ) , which corresponds to the inter-epidemic period for leptospirosis . The microscopic agglutination test ( MAT ) was performed on sera to determine titers of agglutinating antibodies against pathogenic Leptospira . A panel of five reference strains ( WHO Collaborative Laboratory for Leptospirosis , Royal Tropical Institute , Holland ) and two clinical isolates [21] was used , which included L . interrogans serovars Autumnalis , Canicola and Copenhageni , L . borgspetersenii serovar Ballum , and L . kirschneri serovar Grippotyphosa . During serologic confirmation of leptospirosis cases [5] and infection in studies performed in Salvador [12 , 21]this serovar panel demonstrated equivalent performance , to that of the WHO-recommended panel of 16 reference serovars [11] . Screening was performed with serum dilutions of 1:25 , 1:50 , and 1:100 . When agglutination was observed at a dilution of 1:100 , the sample was titrated to determine highest agglutination titer . The study outcome of leptospiral infection was defined as seroconversion , an MAT titer increase from negative to ≥1:50 , or a four-fold increase in titer between sequential paired samples from cohort subjects . As part of quality control procedures , MAT testing was repeated to confirm all identified infections . Rates and 95% confidence intervals were estimated based on the number of infections that occurred among cohort subjects during each follow-up event , defined as two sequential annual serosurveys . A mixed effects model was constructed to explain the spatiotemporal variation in leptospiral infection . We first investigated the relationship between infection risk and each potential explanatory variable in turn , using generalized estimating equations with an unstructured working correlation matrix to account for temporal correlation due to repeated measurements , but ignoring spatial correlation [27] . We assessed whether continuous explanatory variables could be assumed to have a linear relationship ( on the log-odds scale ) with leptospiral infection by fitting a generalized additive model ( GAM ) [28] and examining the shape of the fitted smooth function . There were no missing values for any of the analyzed variables . Variables were selected for the spatiotemporal mixed model within eight groups of factors related to: demographic and social status; self-reported prior history of hospitalization for leptospirosis; occupational exposures; household environment; household-related behavior and activities; household reservoirs; occupational behavior and activities; and occupational reservoirs . Within each group , we fitted a GAM using as covariates all of the categorical variables , non-linear effects of the continuous variables , and interactions . We then followed a backward elimination selection process until the minimum value of the modified Akaike Information Criterion ( AIC ) was achieved [29] . These eight groups of variables were then merged , and the same backward elimination process was followed until it was no longer possible to reduce AIC by elimination of any of the remaining variables . Non-linear effects of continuous variables were accommodated by fitting simple spline models based on the functional form of the components of the GAM model . Finally , the selected covariates were included as fixed effects into a model that additionally included random effects to account for unexplained spatial and/or temporal variation . The structure of this unexplained variation aids the identification of anomalous areas of high or low risk . Spatiotemporal random effects vary for each location and time but are common to all individuals living in the same household at a given time . We also considered a model with an additional random effect to model unexplained variation in the inherent susceptibility to leptospiral infection between individuals at the same location and time . The models were fitted using the Stochastic Partial Differential Equations ( SPDE ) approach [30] and Integrated Nested Laplace Approximation ( INLA ) method [31] . The models were compared using the deviance information criterion ( DIC ) [32] . We found that the model that included both spatio-temporal and individual-level random effects produced lower DIC values and we therefore used this as the preferred model . Choropleth maps were constructed to represent the spatiotemporal distribution of infection risk , which included the random component of that risk as described by our multivariable mixed effects model for log odds: log ( pij1−pij ) =zijβ+S ( xi , j ) +ui , where for individual i at location xi and time j = 1 , 2 , 3 , 4 , pij represents the probability of infection , zij denotes the vector of covariates , β represents the coefficient factor , S ( xi , j ) are spatio-temporal random effects and ui are uncorrelated individual-level random effects .
The baseline community census identified 14 , 122 inhabitants of the Pau da Lima study site , of whom 12 , 651 ( 90% ) were eligible to participate in the cohort [12] . We randomly selected 684 households ( 18 . 5% ) and approached 2 , 419 eligible residents ( 19% ) for enrollment , among which 2 , 003 ( 83% ) consented to participate [21] . Differences between selected vs . non-selected and recruited vs . non-recruited individuals have been described in a previous publication [21] . Over the entire period , data from at least one complete year of follow-up were available from 1 , 730 ( 86% ) individuals . A total of 1 , 127 ( 56% ) individuals completed the four follow-up protocol ( S1 Table ) . Compared to participants who completed all four events , those who completed between one and three follow up events were more likely to be male ( 47% vs . 42% , p = 0 . 022 ) , and had similar daily per-capita income ( median 0 . 79 dollars per day , interquartile range 0 . 28–1 . 26 , vs . 0 . 79 dollars per day , 0 . 30–1 . 39 , p = 0 . 248 ) . Change of residence to a household outside the study site was the major cause of loss to follow-up , accounting for 67% of the lost-to-follow-up subjects . Overall , among 1 , 730 individuals with at least one complete annual follow-up during the study period , we identified serologic evidence for 199 leptospiral infections among 177 individuals . L . interrogans serogroup Icterohaemorrhagiae was identified as the presumptive infectious serogroup based on agglutination titers in 178 ( 90% ) of the infections . Other infections were identified as Ballum ( 9 , 4% ) , Autumnalis ( 4 , 2% ) , Canicola ( 2 , 1% ) , Grippotyphosa ( 2 , 1% ) , and mixed infections including Icterohaemorrhagiae and at least one other serogroup ( 4 , 2% ) . In 21 of 22 cases where a second or third infection was detected in the same individual , the highest MAT titers were directed against serogroup Icterohaemorrhagiae for both initial and subsequent infections . The overall infection rate was 35 . 4 ( 95% CI , 30 . 7–40 . 6 ) per 1 , 000 annual follow up events for the cohort . The infection rate for the 1 , 127 subjects who completed the four-year follow-up period was 36 . 4 ( 95% CI , 31 . 1–42 . 3 ) infections per 1 , 000 follow-up events . Among the 248 , 195 and 160 subjects who completed only one , two or three follow-up years , respectively , the infection rates were 40 . 3 ( 20 . 7–71 . 5 ) , 38 . 5 ( 22 . 5–61 . 8 ) , and 20 . 8 ( 10 . 7–37 . 0 ) infections per 1 , 000 follow-up events . These were not significantly different from the rate for individuals who completed four-year follow-up ( P value = 0 . 335 ) ( S1 Table ) . Infection rates were higher among males ( 48 . 3 infections per 1 , 000 , 95% CI 40 . 0–57 . 7 , vs . 25 . 9 , 20 . 8–31 . 9 ) , and among age groups with 15–24 ( 39 . 1 , 29 . 8–50 . 4 ) and 25–34 years ( 52 . 0 , 39 . 2–67 . 6 ) ( Fig 2 and S2 Table ) . Crude univariable analysis identified characteristics among residents and the slum microenvironment that significantly modified their risk of infection ( Table 1 ) . In addition to adult age groups and males , individuals with lower social status ( functional illiteracy , and informal employment without contract and benefits ) were more likely to acquire infection during follow-up . In addition , a history of previous hospitalization for leptospirosis was associated with a seven-fold increase in risk for acquiring a leptospiral infection during prospective follow-up ( OR 7 . 0 , 95% CI , 3 . 2–15 . 7 ) . The majority of significant risk factors were associated with the household environment . Subjects who resided in households that were situated in regions of the site with lower elevation ( a proxy for flood risk ) who lived in proximity to open sewers , vegetation and accumulated trash , and who had reported frequent rat sightings in the peridomicilary environment had increased risk for leptospiral infection . We also identified specific individual-level risk exposures in the household setting , such as reported contact with mud or trash , and cleaning a blocked sewer . Although we found occupation-associated exposures such as work related to garbage removal , sewers and construction as risk factors , the frequency of such factors was low among infected individuals ( 2–14% ) and among overall community subjects ( 1–7% ) . After stepwise selection of fixed effects , we constructed a mixed model that included significant variables , together with spatiotemporal and individual-level random effects; the model with only the spatio-temporal random effects has DIC = 1700 . 58 , whilst the model fitted with both spatio-temporal and individual-level random effects has DIC = 1698 . 13 . Compared to the first year of follow-up , years 2 and 3 were associated with lower infection risk and year 4 had a higher risk ( Table 2 ) . Temporal differences in risk did not correlate with levels of pluviometric precipitation or number of extreme or heavy rainfall events that occurred during annual follow-up intervals . We found that male gender , age ( peaking at 20 years of age , S1 Fig ) , and illiteracy were significant risk factors for infection ( Table 2 ) . Environmental factors and exposures related to topography and ground characteristics of the slum microenvironment were important determinants of infection risk . Peridomestic rodent infestation was a significant risk factor ( OR 1 . 46 , 95% CI 1 . 00–2 . 16 ) . Contact with mud in the peridomicilary environment was a significant environmental exposure for infection ( OR 1 . 57 , 95% CI 1 . 13–2 . 17 ) . Additionally there was a nearly significant linear relationship between higher household elevation and lower infection risk ( OR 0 . 92 , 95% CI 0 . 82–1 . 04 for each 10 meter increase in elevation . ) We found that the spatial risk of infection was highly heterogeneous within the slum community . As seen in the choropleth maps in Fig 3 , odds of infection varied significantly across regions of the study area . In general , “hot-spots” of increased infection risk were situated in valley bottoms and in the northern region with lowest elevation of the slum settlement , which were recently invaded by squatters , had more vegetation and had the least access to services such as formal or informal refuse collection ( Fig 1 ) . However , hot spots were frequently juxtaposed to “cold-spots” of infection risk by distances of less than 20–30 meters . Of note , the temporal distribution of regions of high and low infection risk remained relatively stable during the four annual follow-up periods , indicating that the same or similar risk exposures occur year to year in specific microenvironments within the slum community . The fixed effects components of the model accounted for a large amount of the spatiotemporal heterogeneity in the leptospiral infection risk observed at the slum community site . The overall odds of infection in each follow-up period are plotted in the choropleth maps in S2A Fig . S2B Fig plots the fixed effects component of infection odds , indicating the spatial variation in infection risk that is explained by the variables included in the model ( Table 2 ) . Yet we also observed significant unexplained spatial and temporal variation in infection risk , which we accounted for in our modeling strategy through the random effect terms . S2C Fig plots the random effect component of infection odds , i . e . the component of the overall spatial variation in infection risk that could not be explained by the regression component of the model . Similar to the overall odds of infection , the spatial distribution of high and low risk areas associated with fixed effects ( S2B Fig ) and random effects ( S2C Fig ) remained similar year-to-year during prospective follow-up .
We present the results of a large prospective community-based study to determine rates and risks for Leptospira infection in an urban slum where leptospirosis is endemic . We followed a cohort of residents for four years and developed a spatiotemporal mixed model to identify risk factors and capture the distribution of unexplained variation in Leptospira infection . The model combines regression effects of explanatory variables , spatiotemporally correlated random effects , and uncorrelated individual-level random effects . Through these analyses , we found that Leptospira transmission in urban slums occurs due to the interaction of poverty , geography and climate . Our analysis of long-term prospective data confirms previous studies that identified socioeconomic and environmental risk factors but were limited by their retrospective design or limited follow-up [12 , 20–22 , 33] . Importantly , we found that among other mechanisms for environmental exposure , at-risk slum residents become infected with Leptospira through behaviors that lead to contact with contaminated soil and mud , mobilized most often by floodwater during heavy rain events . Individuals who live within an environment that is generally associated with elevated infection risk have specific characteristics and behaviors that elevate their risk of infection . In addition , we demonstrate that there exists a spatially stable , temporally varying layer of infection risk that is intrinsic to the environment on a fine scale , over and above the variation attributable to measured risk-factors . Risk for Leptospira infection in urban slums is therefore determined in large part by structural features , both social and environmental . We found a high incidence of leptospiral infection ( 35 . 4 per 1 , 000 annual follow-up events ) that disproportionately affected specific risk groups within our slum community site , which has an overall high level of absolute poverty . History of prior hospitalization for leptospirosis was associated with a markedly higher risk for serological conversion . We previously observed during one-year follow-up of this cohort [21] that serologic evidence for leptospiral infection occurred in individuals who had a positive baseline agglutinating antibody titer , presumably due to an exposure prior to the study . This investigation found that indeed , there were 22 repeat infections , including one tertiary infection , which occurred after cohort subjects acquired a documented initial infection during follow-up . These findings demonstrate that repeat exposure to the pathogenic bacteria is a frequent event among slum residents . To date , little is known about repeat leptospiral infections and the role of naturally-acquired immunity to reinfection due to the lack of population-based prospective studies . Our long-term cohort study suggests that an initial exposure , whether resulting in clinical disease or asymptomatic infection , confers at best partial immunity to a subsequent re-infection , in a setting where one predominant serovar is circulating . We identified demographic and individual risk factors for infection that were consistent with previous studies in this population [12 , 21] , as well as for leptospirosis in other epidemiologic settings [34 , 35] . Infection risk was highest in young adults and in males . Functional illiteracy , which is a marker of low social status and social exclusion , was also an important risk factor . In univariable analysis , garbage workers and construction workers also had significantly higher risk , possibly reflecting risk exposures related to social status as well as specific occupational exposure to mud and other environments contaminated by rat urine . These findings relating to the social determinants of infection risk indicate that behavior-related [33] differences influence the frequency or intensity of contact with contaminated environmental sources , leading to infection by Leptospira . Within a slum community that is characterized by poor sanitation infrastructure and widespread rodent infestation , we found that distinct differences in the microenvironment lead to substantial spatial variation in infection risk . Lower household elevation was an environmental risk factor for infection in our model . In the study community , elevation correlates , in part , with socioeconomic gradient , with more impoverished squatters settling in regions of low elevation and poor land quality ( Fig 1 ) . Importantly , low elevation is also a proxy for risk of flooding , which typically occurs during periods of heavy rainfall in this and similar communities due to poor rainwater drainage infrastructure , leading to rapid accumulation of rainwater , mudslides , and overflow from open-air sewer canals . Flooding is known to promote leptospirosis transmission and is associated with both seasonal incidence fluctuation and outbreaks in the setting of extreme weather events [5 , 7 , 16 , 17 , 35 , 38–40] . Yet , we found a complex relationship between flooding and infection risk that sheds light on the environmental dynamic of Leptospira and the conditions that lead to human infection . As previously observed in this community [12 , 21] , participants who report contact with mud had a significantly higher risk of infection . However , reported contact with floodwater was not found to be an independent risk factor . At low elevations , the majority of ground surfaces are unpaved dirt , leading to mobilization of mud , and risk of landslides during heavy rain . In contrast , participants who reported contact with floodwater without mud were more likely to live at higher elevation , where flooding reflects poor rainwater drainage of asphalted surfaces . Our analyses reveal the important role of the peridomiciliary environment in facilitating infectious contact with environmentally transmitted pathogens such as Leptospira . While survival of L . interrogans in soil and water of urban slums is unknown , studies in other settings have proved that this organism can survive from days to months in the environment [41] . In this case , soil may serve as an important environmental reservoir for pathogenic Leptospira in urban environments with heavy rat infestation and poor infrastructure . Heavy rainfall may also increase disease transmission by mobilizing the pathogen from this reservoir and promoting contact of slum residents with contaminated environmental sources at the soil-water interface . There was substantial temporal variation in infection risk . During the fourth follow-up period , the risk of infection was 3 . 1 times higher than in earlier years ( 95% CI for RR , 2 . 4–4 . 1 ) . We found similar risk factors for infection during this year compared to the preceding three years , and a similar spatial distribution ( Fig 3 ) . While this study was not structured to understand the specific determinants of temporal variation in incidence , there were no unusual patterns of rainfall , humidity , or temperature in follow-up year 4 compared to preceding years . This high incidence in asymptomatic transmission in year 4 may have occurred due to stochastic differences in rodent density within the community , changes in flooding patterns related to upstream changes in rainwater drainage infrastructure , or other unmeasured factors leading to more frequent exposure . Temporal fluctuation in asymptomatic Leptospira infection has not previously been studied . There were no detected changes in the incidence of severe leptospirosis during year 4 , but it is not known whether there was a concomitant fluctuation in nonspecific febrile illness . However , these findings provide support for the importance of including adjustment for temporal variability when attempting to understand the risk factors of infection in complex ecological systems such as urban slums , and provide important information to understand the natural history of urban leptospirosis . In contrast to the marked temporal variation we observed , the spatial distribution of risk was relatively constant from year to year . The fixed effects in the model account for much of this spatial variation in risk , but there was also a substantial residual component of risk that was best explained by the spatial random effect . By plotting this unexplained risk , discrete locations within the study area were identified with markedly higher or lower risk than explained by the fixed effects in the model . Based on these analyses , there may be localities within the study area that have specific unique characteristics , not captured by the fixed factors included in our multivariable regression , which influence risk of infection . These areas should be further investigated for possible explanatory characteristics that may lead to identification of novel hypotheses about disease transmission mechanisms in this community , and possible opportunities for targeted , informed intervention to decrease the burden of this infection . There are limitations to this study . Among individuals who were lost to follow-up , incidence was inversely correlated with the number of years of completed follow-up , suggesting a potential source of bias . The estimate in our random effects model is the incidence that would apply in the absence of loss to follow-up . Compared to those with complete follow-up , people who participated in the study but were lost to complete follow-up had similar income but were more likely to be male , an important risk category for infection , thus the true overall incidence in this community may be higher than our results indicate . This study was designed to understand the individual and environmental risk factors for disease transmission while adjusting for temporal variability in infection risk , but did not aim to identify specific explanatory variables that explain annual incidence fluctuation . We therefore did not include any temporally varying data in the model , such as precipitation , temperature , humidity , or ultraviolet light intensity , which might influence the frequency of flooding events or the environmental survival of Leptospira . However , based on our model , the spatial pattern of risk was relatively constant over time . This suggests that our model adequately adjusted for temporal variation by including follow-up event as an independent fixed effect without interaction with other variables . Future studies should include time-varying data in order to provide improved explanatory information about annual fluctuations in infection rates . Although leptospirosis incidence in Brazil and other settings fluctuates seasonally in close association with rainfall patterns [5 , 17 , 38] , nothing is known about the effects of seasonal weather differences on the temporal dynamics of asymptomatic infection . Studies that aim to understand the dynamic of Leptospira transmission during rainy and dry seasons would be valuable to understand the impact of environmental factors on the development of severe disease . Furthermore , the population dynamics of the rat population and shedding of the leptospiral pathogen from the reservoir may influence the spatio-temporal distribution of risk in the urban slum setting . Recent studies suggest that the demography , movement patterns and Leptospira shedding from R . norvegicus , the main reservoir in the study area , may influence the spatial dynamics of human infection risk but perhaps not the temporality [14 , 23 , 36 , 37] . However future work needs to be performed that combine ecological and epidemiological studies to establish the spatiotemporal link between reservoir transmission and risk of spill-over infection to humans . Despite these limitations , we performed rigorous adjustment for spatial and temporal variation in disease transmission risk , and therefore are able to draw important conclusions about the drivers of leptospirosis infection in high-risk urban slum environments . We demonstrate that there exist specific risk groups of individuals , including young adults , males , and individuals with marginalized social status and high-risk occupational conditions , who may have activities that place them in more frequent or more intense contact with contaminated environmental sources of transmission . In addition , our analyses suggest that mobilization of soil and mud contaminated by infected rodent urine may be an important process leading to human exposure to pathogenic Leptospira . This draws attention to the potentially important role of exposed soil as an environmental reservoir for pathogenic Leptospira . Eco-epidemiological studies are therefore needed to provide an integrated understanding of the dynamic of Leptospira transmission between the rodent reservoir , soil and other environmental compartments , and human hosts . Finally , our findings suggest that structural interventions may be capable of reducing the burden of leptospirosis in this and other vulnerable communities by protecting residents from contact with contaminated soil and mud during heavy rain events . | Leptospirosis is a rat-borne infectious disease that occurs worldwide , predominantly among vulnerable populations , such as urban slum communities with poor sanitation infrastructure . However , urban slums are complex local settings , where transmission of the disease varies over space and time , and the factors that influence this risk difference are unknown . An improved understanding of the environmental and social factors that modify the risk of this infection is needed in order to guide interventions to reduce the disease burden . We recruited a cohort of 2003 community residents of a high- risk urban slum in Salvador , Brazil . We followed them for a four-year period to understand yearly variation in individual and spatial risk factors for infection using spatiotemporal statistical modeling techniques . Our findings suggest that environmental factors related to topology such as household elevation and inadequate sewage drainage systems increase the risk of transmission in the slum microenvironment by promoting contact with mud contaminated with the pathogenic leptospiral bacteria , and that individual characteristics such as age and gender increase risk through behaviors that lead to increased exposures to a contaminated environment . Through this technique , we also identified local geographic areas where the risks are not well explained by these factors . This will help generate new hypotheses and identify intervention strategies for targeted prevention of leptospirosis in urban slum populations . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2016 | Spatiotemporal Determinants of Urban Leptospirosis Transmission: Four-Year Prospective Cohort Study of Slum Residents in Brazil |
A critical step in the life cycle of many fungal pathogens is the transition between yeast-like growth and the formation of filamentous structures , a process known as dimorphism . This morphological shift , typically triggered by multiple environmental signals , is tightly controlled by complex genetic pathways to ensure successful pathogenic development . In animal pathogenic fungi , one of the best known regulators of dimorphism is the general transcriptional repressor , Tup1 . However , the role of Tup1 in fungal dimorphism is completely unknown in plant pathogens . Here we show that Tup1 plays a key role in orchestrating the yeast to hypha transition in the maize pathogen Ustilago maydis . Deletion of the tup1 gene causes a drastic reduction in the mating and filamentation capacity of the fungus , in turn leading to a reduced virulence phenotype . In U . maydis , these processes are controlled by the a and b mating-type loci , whose expression depends on the Prf1 transcription factor . Interestingly , Δtup1 strains show a critical reduction in the expression of prf1 and that of Prf1 target genes at both loci . Moreover , we observed that Tup1 appears to regulate Prf1 activity by controlling the expression of the prf1 transcriptional activators , rop1 and hap2 . Additionally , we describe a putative novel prf1 repressor , named Pac2 , which seems to be an important target of Tup1 in the control of dimorphism and virulence . Furthermore , we show that Tup1 is required for full pathogenic development since tup1 deletion mutants are unable to complete the sexual cycle . Our findings establish Tup1 as a key factor coordinating dimorphism in the phytopathogen U . maydis and support a conserved role for Tup1 in the control of hypha-specific genes among animal and plant fungal pathogens .
Dimorphism , the capacity of certain fungi to change their morphology between yeast-like growth and a filamentous state in response to environmental signals , is frequently associated with the virulence of both animal and plant pathogenic fungi [1]–[6] . This morphological conversion is controlled by several conserved signaling pathways , such as the cyclic AMP-protein kinase A pathway and a mitogen-activated protein ( MAP ) kinase cascade [4] , [6]–[10] . Another well known transcriptional regulator controlling dimorphism is the general transcriptional repressor Tup1 , which is conserved from fungi to mammals [11]–[16] . The mechanism of action for Tup1 has been best studied in the yeast Saccharomyces cerevisiae . In this fungus , Tup1p forms a transcriptional co-repressor complex with Ssn6p , a protein that contains tetratricopeptide repeat ( TPR ) motifs known to mediate protein-protein interactions [17]–[20] . Neither Tup1p nor Ssn6p have direct DNA binding activity and their role in transcription depends on their recruitment to promoters by specific DNA binding proteins [18] , [21] . Tup1p repression mechanisms include the interaction with RNA polymerase II holoenzyme components and the alteration of chromatin structure through interaction with histones H3 and H4 and histone deacetylases [22]–[26] . Tup1p controls S . cerevisiae dimorphism in both haploid and diploid strains . Deletions of TUP1 result in reduced haploid invasive growth and reduced diploid pseudohyphal growth , which are considered the filamentous forms of this yeast [11] . Although the role of Tup1 in fungal dimorphism seems conserved , the way it controls this process frequently differs between fungi . The deletion of tup1 from the animal pathogens Candida albicans , Penicillium marneffei and Cryptococcus neoformans give clear examples of this variability . In C . albicans , the homozygous mutant for TUP1 shows a constitutive filamentation phenotype , in contrast to the situation described for S . cerevisiae , and reduced virulence [11] . In P . marneffei , however , tupA is required for the maintenance of its filamentous form , negatively regulating yeast morphogenesis instead of filament formation [12] . In the case of C . neoformans , TUP1 is required for the formation of dikaryotic hyphae due to a mating defect of TUP1 mutant strains , and for virulence [27] , [28] . In addition , the molecular mechanisms and genetic pathways by which Tup1 acts in fungal dimorphism are poorly understood in most species [7] , [12] , [27]–[33] . This role of Tup1 in regulating the dimorphic transition is completely unknown in plant pathogenic fungi , which require different morphogenetic changes to successfully colonize their hosts and cause disease . The only data that might link Tup1 to a role in plant fungal dimorphism are a study into the role of sql1 , a gene functionally homologous to S . cerevisiae SSN6 , in U . maydis . Here overexpression of truncated forms of Sql1 was shown to induce morphological changes in this fungus [34] . The corn smut fungus Ustilago maydis is a well established model for studying dimorphism and virulence in plant pathogens [35]–[38] . Pathogenic development of this fungus initiates with the transition from yeast-like growth to the formation of polar filaments on the plant leaf surface . Control of this process relies on a tetrapolar mating system consisting of the biallelic a and the multiallelic b loci . Only strains differing in the allelic composition at both loci can successfully form and maintain the infectious filamentous form of the fungus [39] . Locus a encodes the pheromone-receptor system that allow cells from different mating types to detect each other , form conjugation tubes , and fuse [40] , [41] . Locus b is then responsible for determining the fate of the resulting dikaryon . This locus encodes a pair of homeodomain transcription factors , bE and bW , that form a compatible heterodimer if proceeding from different alleles , triggering filamentation and pathogenicity [42] , [43] . Upon dikaryon filament formation , the hypha tip differentiates to form a specialized structure for plant penetration , known as the appressorium [44] , [45] . Once inside the plant , mycelium expansion takes place , leading to the formation of plant tumors . In these tumors , fungal nuclei fuse prior to the separation and rounding up of each hyphal section to form diploid spores . In favorable conditions spores germinate in a meiotic process that forms new haploid cells [46] . The highly conserved cAMP and MAP kinase pathways play a central role in the control of several of the morphological changes required during U . maydis pathogenic development [47]–[51] . Both of these pathways are activated following the recognition of pheromones by receptors of opposite mating types during the yeast to infective hyphae transition , resulting in the transcriptional and post-translational activation of the Prf1 transcription factor [47] , [51]–[53] . Once activated , Prf1 promotes the expression of a and b loci genes ( for review see [38] ) ( Figure 1 ) . Thus , U . maydis integrates the inputs that activate both pathways through Prf1 to promote the b-dependent infectious form of the fungus . In the animal pathogen C . albicans , cAMP and MAP kinase pathways induce filamentous growth by promoting the activation of Efg1 and Cph1 transcriptional regulators , respectively , that extend down to hypha-specific target genes [2] , [7] , [54]–[56] . Control of filamentation in this fungus also requires the transcriptional repression of hypha-specific genes via Tup1 , which acts through a third parallel pathway involving Rfg1 and Nrg1 transcriptional regulators [7] , [29]–[33] . In U . maydis , as a plant pathogenic fungus , it is unknown whether or not Tup1 plays a role in dimorphism and virulence . Analyzing the function of Tup1 in this plant pathogen could help better understand how it acts within the genetic pathways controlling these processes in different biological contexts . In this work , we explore the roles of Tup1 during the life cycle of the maize pathogen U . maydis . We demonstrate that tup1 is required for normal mating and filament formation in this fungus and that it controls these processes by transcriptional activation of the Prf1 transcription factor through at least two of its direct regulators . Additionally , we show that tup1 is essential for full pathogenic development , affecting tumor formation and spore production . Our results indicate that Tup1 represents a key factor for the regulation of the pathogenic filamentous and dispersible spore forms of the corn smut fungus U . maydis .
To identify Tup1 homologues in U . maydis we performed a blast search against the MIPS U . maydis database ( MUMDB ) proteome using Tup1p from the S . cerevisiae database ( SGD ) as the query sequence . A U . maydis protein sequence , um03280 , with an e-value of 9 . 5e-81 and 66% similarity to S . cerevisiae Tup1p , was retrieved . This sequence , already annotated in MUMDB as Tup1 , shows homology to Tup1 proteins from other fungi; including the animal pathogens C . albicans ( 67% similarity ) , C . neoformans ( 73% ) and P . marneffei ( 75% ) ( all data in Table S1 ) . A sequence alignment of Tup1 proteins from these organisms revealed a number of conserved domains , based on S . cerevisiae: ( 1 ) the tup_N domain , located in the N-terminal region , which is known to be required for Tup1p/Ssn6p complex formation; ( 2 ) seven WD40 domain repeats in the C-terminal region , that mediate protein-protein interactions and ( 3 ) a poorly conserved central region , which possesses histone binding activity in S . cerevisiae [24] , [57] , [58] ( Figure 2 , Table S2 and Figure S1 ) . To test if Tup1 has a role during the U . maydis life cycle , we generated deletion mutants for tup1 in both mating compatible strains , FB1 and FB2 , replacing the tup1 open reading frame with the carboxin resistance cassette from pMF1-c [35] . Examination of cell growth and morphology did not reveal any statistically significant differences in either of the tup1 mutants ( Figure S2 ) . Since the U . maydis life cycle is intrinsically linked to its host , we assayed the virulence of tup1 deletion strains . For this purpose , we infected seven day old maize seedlings with compatible mixtures of either wild-type or Δtup1 fungi , and scored tumor formation 14 and 21 days post-infection ( dpi ) . We noticed a considerable reduction in the number of Δtup1 infected plants that developed tumors compared to wild-type infections . Moreover , the size of tumors developed by Δtup1 strains were also considerably reduced ( Figure 3A , 3B , and Figure S3 ) . In addition , we observed reduced plant mortality for tup1 mutant infections , with no dead plants observed at 14 dpi and only 11% mortality versus 57% for the wild-type strain 21 dpi . ( Figure 3B and Figure S3 ) . To ascertain whether tup1 mutants are able to complete the sexual cycle we assayed infected plants for the presence of spores 21 dpi . Interestingly , while we found large numbers of spores in wild-type tumors , we could not find spores in tup1 mutant infected plants . Microscopy analysis of the Δtup1 induced tumors revealed that none of the fungal hyphae observed had progressed beyond the rounded cell formation stage that occurs just before spore maturation [46] ( Figure 3C ) . These results indicate that tup1 is required for full pathogenic development of U . maydis and support a conserved role for tup1 in the virulence of animal and plant fungal pathogens . During U . maydis plant infection , multiple morphological changes of the fungus are required ( for review see [38] ) . To ascertain which steps of the infectious process are responsible for the decreased amount and size of tumors generated by tup1 mutants , we first determined the extent to which they were able to successfully undergo mating and develop dikaryon filaments . To test this , we co-spotted compatible combinations of tup1 mutants and wild-type strains on PD-Charcoal plates , where the appearance of “fuzzy” white colonies indicates successful mating and the formation of dikaryon filaments . As shown in Figure 4A , crosses between tup1 mutants were unable to form white fuzzy colonies , indicating a recognition or fusion defect between compatible partners , or a post-fusion filamentation defect . Similarly , crosses between tup1 mutants and compatible wild-type strains also showed fuzzy colony formation defects . Filamentation was partially affected when FB1Δtup1 was crossed with wild-type FB2 , showing an intermediate phenotype between wild-type and Δtup1 crosses . In contrast , the FB1 and FB2Δtup1 cross showed the same loss of fuzzy colony phenotype as the double mutant cross . In order to check whether the differences observed in FB1Δtup1 and FB2Δtup1 strains could lead to different rates of tumor formation , we performed a plant infection assay using FB1 vs FB2Δtup1 and FB1Δtup1 vs FB2 crosses . As shown in Figure S4A the infection rates of these two strains were similar and slightly different to the rates observed for the cross of both wild type strains . In addition , we analyzed white fuzzy colony formation in a SG200 background , which is able to form the infective hypha without the necessity of mating with a compatible partner , because of the presence of an active bE1/bW2 heterodimer and a constitutively expressed mfa2 gene [59] . Significantly , SG200Δtup1 did not generate fuzzy colonies on charcoal plates , suggesting a post-fusion role for tup1 ( Figure 4B ) . In order to quantify the phenotype , we performed a filamentation assay by co-spotting SG200CFP [60] and SG200YFPΔtup1 labeled strains on PD-charcoal plates . After fuzzy colony formation , colony samples were used for the quantification of filaments formed by each strain . As shown in Figure 4C , 80% of the filaments corresponded to the wild-type strain , while only 20% belonged to the mutant . Maize infection experiments with tup1 mutants in the SG200 background revealed similar virulence defects to what we had observed in FB1 and FB2 backgrounds ( Figure S5A and S5B ) . Insertion of a single copy of tup1 under the control of the constitutive otef promoter in the ip locus [34] of SG200Δtup1 , restored its filamentation and pathogenic capacity , indicating successful complementation ( Figure 4B , Figure S5A and S5B ) . Moreover in the case of the FBD11 diploid strain , which also do not need to mate with a compatible partner to cause virulence , the heterozygous mutant FBD11Δtup1/tup1 and the homozygous FBD11Δtup1/Δtup1 were almost completely avirulent in leaf infection experiments ( Figure S4B and S4C ) . Because of the reduced infection capacity of the FBD11 wild-type strain , we also performed flower infections ( where we usually observe bigger tumors ) with these strains to better reflect the differences between them . This experiment revealed big tumors in the wild-type strain , medium tumors in the heterozygous and small tumors in the homozygous mutant strains ( Figure S4D and S4E ) . These results point to a post-fusion filamentation defect as a plausible reason for the impaired pathogenicity of Δtup1 strains . However , it has been reported that mating or filamentation defects on PD-Charcoal plates are not always conserved on the plant leaf surface [61] . To check this , we co-infected 7 day old maize seedlings with the labeled strains , SG200CFP and SG200YFPΔtup1 and quantified filament formation on the leaf surface . As shown in Figure 4C ( on plant columns ) , the filamentation defect seen on charcoal containing media was also apparent on the leaf surface , with only around 5% of the filaments formed corresponding to the mutant strain . Finally , to check whether tup1 could also be implicated in other morphological changes required during the U . maydis infection process , we checked for appressoria formation and the presence of clamp-like cells during mycelium expansion in tup1 mutant strains . We observed that both of these structures were formed in the deletion mutants for tup1 ( Figure 5A and 5B ) , although at lower frequency than the wild type , which is very likely a consequence of the filament formation defect showed by these mutants . The frequency of appressoria formation by SG200YFPΔtup1 was reduced to a similar extent as filament formation ( Figure S5C ) , and mycelium expansion was reduced in Δtup1 infected plants at 2 dpi ( Figure 5C ) . These results , together with the capacity , albeit reduced , of tup1 mutants to induce tumors in maize , suggest that those tup1 mutant cells that overcome the filamentation defect are then able to undergo the morphological changes required for plant penetration and expansion . Thus , the role of tup1 in the morphological changes that occur during U . maydis infection seems to be specific to the yeast-to-hypha transition . As tup1 mutants are unable to form dikaryotic hyphae at wild-type levels , we wondered whether tup1 regulates genes downstream of the b locus , thus compromising the fungal dimorphic transition in tup1 mutants . To this end , we used the AB33 strain in which expression of a compatible bE1/bW2 heterodimer is under the control of the nar inducible promoter [62] . When this strain is grown in inducing conditions it forms a b-dependent filament . We found that deletion of tup1 in this background did not affect its filamentation capacity ( Figure 6A; see Figure S6 for quantification ) . This result suggests that Tup1 is affecting processes upstream of the b locus or , alternatively , is acting on a parallel pathway regulating filamentation . To discern between these two possibilities , we extracted total RNA from SG200 and SG200Δtup1 fungi grown on charcoal-containing media for 48 hours and quantified the expression of bE and bW by Northern blot . We observed a strong decrease in both gene transcripts in SG200Δtup1 indicating that tup1 is required for the normal expression of b loci genes ( Figure 6B lanes 5 and 6 ) . To test if constitutive b expression could rescue the filamentation and virulence phenotypes of tup1 mutants , we took advantage of the HA103 strain , which harbors a compatible bE1/bW2 heterodimer under the control of constitutive promoters [52] . Deletion of tup1 in HA103 did not produce the filamentation and virulence defects described for the SG200 background ( Figure 6C , 6D and Figure S7 ) , indicating that constitutive b expression partially rescues these phenotypes . To better understand the effect of b expression on the tup1 mutant virulence phenotype , we used the HA103 parental strain , CL13 [59] , which carries compatible bE1 and bW2 genes under the control of their own promoter and lacks the constitutively-expressed mfa2 gene present in SG200 . Deletion of tup1 from CL13 led to a 90% reduction in maize tumor formation ( Figure 6D and Figure S7 ) , revealing an even clearer b-genes dependent rescue of tup1 mutant phenotypes . Interestingly , the expression level of the b genes correlated with the phenotype of the wild-type and Δtup1 strains ( Figure 6B ) . Moreover , when we focused on the CL13 and SG200 backgrounds , we observed that the SG200Δtup1 strain had a b expression level , filamentation and virulence capacity comparable to the wild-type CL13 strain ( Figure 6 , Figure S7 and Figure S8 ) . Thus , the effect of deleting tup1 from SG200 seems to be equivalent to removing its constitutive expression of mfa2 , which would suggest a putative role for the pheromone responsive pathways in tup1 mutant phenotypes . In our earlier experiment we bypassed the requirement for cell fusion by using the SG200 strain to identify a post-fusion requirement for tup1 in U . maydis filamentation . However , this experiment does not exclude a role for tup1 in mating between compatible strains as well , especially since both a and b loci genes are in the same position of the genetic pathway that controls the dimorphic transition . Moreover , as commented above , the similarity between SG200Δtup1 and CL13 strains may reflect a role for tup1 in the transduction of the pheromone signal . To test this possibility , we extracted total RNA from a FB1Δtup1 vs FB2Δtup1 cross grown on charcoal-containing media for 24 hours and compared mfa1 and bE1 expression with a wild-type strains cross by Northern blot . In the wild-type cross , as a result of the recognition of pheromones by receptors of opposite mating types , activation of pheromone responsive pathways takes places , which is reflected in the expression of genes at both a and b loci . In the case of the tup1 mutant cross , however , we observed reduced mfa1 and bE1 expression ( Figure 7A ) , indicating that tup1 is necessary for wild-type expression of these genes . Accordingly , FB1Δtup1 and FB2Δtup1 strains drastically reduced conjugation hyphae formation upon stimulation with synthetic pheromones of the opposite mating type ( Figure 7B and 7C ) . Thus , tup1 is required for signal transduction upon stimulation with pheromone and expression of genes at both a and b loci , which is reflected in the observed pre and post-fusion defects of Δtup1 cells . The expression of a and b loci genes is controlled by the cAMP and MAP kinase pathways through their common effector Prf1 . To situate tup1 within this genetic context , we used the FB1Pcrg1:fuz7DD strain , which harbors a constitutively active allele of fuz7 MAPKK under the control of the arabinose inducible promoter crg1 [51] ( see Figure 1 for components of the MAP kinase pathway ) . Upon induction , this strain promotes the expression of a and b loci genes via the Prf1 transcription factor . After deleting tup1 from this strain , we checked for a and b loci gene expression under inducing conditions . As expected , increased transcription for genes at both loci was observed in the wild-type strain; however , this was not the case for the tup1 mutant , indicating that Tup1 regulates a and b gene expression downstream of Fuz7 MAPK kinase ( Figure 8A ) . Since Tup1 is involved in regulating the expression of genes related to glucose metabolism , the expression level of Fuz7 under the control of the crg1 promoter was also examined . No difference in fuz7DD expression was observed between the wild-type and the Δtup1 strains ( Figure 8A ) . Apart from its effect on the expression of the previously mentioned genes , induction of the fuz7DD allele , promotes conjugation tube formation through a Prf1 independent pathway that also requires the action of Kpp2 MAP kinase [51] . Thus , we wondered whether the induction of fuz7DD in the tup1 deletion strain could also induce conjugation tube formation . As shown in Figure 8B , tup1 mutants in this background were able to form conjugation hyphae at similar levels to wild-type fungi in inducing conditions ( Figure S9 for quantification ) . This result makes it unlikely that Tup1 is regulating conjugation tube formation downstream of the MAP kinase cascade and , at the same time , strongly suggest that tup1 regulates mating-type genes downstream of Kpp2 MAP kinase . We have shown that tup1 seems to regulate the expression level of a and b loci genes acting downstream of the MAP kinase cascade . Since the Prf1 transcription factor is the genetic element connecting the MAP kinase cascade and the mating-type genes , we measured prf1 expression level in a FB1Pcrg1:fuz7DD background under inducing conditions . The removal of tup1 prevented the increase in prf1 expression ( Figure 8A ) , indicating that tup1 is required for prf1 expression upon MAP kinase cascade induction . Moreover , the filamentation defects on charcoal-containing media as well as on the plant surface were rescued with the constitutive expression of prf1 ( Figure 8C and 8D ) . These results strongly suggest that tup1 affects mating and b-dependent filament formation through control of prf1 transcription factor expression level rather than by controlling the expression of a and b loci genes directly . As constitutive bE/bW expression did not fully complement Δtup1 phenotypes , we were interested in identifying other Tup1 regulated genes , that might also have roles in the dimorphic transition and virulence in U . maydis . For this purpose we performed a microarray analysis with custom Affimetrix array ( MPIUstilagoA ) , covering 5823 of the 6787 predicted U . maydis genes , and compared the gene expression of SG200 and SG200Δtup1 strains grown on MM-charcoal array plates for 48 hours ( see Methods ) . We identified a total of 115 genes ( around 2 % of the covered genes ) with altered expression in the tup1 mutant strain . Of these , 59 were upregulated and 56 downregulated . Within this list appear the bE and bW genes together with 34 genes that have also been described as b regulated genes [63] , and 17 genes described as pheromone regulated [64] ( Table S3 ) . Thus , around 36% of the genes directly or indirectly regulated by tup1 are also regulated upon bE/bW heterodimer and/or pheromone/fuz7DD induction , in agreement with our earlier results and supporting the quality of our dataset . Additionally , in order to experimentally validate our microarray data , the differential expression of some of the genes was confirmed by Northern blot analysis ( Figure 9A ) . All the 115 Tup1-regulated genes were classified in functional categories using the Blast2Go tool [65] . Enrichment analysis of genes up-regulated by the deletion of tup1 did not reveal a significant over-representation in any of the GO categories ( Table S4 ) . Of the genes down-regulated upon tup1 deletion our analysis revealed a significant over-representation in two GO categories: “Carbohydrate metabolic process” ( GO:0005975; 8 genes ) and “Antioxidant activity” ( GO:0016209; 3 genes ) ( Table S4 ) . 4 of the 8 genes belonging to the first category were also b-regulated genes , with two of them defined as strictly b-dependent ( Table S3 ) . The second category comprises proteins involved in the inhibition of dioxygen or peroxide-induced reactions and could be related to pathogenicity since production of these compounds is a well-characterized plant defense mechanism [66] , [67] , and H2O2 detoxification is required for U . maydis virulence [68] . Interestingly , several tup1-regulated genes are associated with processes that could be related to the morphological switch from yeast-like to filamentous growth . Almost 10% of these genes are potentially involved in cell wall synthesis or modification , revealing that the altered yeast-to-hypha transition , promoted by deletion of tup1 , results in a different cell wall composition . Significantly , we found that rop1 , that encodes a direct activator of Prf1 , was down-regulated in the tup1 deletion strain ( Table S3 ) . This suggests an indirect role for tup1 in controlling prf1 expression . Rop1 has been described as being required for the mating of compatible strains on charcoal containing media , with a post-fusion role , due to the inability of SG200Δrop1 to form white fuzzy colonies on charcoal plates . It is essential for conjugation tube formation upon pheromone stimulation , and for expression of pheromone-responsive genes [61] . These phenotypes clearly resemble the situation described for tup1 mutants; however , rop1 mutants are fully pathogenic , with no mating or filamentation defects described on the plant leaf surface [61] . In addition to rop1 , we identified an interesting candidate gene , um15096 , that could be related to the tup1 mutant phenotypes . In Schizosaccharomyces pombe , a homologue of um15096 , named pac2 , has been shown to be a repressor of ste11 ( the putative functional homologue of prf1 ) [69] . Interestingly , um15096/pac2 , herein referred to as pac2 , appeared over-expressed in the tup1 deletion strain . To check whether this putative prf1 repressor could also be playing a role during filamentation and pathogenic development , we over-expressed pac2 by integrating an extra copy of the gene under the control of the otef constitutive promoter in the ip locus of the SG200 strain . Filament formation of SG200pac2con was reduced on charcoal containing media ( Figure 9B ) and , more importantly , pathogenicity was reduced to levels comparable to tup1 mutants ( Figure 9C ) . The fact that pac2 is over-expressed in tup1 mutants together with the observation that ectopic pac2 expression decreases filamentation and virulence in the wild-type strain , strongly suggest that pac2 expression contributes to the filament formation and pathogenic defects of Δtup1 cells . Consistent with this , the deletion of pac2 from SG200 resulted in wild-type filamentation and infection rates ( Figure 9C ) . When prf1 expression was induced by constitutively activating the MAPK pathway at Fuz7 level , overexpression of pac2 abolished its expression , while deletion of pac2 did not apparently affect it . Similar results were observed for mfa1 and bE1 genes . The double Δtup1Δpac2 mutant showed the same level of expression as the single Δtup1 strain ( Figure 9D ) ; probably as consequence of the regulation of rop1 via Tup1 . Surprisingly , pac2 deletion , weakly restored the filamentation and infection defects shown by SG200Δtup1 strain ( Figure 9B and 9C ) , indicating that Pac2 contributes to tup1 deletion strain phenotypes . In summary , our microarray data reveal that at least 36% of the genes whose expression is affected by deletion of tup1 seems to be a consequence of tup1-dependent regulation of a and b loci genes through prf1 . Moreover , the role of Tup1 in the control of prf1 expression could be explained by the altered expression of rop1 and pac2 observed in the tup1 mutant strain . As Tup1 seems to have an indirect effect on prf1 transcription level through Rop1 and , putatively , Pac2 , we wondered whether the expression of other known prf1 regulators could be affected in tup1 deletion strains . Apart from Rop1 , prf1 is known to be directly regulated by Hap2 [70] and indirectly through the MAP kinase Crk1 [71] . Northern blot assays of SG200 and SG200Δtup1 grown on charcoal media showed that the expression level of crk1 was unaffected in tup1 deleted strain . In contrast , the levels of rop1 and hap2 were reduced in comparison to the wild-type strain ( Figure 10 ) . However , as Crk1 acts on prf1 indirectly , and since it has been previously reported that the effect of Crk1 on prf1 depends on the prf1 promoter UAS [71] , we tested whether Tup1 could regulate prf1 via its UAS . For this purpose , we used the HA232 strain , which harbors a GFP reporter gene under the control of the prf1 promoter UAS ( see [53] for details ) . In this strain , GFP is strongly expressed when grown on glucose-containing media , while its expression is reduced on a maltose containing media [53] . As is shown in Figure S10 , the expression levels of the reporter gene were indistinguishable in Δtup1 mutants from the wild-type in all the conditions tested . This indicates that Tup1 is unlikely to act via the prf1 promoter UAS , in contrast to Crk1 . Thus , the effect of Tup1 on prf1 expression seems to be mediated via Rop1 and Hap2 but not through the Crk1 pathway . To sum up , although other factors may be implicated in tup1 mutant phenotypes , Tup1 seems to control the dimorphic transition and participates in the virulence program of U . maydis by indirectly regulating prf1 expression via altered rop1 and hap2 expression levels , and possibly also through pac2 , which would lead to a down-regulation of prf1-dependent expression of a and b loci genes and their related phenotypes .
In the basidiomycete phytopathogen U . maydis , the switch from non-infective yeast-like growth to an infective filament formation occurs in response to different environmental cues , and is tightly controlled by complex genetic pathways in order to ensure the coordination and timing of the different processes associated with dimorphism . In this work , we have shown that the highly conserved general transcriptional repressor Tup1 plays a central role in controlling the proper expression of the genes implicated in the genetic control of mating , filamentation , and pathogenic development of this corn smut fungus . Tup1 has been shown to be important during growth of vegetative cells in other fungi such as S . cerevisiae , C . neoformans or P . marneffei [12] , [27] , [72] . In the case of Ustilago maydis , differences could be observed in the tup1 mutants , although none of these were statistically significant . Interestingly the normal growth of Δtup1 strains contrasts with the poor growth capacity described for U . maydis strains harboring a partial deletion of sql1 , the functional homolog to S . cerevisiae SSN6 . However because these strains were not stable , the role of Sql1 could not be completely analyzed [34] . Thus a comparison between Tup1 and Sql1 of their growth capacity on U . maydis vegetative cells cannot be properly established . In other fungi , single deletions of tup1 and ssn6 have been reported to result in different phenotypes [73]-[76] . For example , the deletion of SSN6 but not of TUP1 homologues is lethal in S . pombe [75] and Aspergillus nidulans [76] . Moreover , Tup1 and Ssn6 have been shown to regulate different set of genes [74] and to form independent complexes in C . albicans [77] . A central question in this study was whether tup1 is involved in the infectious process of plant pathogenic fungi . We have observed that infections with Δtup1 cells lead to a reduction in tumor formation , plant death , and a failure of spore formation , indicating that Tup1 is required for full pathogenic development in U . maydis , and making tup1 mutants unlikely to cause damage in natural environments . Thus , tup1 seems to play a conserved role in virulence of animal and plant fungal pathogens . The next key question was to try to understand the mechanism by which tup1 is required for normal tumor formation . Our results suggest that the virulence phenotype of Δtup1 cells has two main causes: ( i ) a recognition problem between compatible partners , due to the inability of tup1 mutants to form conjugation hyphae upon pheromone stimulation , and ( ii ) a filamentation defect , due to the inability of SG200 to form filaments at wild-type levels both on PD-charcoal plates and on the plant leaf surface . Additionally , the fact that the differences on conjugation hypha formation between FB1Δtup1 and FB2Δtup1 strains , though not statistically significant , together with the differential filamentation showed by crosses of these strains with their respective compatible wild-type strains on charcoal plates , suggest also a role for Tup1 in cell fusion , at least in the FB2 background . These defects result in tup1 mutants being unable to properly undergo dimorphic transition . These findings suggest that the impaired pathogenicity of tup1 mutant animal and plant fungi may also depend on a conserved role in the yeast-to-hypha transition . Consistent with the conjugation and filamentation phenotypes of tup1 mutants , the expression of a and b loci mating-type genes was reduced in tup1 deletion strains , most likely as a consequence of Tup1-dependent regulation of the prf1 transcription factor . Microarray analysis of SG200Δtup1 during filamentation on charcoal media revealed a number of mis-regulated genes whose expression was also affected upon b-compatible heterodimer and/or pheromone/fuz7DD induction , including the b locus genes themselves , supporting the proposed role for tup1 during U . maydis mating and dikaryotic filament formation . On the other hand , in our microarray analysis we did not detect tup1-dependent changes in gene expression for any of the b-dependent genes previously described as being essential for pathogenicity [60] , [63] , [78] , which is consistent with the ability , albeit reduced , of tup1 mutants to induce tumors in maize . Interestingly , the main effector that links tup1 to the control of dimorphism seems to be conserved between U . maydis and C . albicans . In contrast , the genetic pathways by which tup1 acts on filamentation seem to differ , depending on the genetic control of hypha-specific genes in each organism . In C . albicans , Tup1 is proposed to control filamentous growth through the repression of hypha-specific genes by forming complexes with the transcriptional repressors Rfg1 and Nrg1 , rather than affecting the elements in the Cph1-mediated MAPK and Efg1-mediated cAMP pathways [7] , [54]–[56] . Moreover , expression analysis of filament-specific genes in Δcph1/Δcph1 , Δefg1/Δefg1 and Δtup1/Δtup1 strains revealed common and divergent target genes [7] . Thus , Tup1 integrates into the network system proposed for the control of filament-specific genes in this fungus [7] , [10] . On the other hand , in U . maydis , Tup1 controls infective filament-specific gene expression via a central regulatory , the Prf1 transcription factor , which is transcriptionally and post-translationally regulated by the cAMP and MAPK pathways [47] , [51]–[53] . Interestingly , U maydis Prf1 is a High Mobility Group ( HMG ) transcription factor , similar to C . albicans Rfg1 . Thus , an analogous mechanism , implicating a Tup1-Prf1 complex , could explain the roles of Tup1 in the regulation of hypha specific genes in U . maydis . Moreover , in S . cerevisiae , a complex between Tup1p and the HMG-transcription factor Rox1p has also been proposed [19] , [79]-[81] . S . cerevisiae ROX1 , whose deletion can be complemented by C . albicans RFG1 [33] , is known to control hypoxic gene expression in a TUP1 dependent manner [19] , [79]–[81] . Additionally , the deletion of TUP1 increases the expression of ROX1 [82] , [83] , but Rox1p itself is also able to regulate its own expression [83] . In aerobic conditions these observations can be explained by the proposed Tup1p-Ssn6p-Rox1p complex which would regulate ROX1 expression and Rox1p-dependent hypoxic gene expression . In anaerobic conditions , however , the regulation of ROX1 expression seems to implicate an anaerobic repressor that requires Tup1p for its function [83] . Similarly , in U . maydis , the expression of prf1 is dependent on Tup1 and prf1 is also self-regulated [52] . However , when we analyzed the effect of Tup1 on prf1 expression level more deeply , we observed that at least two direct activators of Prf1 were also down-regulated upon tup1 deletion , rop1 and hap2 . This finding , although not excluding a putative Tup1-Prf1 complex , points to an indirect effect of Tup1 on the expression of prf1 and its regulated genes . Rop1 is required for pheromone response and for fuzzy colony formation on charcoal-containing plates , but is dispensable for mating and filamentation on the plant leaf surface . In the case of hap2 , it is known to be essential for the pheromone response and has also an effect on the filamentation capacity of SG200 that seem to be conserved on planta . Thus , we propose that the effect of Tup1 on prf1 is the sum of the effects of Tup1 in both rop1 and hap2 on artificial media , while only the effect on hap2 would be responsible for the on planta phenotypes . The drastic effect of tup1 deletion on prf1 expression levels on charcoal plates may be diminished on the plant leaf surface as rop1 is dispensable in this situation . In this work , we have also described a new gene , pac2 , which is likely to be playing a role in the tup1 mutant virulence phenotype , since its over-expression causes a decrease in the pathogenic capacity of U . maydis SG200 strain and its expression is increased in the SG200Δtup1 strain . Since the homologue of this gene in S . pombe is a repressor of ste11 [69] , the putative functional homologue of prf1 , we analyzed the relationship between Pac2 and Prf1 in U . maydis . We found that over-expression of pac2 in a FB1Pcrg1:fuz7DD strain abolished the prf1 expression observed in the wild type strain establishing Pac2 as a repressor of Prf1 . Accordingly , the deletion of pac2 in a SG200Δtup1 strain partially restored its filamentation and virulence defects . However , the double Δtup1Δpac2 mutant in the FB1Pcrg1:fuz7DD background shows the same prf1 expression level than the single Δtup1 strain , probably because of Tup1 control of rop1 and hap2 . Nevertheless since prf1 regulation on charcoal plates or during virulence integrates several imputs besides the MAPK pathway the relationship between pac2 and prf1 in the regulation of filamentation and pathogenicity cannot be fully established . Thus , the final role of tup1 in U . maydis virulence is also likely to be linked to its control of hap2 and pac2 mRNA levels ( Figure 11 ) . Surprisingly , although Tup1 is described as a general transcriptional repressor , the deletion of tup1 from U . maydis leads to the down-regulation of the genes that control the dimorphic transition , suggesting an activator role for tup1 in controlling them . On the other hand , determining how Pac2 controls prf1 gene expression would help to determine the role of tup1 as an activator and/or repressor during dimorphism . The way Tup1 seems to control the expression of the prf1 transcription factor , through hap2 and rop1 and , putatively , pac2 , clearly reflects the complex genetic regulation that prf1-related processes require . Similarly , the number of genes that we found to be up- or down-regulated following tup1 deletion when cultured on charcoal-containing media was equivalent . Thus , under the conditions tested , the loss of tup1 causes a similar effect on both the de-repression and repression of genes . Although this could reflect indirect changes in genes expression resulting from the repression of Tup1-gene targets , it is nevertheless an intriguing observation . Regarding an activating role for Tup1 , previous studies have also shown that Tup1 can behave as an activator as well as a repressor of the same target gene in different conditions [84] or different genetic backgrounds [85] in S . cerevisiae . Finally , we have shown that tup1 seems to be required for spore production inside maize tumors . Roles for Tup1 in sporulation have been previously reported in other fungi . In S . cerevisiae , the sporulation-specific genes DIT1 and DIT2 , which are required for spore wall formation , are regulated by Tup1p [86]; in Neurospora crassa , mutants for rco-1 , the homologue of TUP1 , are aconidial [87]; and in C . neoformans , tup1 deletion considerably reduces spore production [27] . In summary , our work provides new insights into the complex regulatory circuits for sexual and pathogenic development of U . maydis . We have identified for the first time a requirement for tup1 at several steps of the life cycle of a pathogenic plant fungus , including in the genetic pathways controlling dimorphism and virulence . Our findings contribute to a better understanding of the role of this general transcriptional repressor in pathogenic fungi and of the precise genetic control that these pathogenesis-related processes require . We consider that the roles and mechanisms of action described for U . maydis tup1 in this work will also be extremely valuable for studying the roles of tup1 in the transcriptional regulation of morphogenetic processes in other organisms .
Escherichia coli DH5α was used for cloning purposes . Growth conditions for E . coli [88] and U . maydis [42] , [89] and the quantification of appressoria formation on the plant leaf surface [60] have been described previously . Quantification of filaments was performed as for the appressoria . For studies of growth rates and morphology , cells were grown on YEPSL liquid media for 12 hours , then diluted in the same media to an OD600 of 0 . 05 and grown until an OD600 of 0 . 8-1 . Exponential growth cultures were examined under the microscope and transferred to solid plates for colony morphology studies . Growth rates on liquid media were determined by counting cells at different time-points . For charcoal mating and filamentation assays , cells were grown on YEPSL until exponential phase , washed twice with water , spotted onto PD-charcoal plates and grown for 24–48 hours at 25°C . For charcoal-grown cells used for RNA extractions , cells were spread out on charcoal plates at a concentration of OD600 = 0 . 1 per cm2 . For DNA array charcoal media see below . U . maydis strains relevant to this study are listed in Table S5 . Induction of nar promoter in AB33 [62] and crg promoter in FB1Pcrg1:fuz7DD [51] strains , and their derivatives , were done as previously described . Mating assays were performed as previously described in [90] . Pheromone stimulation was performed following the protocol of [51] . For pathogenicity assays , U . maydis strains were grown to exponential phase and concentrated to an OD600 of 3 , washed twice in water , and injected into 7 days old maize ( Zea mays ) seedlings ( Early Golden Bantam ) . Tumor formation was quantified 14 to 21 days post infection . Data are expressed as means ±SD of triplicate samples . Statistical significance was assessed using Statistical Calculators ( http://www . graphpad . com/quickcalcs/index . cfm ) and considered significant if p values were <0 . 05 . Molecular biology techniques were used as described by [88] . U . maydis DNA isolation and transformation procedures were carried out following the protocol of [91] . Deletion constructs were generated according to [36] . To generate single deletion U . maydis mutants for tup1 ( Um03280 ) , pac2 ( Um15096 ) and um04807 genes , fragments of the 5′ and 3′ flanks of their open reading frames were generated by PCR on U . maydis FB1 genomic DNA with the following primer combinations: UmTUP1KO5-1/UmTUP1KO5-2 and UmTUP1KO3-1/UmTUP1KO3-2; UmPAC2KO5-1/UmPAC2KO5-2 and UmPAC2KO3-1/UmPAC2KO3-2; Um04807KO5-1/Um04807KO5-2 and Um04807KO3-1/Um048071KO3-2; ( Sequences in Table S2 ) . These fragments were digested with SfiI and ligated with the 1 . 9 Kb SfiI carboxin resistance cassette , 2 . 7 Kb SfiI hygromycin resistance cassette , or 1 . 5 Kb SfiI neourseotricin resistance cassette as described previously [35] . Ligation products were then clone into pGEM-T-EASY vector ( Promega ) . PCR generated linear DNA for each construct was used for U . maydis transformation . For complementation of the tup1 deletion , the p123-tup1 plasmid was generated . p123-tup1 is a p123 [92] derivative in which the eGFP fragment has been substituted with the tup1 open reading frame . For this purpose , the tup1 open reading frame was amplified by PCR with the oligonucleotides Tup1-Start and Tup1-Stop , which contain NcoI and NotI restriction sequences respectively . Phusion high fidelity DNA polymerase ( Invitrogen ) was used . The PCR product was digested with NcoI and NotI , purified , and cloned into a p123 vector digested with the same restriction enzymes . Positive cloning was verified by restriction analysis and sequencing . To generate SG200Δtup1Potef:tup1 strain , p123-tup1 was linearized with SspI and integrated into SG200Δtup1 ip locus by homologous recombination . For over-expression of pac2 , the p123-pac2 plasmid was generated by replacing the eGFP fragment from p123 with the pac2 open reading frame . The Pac2 open reading frame was amplified using the oligonucleotides UmPac2ATGSmaXma y UmPac2StopNotI , digested with XmaI and NotI restriction enzymes and ligated into the p123 vector digested with the same enzymes . Successful cloning was verified by restriction analysis and sequencing . To generate SG200pac2con , p123-pac2 was linearized with SspI and integrated into SG200 wild-type strain ip locus . For constitutive expression of pac2 in FB1Pcrg1:fuz7DD , we constructed the plasmid p5HOP2 . This plasmid consists in 1 kb fragment of the upstream sequence of pac2 open reading frame ( ORF ) followed by the otef constitutive promoter , the hygromycin resistance cassette and 1 kb of the pac2 ORF integrated in a pGEM-T-EASY vector . For this purpose 1 kb fragment of the upstream sequence of pac2 was amplified with the primers Umpac2-5UTR-1 and Umpac2-5UTR-2 , using FB1 genomic DNA; the otef constitutive promoter followed by 1kb of pac2 ORF was amplified with the primers Umotefpac2 and Umpac2-+1kb , using the plasmid p123-pac2 as template . Both flanks where then digested with SfiI restriction enzyme and ligated with the hygromycin resistance cassette . This construction was ligated to a pGEM-T-EASY vector . FB1Pcrg1:fuz7DDpaccon was generated by transformation of the wild-type FB1Pcrg1:fuz7DD with the mentioned construct . Single homologous integration of the linear plasmids or PCR products transformed was verified by PCR and Southern blot . In the expression analysis , cells grown on liquid culture were recovered by centrifugation , washed with cold water , and total RNA was isolated with QIAGEN ( Valencia , CA ) RNeasy mini kit . For charcoal grown cells , biomass was recovered and transferred to liquid nitrogen pre-chilled mortars . Total RNA was then extracted from the crushed powder with trizol reagent ( Invitrogen ) and with the QIAGEN RNeasy mini kit . Isolated RNA was separated by formaldehyde denaturing agarose gel electrophoresis , and transferred overnight by capillary action to nylon membranes . Probes were obtained by PCR with the oligonucleotides indicated in Table S6 . Radioactive labelling of PCR generated probes was carried out . Radioactive bands were visualized and quantified using a Molecular Dynamics PhosphoImager . For qRT-PCR first strand cDNA synthesis was performed using the Transcriptor First Strand cDNA Synthesis Kit ( Roche ) according to the manufacturer's protocol . As a template for the reaction 1 µg of total RNA was used . Samples were incubated at 50°C for 1 hour . Real-time PCR was performed in a ABIPRISM 7000 Sequence Detection System ( Applied Biosystems ) using the Power SYBR Green PCR Master Mix according to the manufacturer's protocol . Primers used for detection are shown in Table S6 . U . maydis Tup1 sequence was obtained from MIPS U . maydis DataBase ( http://mips . gsf . de/genre/proj/ustilago/ ) . S . cerevisiae and C . albicans Tup1 sequences were obtained from SGD ( http://www . yeastgenome . org/ ) and CGD ( http://www . candidagenome . org/ ) databases , respectively . The rest of the Tup1 sequences were obtained from the NCBI . Multiple sequence alignments were made with ClustalW2 . Domain structure analysis was performed using InterProScan Sequence Search tool from the European Bioinformatics Institute ( http://www . ebi . ac . uk/ ) . Pfam retrieved domains were used . Schematic representation of the retrieved domains was performed maintaining proportions of each domain with respect to the whole protein sequence length . Cells were grown on nitrate minimal media containing 1% glucose or 1% maltose to an OD600 of 0 . 6–0 . 8 , then pelleted and resuspendend in sterile water to an OD600 of 1 . 0 . Fluorescence from 200 µl of cell suspension transferred to a microtiter plate was measured by using a POLARstar Omega fluorescence reader ( BMG LABTECH ) . GFP fluorescence was measured at a wavelength of 485 nm for excitation and 520 nm for emission . Fluorescence was normalized to OD600 . At least three independent experiments were performed , each measured in triplicate . Cell morphology of WGA-stained cells , conjugation tube and b-dependent filament formation were analyzed with a Zeiss Apotome microscope . For on planta quantification of filament and appressoria formation in co-infection experiments with U . maydis CFP and YFP labelled strains , leaf samples were stained with calcofluor white ( Sigma ) to visualize fungal material and then checked for CFP or YFP fluorescence . Quantification of filament formation on charcoal plates was performed by fluorescence analysis of colony samples from co-spotted YFP and CFP strains . Post-penetration stages were visualized by WGA-AF 488 and Propidium Iodide ( Sigma ) staining of infected leaf samples as previously described [93] . Samples were examined using a Leica fluorescence microscope , equipped with a PlanApo x 100 lens and a Deltavision widefield microscope ( Applied Precision , Issaquah , WA ) equipped with 20 , 40 , 63 and 100 x lens . Image processing was carried out using Adobe Photoshop CS2 . SG200 and SG200Δtup1 cells were grown on YEPSL until exponential phase , then washed twice with sterile water and cultured on minimal charcoal array plates ( 12 . 5% Holliday salts , 2% vitamins , 30 mM L-glutamine , 2% glucose , 4% agar and 2% charcoal , pH 7 ) during 48 hours at 25°C . 144 cm2 plates and a cell density of OD600 of0 . 1/cm2 was used . DNA-array analysis was performed using custom-designed Affymetrix chips ( UstilagoA ) . Probe sets for the individual genes can be obtained from http://mips . helmholtz-muenchen . de/genre/proj/ustilago/ . Target preparation , hybridization and data analysis was performed as described before [94] , with the following alterations: total RNA was extracted as commented in DNA and RNA procedures for charcoal growing cells; 5 µg RNA were used for first strand cDNA synthesis at 50°C with Superscript II ( Invitrogen ) ; an adjusted P-value of ≤0 . 01 for the false discovery rate [95] and a change in expression of ≥2 was used for filtering . Expression values were calculated as mean of two biological replicates . Array data can be accessed at GEO/NCBI database ( accession number GSE29591 ) . U . maydis sequence data can be found in the GenBank/EMBL data libraries under accession numbers XP_759427 for Tup1 , XP_762643 . 1 for Pac2 , , XP_756724 for bE1 , XP_756725 for bW1 , XP_758529 for Mfa1 , XP_760967 for Acf1 , XP_762479 for Egl1 , XP_762172 for Rop1 , XP_762530 for Hap2 , XP_758660 for Crk1 , XP_758860 for Prf1 , XP_757661 for Fuz7 , XP_760954 for um04807 , XP_758669 for um11413 , XP_756174 for um00027 , XP_759558 for um03411 and XP_758874 for um02727 . Other sequences used in this study have the following accession numbers: S . cerevisiae Tup1p , NP_010007; C . albicans Tup1 , AAB63195; C . neoformans Tup1 , XP_570974; P . marneffei TupA , AAL99251; N . crassa Rco-1 , AAB37245; A . nidulans TupA ACD46267; S . pombe Tup11 , NP_592873; S . pombe Tup12 , NP_592910 . | Fungal plant pathogens cause serious damage to crops with huge social and economic consequences . To cause disease , many such fungi need to change their morphology between a yeast-like , unicellular form and a filamentous state . This change , known as dimorphism , is tightly controlled by complex genetic pathways to ensure successful pathogenic development . In animal pathogens , one of the most important genes controlling dimorphism is Tup1 . In plant pathogens , however , the role for this gene is completely unknown . In this work , we describe the role of Tup1 in the dimorphism and virulence of Ustilago maydis , the plant fungal pathogen that causes maize smut disease . We show that mutant U . maydis cells lacking Tup1 are unable to properly change between yeast-like and filamentous forms , thus compromising its virulence . We look at the underlying genetic pathways , and find that Tup1 regulates key genes known to regulate dimorphism . We also show that Tup1 is essential for the production of mature fungal spores , which normally allow the fungus to disperse and infect new plants . Our results show that Tup1 is a key element in the control of both infectious and dispersible fungal forms and supports an evolutionary-conserved role for this gene in the regulation of dimorphism among animal and plant pathogenic fungi . | [
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] | 2011 | The General Transcriptional Repressor Tup1 Is Required for Dimorphism and Virulence in a Fungal Plant Pathogen |
Interferon-induced transmembrane ( IFITM ) proteins are a family of viral restriction factors that inhibit the entry processes of several pathogenic viruses , including influenza A virus ( IAV ) , in vitro . Here we report that IAV-infected knockout mice lacking the Ifitm locus on chromosome 7 exhibited accelerated disease progression , greater mortality , and higher pulmonary and systemic viral burdens as compared to wild type controls . We further observed that the phenotype of Ifitm3-specific knockout mice was indistinguishable from that of mice lacking the entire Ifitm locus . Ifitm3 was expressed by IAV target cells including alveolar type II pneumocytes and tracheal/bronchial respiratory epithelial cells . Robust Ifitm3 expression was also observed in several tissues in the absence of infection . Among murine Ifitm promoters , only that of Ifitm3 could be induced by type I and II interferons . Ifitm3 could also be upregulated by the gp130 cytokines IL-6 and oncostatin M on cells expressing appropriate receptors , suggesting that multiple cytokine signals could contribute to Ifitm3 expression in a cell or tissue-specific manner . Collectively , these findings establish a central role for Ifitm3 in limiting acute influenza in vivo , and provide further insight into Ifitm3 expression and regulation .
The interferon-induced transmembrane ( IFITM ) proteins are a family of small transmembrane proteins that mediate some of the antiviral activities of type I and II interferons [1] , [2] , [3] . IFITM-mediated restriction is specific to particular virus families . Influenza A viruses ( IAV ) , Ebola virus ( EBOV ) , Marburg virus ( MARV ) , SARS coronavirus ( SARS-CoV ) , dengue virus , and West Nile virus are all efficiently restricted by one or more IFITM family proteins . Conversely , IFITM proteins do not restrict murine leukemia virus ( MLV ) , a range of alphaviruses , and arenaviruses including Machupo virus ( MACV ) , Lassa virus ( LASV ) , and lymphocytic choriomeningitis virus ( LCMV ) [2] , [4] , [5] . Vesicular stomatitis virus is inefficiently restricted [6] , [7] . Two groups have reported cell line-specific restriction of HIV-1 [1] , [8] , but these reports differ as to which cell lines show efficient restriction . Restriction activity against non-enveloped viruses has not been reported to date . The mechanism of action of IFITM-mediated restriction has not yet been determined . Multiple lines of evidence indicate that restriction occurs after virion endocytosis but during or prior to membrane fusion . IFITM proteins prevent infection by retroviruses pseudotyped with entry proteins of IFITM-restricted viruses , but not those pseudotyped with entry proteins of unrestricted viruses [4] . Entry assays using virions containing β-lactamase-Vpr ( BlaM-Vpr ) fusion proteins likewise reveal that IFITM proteins prevent transfer of BlaM-Vpr to the cytosol of target cells [8] . Previously , we showed by fluorescent microscopy that labeled IAV virions are internalized normally and traffic to acidified endocytic compartments despite IFITM expression [4] , a conclusion confirmed and extended by Feely et al . using a rigorous set of imaging studies . They further demonstrated by fluorescent in situ hybridization that IFITM proteins cause sequestration and accumulation of IAV genetic material in late endocytic/lysosomal compartments [9] . IFITM proteins also localize to late endosomes/lysosomes as demonstrated by co-localization with LAMP1 , LAMP2 , and CD63 [4] . Moreover , IFITM protein over-expression – and in some cell lines , interferon treatment – results in enlargement of these organelles [9] . Finally , a recent report showed that endosomes in interferon-treated Ifitm knockout cells are inefficiently acidified and suggested that a physical association between Ifitm3 and a subunit of the vacuolar ATPase complex mediates this effect [10] . It appears , therefore , that IFITM proteins alter the properties of late endosomes and lysosomes , and render these organelles inhospitable to viral fusion . Such a model is supported by the pattern of IFITM-mediated restriction; excepting HIV-1 , restricted viruses generally fuse late in the endocytic pathway whereas unrestricted viruses fuse at the plasma membrane or in early endocytic compartments . Further evidence that restriction depends on the site of viral fusion comes from investigation of SARS-CoV . SARS-CoV requires lysosomal cathepsins to activate its entry protein and fuse with the lysosomal membrane [11] . Treating receptor-bound virions with trypsin , however , removes this cathepsin dependency , induces fusion at the plasma membrane , and bypasses IFITM-mediated entry restriction [4] . Most vertebrates have two or more IFITM genes [12] . The human IFITM family is composed of four functional genes , IFITM1 , 2 , 3 , and 5 . Murine Ifitm1 , 2 , 3 , and 5 , located on chromosome 7 , are clear orthologs of their human counterparts but mice possess two additional loci: Ifitm6 , also located on chromosome 7 , and Ifitm7 , a retrogene on chromosome 16 [13] . The proteins expressed by these genes restrict influenza A virus ( IAV ) , filovirus , flavivirus , and SARS coronavirus entry in vitro with varying efficiencies [4] , [6] . Ifitm5 expression is limited to bone [14] , [15] , and the roles of Ifitm6 and 7 are not yet clear . Two strains of knockout mice were used in the following experiments . IfitmDel mice lack Ifitm1 , 2 , 3 , 5 , and 6 and Ifitm3egfp mice lack Ifitm3 alone [13] . The aim of the present study was to determine the in vivo contribution of the Ifitm proteins to the innate immune control of IAV . We show that Ifitm3 alone makes a significant contribution to the control of influenza in mice and provide further insight into its expression and regulation .
To determine the in vivo relevance of Ifitm proteins , we challenged cohorts of IfitmDel , wild type , and heterozygous male mice with intranasal doses of either 500 or 1000 PFU of influenza A/PR/8/34 ( H1N1 ) ( PR8 ) . In accordance with institutional policies , mice were considered moribund and euthanized upon loss of 20% of initial body weight , defined as the 2-day average of their pre-inoculation weights . At both challenge doses , knockout mice exhibited significantly accelerated disease progression and mortality compared to wild type controls ( Figure 1A ) . Heterozygotes had an intermediate phenotype . A modest but insignificant difference was observed in the mean survival time of knockout mice receiving each challenge dose , with mice receiving 1000 PFU surviving 0 . 7 days longer . With the 500 PFU challenge dose , two of five wild type animals survived acute infection but disease remained uniformly lethal in knockout mice . Weight loss at days 3 and 5 was significantly increased in IfitmDel and heterozygous animals as compared to wild type controls ( Figure 1B; plots of body weight in Figure S1A ) . Weight loss in wild type animals receiving a 500 PFU challenge followed trends reported by other investigators [16] . Price et al . found that mice generally maintained body weight until day 4–5 post-infection , after which they underwent a rapid period of weight loss that continued until day 8–9 . The rapid clinical progression beginning on day 4–5 coincided with the onset of cellular and humoral immunity while recovery on day 8–9 corresponded to clearance of the virus . In contrast to wild type animals , IfitmDel mice began a steep decline in weight beginning on day 2 consistent with a failure of innate control of early infection . The variability in the rate of disease progression of knockout mice was also far lower than that of both wild type and heterozygous animals ( Figure S1A ) . These results demonstrate that Ifitm proteins mediate innate immunity to influenza A virus during acute infection and that the effect of their deletion is powerful enough to overwhelm other sources of experimental variation . The intermediate phenotype of heterozygous animals shows that gene dosage is important for Ifitm protein-mediated viral restriction . The requirement for high gene dosage may also explain the duplication of the Ifitm alleles observed in multiple species . Viral loads were measured in a separate cohort of mice euthanized three days after infection , the period during which body weights of wild type and IfitmDel animals diverge ( Figure S1A ) . Weight loss trends in this cohort of animals were similar to those of previous cohorts ( Figure S1B ) . Viral loads , as assessed by real-time RT-PCR of lung RNA , were 2 . 7-fold higher on average ( p = 0 . 0159 ) in IfitmDel mice as compared to wild type controls ( Figure 1C ) . Differences in splenic viral loads ( Figure 1C ) were similarly increased ( 15 . 9-fold , p = 0 . 0079 ) . Although the relative difference in splenic viral loads was high , IAV genome copy numbers in the spleen were on the order of 104 to 105 times lower than those in the lung ( Figure S1C ) and genome expression in the spleens of wild type animals was barely above the detection threshold of our assay . Similarly , we did not identify splenic lesions or any other lesions beyond those in the lungs , trachea , and nasal passages , consistent with the dependence of PR8 hemagglutinin on respiratory proteases for cleavage and activation [17] , [18] , [19] , [20] . We therefore speculate that increased splenic viral loads in knockout mice are a result of more robust infection in the lung and subsequent leakage of virus or viral debris into the periphery . Previously , we reported that murine embryonic fibroblasts ( MEFs ) derived from IfitmDel knockout mice are highly susceptible to IAV and that both baseline and interferon-induced Ifitm protein expression contribute to the IAV resistance in wild type MEFs [2] . Ifitm3 expression alone was sufficient to restore IAV entry restriction to knockout fibroblasts [9] . We have shown that over-expression of most murine Ifitm proteins in a human cell line confers some level of IAV restriction but that Ifitm3 is most effective while Ifitm5 , 6 , and 7 posses comparatively little activity [4] . We confirmed this observation in the context of IfitmDel MEFs ( Figure S2A ) ; again murine Ifitm3 demonstrated the most potent IAV restriction activity regardless of HA type , likely in part due to its higher steady-state expression level ( Figures S2B and S2C ) . Although Ifitm3 most effectively restricts IAV in vitro , most human and murine Ifitm proteins restrict IAV to some extent when over-expressed [4] . To determine the specific in vivo contribution of Ifitm3 , we performed an additional intranasal challenge of wild type , IfitmDel , and Ifitm3-specific ( Ifitm3egfp ) knockout mice with 500 PFU of PR8 . Mortality and weight loss trends of IfitmDel and wild type animals were similar to those of Figure 1 above . Survival times of Ifitm3-specific knockout mice were not significantly different from those of IfitmDel knockout mice ( p = 0 . 5380 ) and the weight loss curves for these two genotypes were superimposed ( Figures 2A and B ) . As with the IfitmDel mice , heterozygotes of the Ifitm3-specific knockout mice exhibited an intermediate phenotype . We therefore conclude that , among Ifitm proteins , only Ifitm3 makes a substantial contribution to PR8 resistance in vivo . Because the Ifitm3 promoter is sensitive to interferon signaling we reasoned that other Jak/STAT-mediated cytokines might likewise induce Ifitm3 expression . We tested the ability of a type III interferon ( IFNλ2 ) , the acute phase cytokine IL-6 , and oncostatin M ( OSM ) , a cytokine released from activated dendritic cells that synergizes with type I interferon [21] , to upregulate Ifitm3 expression . The latter two molecules belong to a diverse family of cytokines that signal through hetero- or multimeric receptors that activate the Jak/STAT pathway via a common gp130 subunit [22] . Consistent with previous studies , type I and type II interferons induced Ifitm3 expression in both NIH 3T3 murine fibroblasts , and the murine macrophage cell line , RAW264 . 7 . IFNλ2 was ineffective , likely due to lack of the appropriate receptor . In 3T3 cells , however , oncostatin M ( OSM ) induced strong Ifitm3 expression and IL-6 was a potent inducer of Ifitm3 expression and in RAW264 . 7 cells ( Figure 3A ) . The ability of these two cytokines to modulate Ifitm3 expression correlated with the surface expression of their cognate receptors ( Figure 3B ) , and was not due to increased expression of type I or II interferons ( Figure S3 ) . These data raise the possibility that other gp130-mediated cytokines such as IL-11 , IL-27 , and LIF could modulate basal and inducible Ifitm3 expression in cells bearing appropriate receptors . Furthermore , they indicate greater specificity and complexity in the regulation of Ifitm3 expression , and possibly other interferon-stimulated genes , than previous studies have implied . Human IFITM1 , 2 , and 3 are interferon-stimulated genes [23] . Murine Ifitm3 is shown to be interferon-inducible but the regulation of the other murine Ifitms , particularly the mouse-specific family members Ifitm6 and 7 , has not been studied . Therefore , we assayed the responsiveness of the murine Ifitm promoters to both type I and type II interferons ( Figure 3C ) . We observed that , of the murine Ifitm genes , Ifitm3 alone has an interferon-inducible promoter . Figure 3C also suggests that – at least under these tissue culture conditions – Ifitm promoters may be constitutively active , perhaps contributing to an intrinsic antiviral state . We validated the results of this promoter assay by analyzing cytokine-mediated upregulation of murine Ifitm1 , 2 , and 3 transcripts by qRT-PCR . Consistent with the promoter studies , and in contrast to the behavior of their human orthologs , murine Ifitm1 and 2 were unresponsive to both type I and type II interferons . Ifitm3 transcript levels were significantly upregulated by both IFNα2 and IFNγ in both NIH 3T3 and RAW264 . 7 cells . Ifitm3 was similarly induced by OSM in 3T3 cells and IL-6 in RAW cells consistent with both western blot and promoter data ( Figure 3D ) . Ifitm1 also appeared to respond to OSM stimulation although this increase was not statistically significant . The data in Figures 2 and 3 show that Ifitm3 is the most potent IAV restriction factor of the murine Ifitm proteins and that Ifitm3 expression alone is efficiently induced interferons . To determine where and under what conditions Ifitm3 is expressed in vivo , we performed comprehensive immunohistochemical staining of the lungs of infected and uninfected wild type mice to determine baseline and influenza-induced patterns of Ifitm3 expression . Contrary to expectations , constitutive expression of Ifitm3 was seen in many lung tissues; marked induction was seen only in the respiratory epithelium of lower airways ( Figure 4A , B , C ) . The pattern of distribution was similar in the remaining lung tissue in IAV-infected and uninfected mice . Constitutive expression was observed in respiratory epithelial cells of the upper airways ( Figure 4D , E , F ) , the visceral pleura ( Figure 4G , H , I ) , and in leukocytes ( Figure 4J , K , L ) , suggesting that Ifitm3 may provide protection against influenza prior to the interferon and acute-phase responses . No immunostaining of Ifitm3-specific knockout mice was observed , confirming the specificity of the antibody for Ifitm3 ( Figure S4 ) . We also localized Ifitm3 expression specifically to influenza A virus target cells – alveolar type II pneumocytes and ciliated respiratory epithelial cells [24] – by immunofluorescence and confocal microscopy . Type II pneumocytes were identified by DC-LAMP expression , a marker of type II pneumocyte-specific lysosome-related organelles [25] . Ifitm3 is not only expressed by DC-LAMP positive cells but also colocalizes with DC-LAMP itself , consistent with previous in vitro reports of its localization to endosomal/lysosomal membranes ( Figure 5A ) . In contrast , ciliated respiratory epithelium shows intense labeling of the apical cytoplasm or plasma membrane rather than the punctate intracytoplasmic distribution typical of lysosomes ( demonstrated by lysosomal marker MAC-3; Figure 5B ) . Whether Ifitm3 functions differently in the context of these cells or its apical localization is the end result of endo/lysosomal exocytosis is unknown . While this manuscript was under review , Everitt et al . published data showing that Ifitm3-specific knockout mice are more susceptible to infection with influenza A virus X31 ( H3N2 ) than wild type controls [26] . They also identified and characterized a polymorphism in human IFITM3 associated with reduced restriction activity and found an increased prevalence of this mutant allele in patients hospitalized for influenza . Our data support and extend the core conclusions of their mouse studies . We further show that mice lacking entire Ifitm locus exhibit a nearly identical disease course to those lacking Ifitm3 alone , and that their respective heterozygote littermates exhibit a phenotype intermediate between knockout and wild type mice . Morever , Ifitm3 is expressed on key IAV target cells in the lungs , and among murine Ifitms , is uniquely induced by type I and II interferons . Collectively these data make clear a primary role for Ifitm3 in the control of influenza A virus in mice . We also show that many tissues express basal levels of Ifitm3 proteins , and that at least two gp130-family cytokines – OSM and IL-6 – can induce its expression , even in the absence of an interferon response . These results suggest greater complexity in the in vivo regulation of other well-characterized interferon-stimulated genes as well . We further noted Ifitm3 expression in cell populations that are not major IAV targets such as macrophages and endothelial cells . This implies that Ifitm3 deletion will likely enhance susceptibility to infections by monocyte-tropic flaviviruses and macrophage- and endotheliotropic filoviruses . Our results may also have implications for treatment of viral infections . The fact that Ifitm3 expression can be induced by gp130-mediated cytokines may allow for modulation of its expression in a more targeted , less pleiotropic way by agonizing or antagonizing cell type-specific gp130 heterodimeric receptors . More generally , our findings suggest that pharmacologic induction of human IFITM3 expression or emulation of IFITM3 activity could provide a broad-spectrum therapeutic approach to the treatment of a range of human pathogenic viruses .
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 . These studies were approved by the Institutional Animal Care and Use Committee of Harvard Medical School ( Protocol Number 04743 ) . Human influenza virus A/PR/8 ( H1N1 ) propagated in chicken eggs and sucrose gradient purified from allantoic fluid was obtained from Charles River Laboratories , aliquoted , and frozen . Titer was determined by standard plaque assay on Madin-Darby canine kidney cells . Heterozygous IfitmDel and Ifitm3egfp mice ( background strain C57BL/6J-Tyrc-2J/J ) were generously provided by Dr . David Adams ( The Wellcome Trust Sanger Institute , Cambridge , United Kingdom ) . These animals were housed under specific pathogen-free conditions prior to infection . All mice were between 8 and 10 weeks old with the exception of three 12-week-old littermates ( one of each genotype ) used in Figure 1A . Mice were anesthetized by intraperitoneal injection of a cocktail of ketamine ( 2 mg per 25 g body weight ) and xylazine ( 0 . 2 mg per 25 g body weight ) in 200 µl of normal saline . Anesthetized mice were inoculated intranasally with influenza A virus diluted in 50 µl of phosphate-buffered saline divided evenly between both nostrils and administered over the course of one to two minutes . Animals were weighed once daily and , per institutional requirements , euthanized upon loss of 20% of initial body weight or when deemed moribund based on clinical signs . All procedures were performed with the approval of Harvard University's Institutional Animal Care and Use Committee . Lungs and spleens were harvested , rapidly frozen , and stored at −80°C prior to RNA isolation . Tissue was thawed in RNALater , transferred to 1 ml ( lungs ) or 0 . 5 ml ( spleens ) of RNAase-free water and mechanically homogenized with a QIAGEN TissueRuptor . RNA extraction was performed by means of the QIAGEN RNeasy Tissue Kit and QIAshredder columns ( QIAGEN ) . Viral loads were determined by real-time TaqMan RT-PCR . Influenza A viral M2-specific primer and probe sequences were adapted for use with IAV PR/8/34 from protocols developed by the Centers for Disease Control [27] . Sequences were: 5′-GAC CAA TCC TGT CAC CTC TGA C-3′ ( forward primer ) , 5′-AGG GCA TTT TGG ACA AAG CGT CTA-3′ ( reverse primer ) , and 5′-TGC AGT CCT CGC TCA CTG GGC ACG-3′ ( probe ) . Reporter and quencher dyes were FAM and IBFQ respectively with an additional internal Zen Quencher ( Integrated DNA Technologies ) . Similarly labeled Gapdh primer and probe sequences were: 5′-GTG GAG TCA TAC TGG AAC ATG TAG-3′ ( forward primer ) , 5′-AAT GGT GAA GGT CGG TGT G-3′ ( reverse primer ) , and 5′-TGC AAA TGG CAG CCC TGG TG-3′ ( probe ) ( Integrated DNA Technologies ) . 25 µl reactions containing 25 ng RNA , 50 pmol each primer , and 25 pmol probe were prepared using the Superscript III One-Step RT-PCR System with Platinum Taq DNA Polymerase and ROX ( Invitrogen ) . Cycling conditions were: 50°C for 30 min ( reverse transcription ) , 95°C for 2 min ( denaturation ) , and 45 cycles of 95°C for 15 seconds ( dentaturation ) and 55°C for 45 sec ( annealing , extension , and fluorescence acquisition ) . Cycling and fluorescence detection were performed in an ABI Prism 7500 thermal cycler ( Applied Biosystems ) . Entry assays were performed on murine embryonic fibroblasts ( MEFs ) derived from IfitmDel knockout mice . Cells were transduced with retroviral puromycin-selectable vectors encoding the murine Ifitm proteins or with an empty vector control , as previously described [4] . MEFs were cultured for one week in puromycin to correct for variation in transduction efficiency . GFP-encoding retroviruses pseudotyped with PR8 neuraminidase and IAV HA ( derived from influenza A/PR/8/34 ( H1N1 ) , influenza A/Thailand/2 ( SP-33 ) /2004 ( H5N1 ) , and A/FPV/Rostock ( H7N1 ) ) or the envelope glycoprotein of MLV or LASV were prepared as previously described [11] , [28] . Ifitm-expressing MEFs were incubated with pseudoviral particles by spin-inoculation at 4000×g for 30 minutes at 4°C . Cells were washed and returned to growth medium . 48 hours later , cells were harvested , fixed in 1% paraformaldehyde , and GFP expression was quantified by flow cytometry . RAW264 . 7 cells and 3T3 cells were incubated with varying concentrations of IFNα2 ( eBioscience ) , IFNγ2 ( Antigenix America ) , IFNγ ( Antigenix America ) , IL-6 ( Sigma ) , or OSM ( Sigma ) . Two days later , cells were lysed with 1% NP40 and lysates were analyzed by SDS-PAGE and western blot . Goat anti-mouse Ifitm3 antibody ( R & D systems ) and HRP-conjugated rabbit anti-goat IgG secondary antibody ( Sigma ) were used to detect the expression of Ifitm3 . Murine anti-β-tubulin antibody ( Sigma ) and HRP-conjugated rabbit anti-mouse IgG secondary antibody ( Santa Cruz Biotechnology ) were used to measure the expression of tubulin as a loading control . Expression of myc-tagged murine Ifitms was determined using the monoclonal 9E10 antibody ( Santa Cruz Biotechnology ) . 5×105 NIH 3T3 or RAW 264 . 7 cells were stained with a panel of rat monoclonal IgG2a antibodies against the OSM receptor ( clone 30-1 , MBL International Corporation ) , IL-6 receptor ( clone 255821 , R&D Systems ) , gp130 ( clone 125623 , R&D Systems ) , or an equal amount of an isotype control antibody ( clone eBR2a , eBiosciences ) . Secondary labeling was performed with Alexa 488-conjugated donkey anti-rat ( Invitrogen ) and fluorescence was measured on a BD Biosceinces FACSCalibur flow cytometer . Approximately 800 bp of each murine promoter region was amplified from wild type MEF genomic DNA and subcloned into the XhoI and HindIII sites of the luciferase reporter vector pGL3-Enhancer ( Clontech ) . Primers used were as follows ( promoter: forward/reverse ) : Ifitm1: 5′-CCC CAC ATA AAA GGT CAT GG-3′/5′-TCG GCT TTT GAA GCT GCA GA-3′ , Ifitm2: 5′-CTC CTC CTT GCT CCA TTC TG-3′/5′-ACT GAC TCT GGA ACA ATC GC-3′ , Ifitm3: 5′-GAG TGG CTG TAG CAC CAA CA-3′/5′-GCG GAG CAA AGG CAG CAC-3′ , Ifitm5: 5′-CCT CTT TGC CTG CTG TCT TC-3′/5′-TTC CAG CGC CGT GTC TTC C-3′ , Ifitm6: 5′-CGA TCC TGT TTT GCC ATC TT-3′/5′-TTT GTG CTT AAA GGA AGC AAG GAA-3′ , Ifitm7 5′-ATT GAG ATG GGG TTT CAC CA-3′/5′-TTG GTT TTT GAG GCT GGA AGA G-3′ . NIH 3T3 cells were cultured in DMEM supplemented with 10% calf serum ( Colorado Serum Company ) , non-essential amino acids , and penicillin/streptomycin . 3T3 cells in 12 well plates were cotransfected with 0 . 5 µg of the promoter/pGL3 construct and 0 . 5 µg of a glucokinase promoter-driven constitutive β-galactosidase reporter ( pGK-β-gal ) as a transfection efficiency control . 3T3 cell transfection was carried out using the TransIT-3T3 Transfection Kit ( Mirus ) . Transfection complexes were removed after 6 hours and growth medium alone or medium containing 100 ng/mL murine IFNα2 ( eBioscience ) or IFNγ ( Antigenix America ) was added to the cells . After 24 hours of stimulation , cells were lysed and enzymatic activities quantitated by means of the commercial Luciferase Assay System and β-Galactosidase Enzyme Activity Kit ( Promega ) in a Victor3V plate reader ( PerkinElmer ) . Untransfected wells were used to determine background signal . Data are presented as the background subtracted luciferase readout for each sample normalized to the background-subtracted β-galactosidase readout for that sample . NIH 3T3 and RAW 264 . 7 cells were grown in 12-well plates . Triplicate wells containing subconfluent cells were incubated in growth medium alone or treated for 24 hours with medium containing 500 ng/mL IFNα2 , 100 ng/mL IFNγ2 , 250 ng/mL OSM ( 3T3 cells ) , or 250 ng/mL IL-6 ( RAW cells ) . RNA was isolated from cells by means of the RNeasy Mini Kit and QIAshredder columns ( QIAGEN ) . 3T3 cells used as controls for Figure S3 were transfected with 1 5 µg/ml high molecular weight poly I:C complexed with LyoVec transfection reagent ( Invivogen ) for 24 hours before harvest . Primer and probe sequences for detection of murine Ifitm1 , 2 , and 3 transcripts follow . Ifitm1: 5′-ACC ACA ATC AAC ATG CCT GA-3′ ( forward primer ) , 5′-CAC CAT CTT CCT GTC CCT AGA-3′ ( reverse primer ) , and 5′-ACA CTC TTC ATG AAC TTC TGC TGC CTG-3′ ( probe ) , Ifitm2: 5′-TTT TCT CTA CCA CCT CTG TGG T-3′ ( forward primer ) , 5′-TGA ATC CAC TGT GGA CAG ATA G-3′ ( reverse primer ) , and 5′-CGG TCC ACA TCT GCC CCG CC-3′ ( probe ) , and Ifitm3: 5′-CTG AAC ATC AGC ACC TTG GT-3′ ( forward primer ) , 5′-TTT TGG TGG TTA TCA AGT GCA CT-3′ ( reverse primer ) , and 5′-TCC GGT CCT GAA GTG CTT CAC CCT-3′ ( probe ) . Murine IFNβ1 was amplified with: 5′-AGA TTC ACT ACC AGT CCC AGA-3′ ( forward primer ) , 5′-TGA AGA CCT GTC AGT TGA TGC-3′ ( reverse primer ) , and 5′-AGG CAA CCT TTA AGC ATC AGA GGC G-3′ ( probe ) . Murine IFNγ1 was amplified with: 5′-TCC ACA TCT ATG CCA CTT GAG-3′ ( forward primer ) , 5′-CTG AGA CAA TGA ACG CTA CAC A-3′ ( reverse primer ) , and 5′-TTC CTC ATG GCT GTT TCT GGC TGT-3′ ( probe ) . Gapdh ( see Viral load measurements above ) was used as a housekeeping control . Primers were 5′ labeled with FAM and included 3′ IBFQ and internal Zen quencher dyes ( Integrated DNA Technologies ) . Cycling conditions were: 50°C for 30 min ( reverse transcription ) , 95°C for 2 min ( denaturation ) , and 45 cycles of 95°C for 15 seconds ( dentaturation ) and 61 . 5°C for 40 sec ( annealing , extension , and fluorescence acquisition ) . All reactions were performed in duplicate . Mouse lungs were inflated with 10% neutral buffered formalin , fixed for 24 hours , and stored in phosphate-buffered saline prior to paraffin embedding . 5 µm sections were deparaffinized and subjected to heat-mediated epitope retrieval in low pH Antigen Unmasking Solution ( Vector Labs ) . Sections were stained for Ifitm3 ( rabbit , Abcam ) , influenza A virus H1N1 ( goat , Abcam ) , Mac-3 ( rat IgG2a clone M3/84 , BD Pharmingen ) , and DC-LAMP ( rat IgG2a clone 1010E1 . 01 , Imgenex ) . Secondary fluorescent labeling was performed with Alexa Fluor 488 , 543 , or 633-conjugated secondary antisera of donkey origin ( Invitrogen ) . Ifitm-tranduced MEFs grown on 8 well chamber slides were fixed and permeabilized by immersion in a 1∶1 mixture of methanol and acetone at −20°C for 10 minutes prior to staining . Cells were stained with primary anti-myc clone 9E11 ( Santa Cruz Biotechnology ) and Alexa Fluor 488-conjugated goat anti-mouse secondary antibody ( Invitrogen ) . Slides were coverslipped with ProLong Gold Antifade mounting medium containing DAPI ( Invitrogen ) . Imaging was performed on a Leica TCS SP5 laser-scanning confocal microscope through a 63× 1 . 4 NA oil-immersion objective . Brightfield immunohistochemistry for Ifitm3 was carried out with similar slide preparation and primary antibodies . Secondary labeling and detection were performed with biotinylated horse anti-rabbit , avidin/biotin/peroxidase complex , and Nova Red chromagen ( Vector Labs ) . Digital images were color balanced for publication . | The human genome contains many genes devoted to combating viral infections . Some of these genes encode a family of proteins called interferon-induced transmembrane ( IFITM ) proteins which were recently discovered to inhibit infection by influenza A viruses in cell culture experiments . Here we show that genetically engineered mice lacking the murine equivalents of the human IFITM genes are more susceptible to influenza than mice with a full complement of these genes . In addition , deletion of one of these genes alone , Ifitm3 , made mice equally susceptible to infection , showing that the Ifitm3 protein plays a central role in the control of influenza A virus in living animals . We also show that murine Ifitm proteins are expressed on cells targeted by influenza A viruses and that the control of their expression in animals is more complex than suggested by previous cell culture studies . | [
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] | 2012 | Ifitm3 Limits the Severity of Acute Influenza in Mice |
Small , secreted proteins have been found to play crucial roles in interactions between biotrophic/hemi-biotrophic pathogens and plants . However , little is known about the roles of these proteins produced by broad host-range necrotrophic phytopathogens during infection . Here , we report that a cysteine-rich , small protein SsSSVP1 in the necrotrophic phytopathogen Sclerotinia sclerotiorum was experimentally confirmed to be a secreted protein , and the secretion of SsSSVP1 from hyphae was followed by internalization and cell-to-cell movement independent of a pathogen in host cells . SsSSVP1∆SP could induce significant plant cell death and targeted silencing of SsSSVP1 resulted in a significant reduction in virulence . Through yeast two-hybrid ( Y2H ) , coimmunoprecipitation ( co-IP ) and bimolecular fluorescence complementation ( BiFC ) assays , we demonstrated that SsSSVP1∆SP interacted with QCR8 , a subunit of the cytochrome b-c1 complex of mitochondrial respiratory chain in plants . Double site-directed mutagenesis of two cysteine residues ( C38 and C44 ) in SsSSVP1∆SP had significant effects on its homo-dimer formation , SsSSVP1∆SP-QCR8 interaction and plant cell death induction , indicating that partial cysteine residues surely play crucial roles in maintaining the structure and function of SsSSVP1 . Co-localization and BiFC assays showed that SsSSVP1∆SP might hijack QCR8 to cytoplasm before QCR8 targeting into mitochondria , thereby disturbing its subcellular localization in plant cells . Furthermore , virus induced gene silencing ( VIGS ) of QCR8 in tobacco caused plant abnormal development and cell death , indicating the cell death induced by SsSSVP1∆SP might be caused by the SsSSVP1∆SP-QCR8 interaction , which had disturbed the QCR8 subcellular localization and hence disabled its biological functions . These results suggest that SsSSVP1 is a potential effector which may manipulate plant energy metabolism to facilitate the infection of S . sclerotiorum . Our findings indicate novel roles of small secreted proteins in the interactions between host-non-specific necrotrophic fungi and plants , and highlight the significance to illuminate the pathogenic mechanisms of this type of interaction .
Sclerotinia sclerotiorum ( Lib . ) de Bary is an exemplary necrotrophic phytopathogenic fungus with a broad host range . At least 408 species of plants are susceptible to this white mold fungus , most of them are from Dicotyledonae but a few are from Monocotyledonae such as onion and garlic [1] . S . sclerotiorum is also a cosmopolitan pathogen of many economically important crops , including oilseed rape ( Brassica spp . ) , sunflowers , soybeans , peanuts and lentils , and its infection often leads to a significant loss of crop production . Plant pathogens have been categorized as biotrophic , hemibiotrophic and necrotrophic pathogens based on the lifestyles of these agents , and the pathogenic mechanisms are obviously different among the different types of pathogens . Biotrophic pathogens must manipulate host physiology and derive nutrients from living host cells and tissues , whereas hemibiotrophic pathogens absorb nutrients from living cells during the early biotrophic stage of infection and subsequently kill host cells during the later necrotrophic stage of infection . The nutrient acquisition of necrotrophic pathogens is based on host cell killing [2] . Often , biotrophic and hemibiotrophic fungi secrete effectors that manipulate host cell structure and function to obtain nutrients and suppress plant defenses , thereby facilitating infection [3] . The secretion and transfer of effectors into plant host cells are also essential for the pathogenesis of many biotrophic and hemibiotrophic fungi [4–7] . Plant cell death triggered through hypersensitive responses ( HRs ) is a major obstacle for the further expansion of biotrophic and hemibiotrophic fungi during the initial stage of infection . However , for necrotrophic fungi , host cell death might be beneficial rather than detrimental for pathogenesis; thus , the canonical necrotrophic fungus S . sclerotiorum secretes a wide array of cell-wall-degrading enzymes ( CWDEs ) to facilitate host cell wall degrading and ultimately promote infection [8] . As a non-selective phytotoxin , oxalic acid ( OA ) produced by S . sclerotiorum can also contribute to pathogenesis in a number of ways ( e . g . acidification , chelation of Ca2+ , low pH activation of degradative enzymes etc . ) that augment fungal colonization of host plants [9] . In addition , OA plays a subtle role in the interaction between S . sclerotiorum and its hosts . For example , OA can suppress the oxidative burst of the host plant [10] and suppress host defenses by manipulating the host redox environment [11] . It also induces apoptotic cell death [12] and plays a crucial role in the control of the interplay of host cell apoptosis and autophagy during infection [13] . Necrotrophic fungi have long been considered as host killers . Previous studies have shown that host-specific necrotrophic fungal pathogens may utilize plant resistance signaling pathways to subvert PCD and enable pathogen growth [14 , 15] . To date , many interactions between host-specific necrotrophic fungal pathogen effector molecules and their host targets have been reported , including the victorin of Cochliobolus victoriae and TRX-h5 as well as LOV1 of Arabidopsis thaliana [16] , the PC toxin of Periconia circinata and Pc locus of sorghum [17] , the Ptr ToxA of Pyrenophora tritici-repentis and Tsn1 of wheat [14] as well as the SnTox1-Snn1 [18] , SnToxA-Tsn1 [19 , 20] , SnTox2-Snn2 [21] , SnTox3-Snn3-B1 [22] , SnTox4-Snn4 [23] , and SnTox3-Snn3-D1 [24] in Stagonospora nodorum-wheat pathosystem . These interactions induce a resistance-like response that confers disease susceptibility in an inverse gene-for-gene manner . However , for host-non-specific fungi with remarkably broad host range such as S . sclerotiorum and Botrytis cinerea , emerging evidence suggests that they have more sophisticated and comprehensive strategies for infecting hosts than previously considered . They can manipulate the antagonistic effects between immune pathways to promote disease development in tomato [25] . Actually , even for these kinds of fungi , there is a transition from a biotrophic to necrotrophic lifestyle and the hemi-biotrophic lifestyle may be more temporally and spatially complex than currently depicted [26] . In addition to CWDEs and OA related pathogenic factors , some potential secreted proteinaceous effectors also play crucial roles in the pathogenesis of host-non-specific necrotrophic fungi . For example , we previously reported that a secreted integrin-like protein SSITL of S . sclerotiorum promotes virulence and directly or indirectly suppresses host resistance during the early stages of infection [27] . Another small secreted protein , Ss-Caf1 , functions as a pathogenicity factor to trigger host cell death during the early stages of S . sclerotiorum infection [28] . Kabbage et al . also identified an effector-like protein in S . sclerotiorum ( SsCm1 ) [13] . The xylanase Xyn11A can induce necrosis independently of the catalytic activity of this enzyme during B . cinerea infection [29] . However , until recently , there has been little experimental evidence for the existence of the interactions between proteinaceous effectors and host targets for typical necrotrophic phytopathogens , such as S . sclerotiorum and B . cinerea . The molecular mechanisms of the interactions between host-non-specific necrotrophic fungal effectors and their host targets is still poorly understood . The identification and characterization of this type of the necrotrophic interactions are difficult because they obviously do not act in the gene-for-gene manner or follow the inverse gene-for-gene scenario . A recent study reported that the S . sclerotiorum genome encodes many predicted secreted proteins that might be involved in the interaction between this fungus and its hosts [30] . Notably , in plant-pathogen interactions , most of effectors are small secreted proteins [31–34] except for some non-proteinaceous toxins and secondary metabolites . However , the biological functions of small secreted proteins from many eukaryotic pathogens remain largely unknown . In the present study , we aim at identifying and characterizing proteinaceous effectors which play crucial roles in the interaction between S . sclerotiorum and its hosts . Digital gene expression profiles ( DGE; Solexa/Illumina ) and bioinformatics approaches were combined to screen for proteinaceous effector candidates in S . sclerotiorum . A cysteine-rich , small , secreted protein SsSSVP1 was experimentally confirmed to interact with a component of plant cytochrome b-c1 complex in mitochondrial respiratory chain , which play a crucial role during S . sclerotiorum-hosts interaction . Our result demonstrated that the necrotrophic fungus S . sclerotiorum also secretes proteinaceous effectors that has targets in plants and the interaction between these effectors and their targets may seriously disturb the physiological processes of its hosts .
In our previous study , the DGE based on deep sequencing technology was used to illuminate the wide range of transcriptional responses associated with six different developmental stages of a virulent wild-type strain , Ep-1PNA367 [35] . In this study , the DGE data was used to identify the differentially expressed genes encoding putative secreted proteins during the vegetative growth stage on PDA and the infection stage on A . thaliana leaves . There were 314 genes encoding predicted secreted proteins that were identified to be significantly up-regulated during infection ( S1 Table ) . We focused our study on those genes which encode cysteine-rich small proteins . RNAi technique was used to study the biological functions of S . sclerotiorum genes because of the multinucleated cells of this fungus . Our results showed that silencing SS1G_02068 significantly reduced the virulence of S . sclerotiorum and SS1G_02068 ( GenBank accession: XM_001597822 ) could induce significant plant cell death when constitutively expressed in host cells . Thus , we named this protein ‘‘SsSSVP1” , as this is the first report that a small secreted virulence-related protein in S . sclerotiorum that has a target in plant cells . SsSSVP1 is a protein without any known domains which may be specific to Sclerotinia and Botryotinia , as the homologs of SsSSVP1 have only been identified in Sclerotinia and Botryotinia in the non-redundant protein sequence database at NCBI to date . SsSSVP1 contains 163 amino acid residues including eight cysteine residues , which account for over 4% ( Fig 1A ) . Multiple sequence alignment indicated that all the cysteine residues in SsSSVP1 are well conserved in its homologues ( Fig 1B ) , indicating these cysteine residues may play an important role in the structure and function of SsSSVP1 . Bioinformatics analysis revealed that SsSSVP1 has a predicted N-terminal signal peptide ( SP , 1–17 aa ) , suggesting that it may be a secreted protein ( Fig 1A ) . To test this hypothesis , the FLAG-tagged SsSSVP1 engineered strains were constructed and inoculated in liquid CM medium for shake culture . Western blot result showed that SsSSVP1-FLAG could be detected in the liquid culture medium ( Fig 1C ) , indicating SsSSVP1 is indeed a secreted protein . To characterize the influence of SsSSVP1 over host cells after being secreted , considering a SP is cut off when a secreted protein is secreted from hyphae into plant cells , SsSSVP1∆SP without its SP was constitutively expressed in Nicotiana benthamiana using Agrobacterium tumefaciens-mediated transformation method . Agrobacterium strains carrying the pTRV2-SsSSVP1∆SP virus vector and the pTRV1 vector , the latter of which facilitates the movement of the recombinant virus , were mixed and co-infiltrated into N . benthamiana leaves . Our result showed that SsSSVP1∆SP could induce significant cell death in leaves , stems and the whole plant ( Fig 2A ) . However , the GFP alone for control did not induce plant cell death , suggesting that plant cell death was specifically induced by SsSSVP1∆SP ( Fig 2A ) . This result indicates SsSSVP1∆SP is toxic to plant cells . A previous report in our lab showed that a small , secreted protein , Ss-Caf1 of S . sclerotiorum without its SP could induce significant plant cell death , however , full Ss-Caf1 with its SP could not induce plant cell death [28] , suggesting that plant cells can recognize SPs from fungi and direct the secretion of fungal proteins expressed in plant cells . Interestingly , we found that full SsSSVP1 with its SP still could induce plant cell death similar to SsSSVP1∆SP ( Fig 3A and 3B ) . So , we postulated that SsSSVP1 could be internalized by plant cells in the absence of a pathogen . If this hypothesis is true , we should still be able to detect SsSSVP1 in plant cells after its secretion . To test this hypothesis , we first examined the subcellular localization of SsSSVP1∆SP in host plant cells . The pTRV2-SsSSVP1∆SP-GFP virus vector was constructed and transformed into an Agrobacterium strain to conduct infiltration assay on tobacco leaves . Confocal images showed that SsSSVP1∆SP mainly distributed throughout the cytoplasm , particularly concentrated at the periphery of cell membrane ( Fig 2B ) . In addition , SsSSVP1∆SP occasionally localized in nuclei under unknown conditions , and sometimes it scattered in cytoplasm in a particle-like form ( S1A Fig ) . Afterwards , we examined the subcellular localization of SsSSVP1 with its SP and SP-GFP ( used for control ) in tobacco leaf cells using the same protein expression system . Results showed that both SsSSVP1-GFP and SP-GFP localized in endoplasmic reticulum ( ER ) -like structure ( S2 Fig ) , however , only SsSSVP1-GFP could be observed to localize in cytoplasmic compartments in a particle-like form , no particle-like form of SP-GFP was observed in cytoplasm , indicating the specificity of the fluorescence signal ( Fig 3C ) . These results indicated the SsSSVP1 could be secreted by plant cells and had plant cell re-entry activity which may result from the internalization of SsSSVP1 . In order to further confirm that SsSSVP1 can be internalized into plant cells independently , nuclear targeting assay was used to facilitate visualization of the translocation of SsSSVP1 according to Khang et al . [4] . A small nuclear localization signal ( NLS ) from simian virus large T-antigen [36] was added at the C terminus of the SsSSVP1-mCherry fusion ( SsSSVP1-mCherry-NLS ) and SP-mCherry fusion ( SP-mCherry-NLS , used for control ) . It is difficult to obtain pure transgenic lines because of the multi-nucleated trait of S . sclerotiorum and the hyper-virulence of S . sclerotiorum is not conducive to observe effector translocation , the constructs described above were transformed into B . cinerea ( which is phylogenetically close to S . sclerotiorum ) to more easily visualize faint fluorescence . The result showed that the SP-mCherry-NLS fluorescence was only observed in the nuclei of infected host cells but not the neighboring host cells , however , the SsSSVP1-mCherry-NLS fluorescence was observed in the nuclei of infected host cells and intact surrounding host cells ( Fig 4 ) . All these intact surrounding host cells were checked in different layers using z-axis scanning of a confocal laser microscope to ensure there were no hyphae in these cells ( S3 Fig ) . These results further indicated SsSSVP1 can be internalized into plant cells independently and move from cell to cell like effectors in other hemibiotrophic fungi [4] . Quantitative reverse transcription PCR ( qRT-PCR ) analysis showed that when pure actively growing hyphal fragments of S . sclerotiorum without culture medium were inoculated onto the leaves of A . thaliana ( Col-0 ) , the transcript levels of SsSSVP1 rapidly increased by more than 50-fold at 3 hours post inoculation ( hpi ) and then gradually increased during the later infection stages ( 6–12 hpi , Fig 5 ) . This result is consistent with the DGE data and suggests that SsSSVP1 may be involved in infection of S . sclerotiorum . In order to explore the roles of SsSSVP1 in virulence of S . sclerotiorum , RNAi technology was used because of the multi-nucleated cells . QRT-PCR was used to examine the transcript accumulation in SsSSVP1-silenced transformants . Three transformants ( SsSSVP1-136 , SsSSVP1-37 and SsSSVP1-70 ) showing dramatically reduced SsSSVP1 expression and one transformant ( SsSSVP1-2 ) with a slightly reduced SsSSVP1 expression ( Fig 6C ) were selected for further study . The colony morphology , virulence and growth rate of these transformants were compared to the wild-type strain Ep-1PNA367 ( Fig 6A , 6B , 6D and 6E ) . The virulence of SsSSVP1-silenced mutants was significantly reduced , and only small lesions were developed on the detached Brassica napus leaves at 2 days post inoculation ( dpi ) . For example , on average of three independent experiments , lesions induced by SsSSVP1-70 were approximately 0 . 9 cm in diameter , while lesions induced by the wild-type strain were approximately 2 . 6 cm in diameter . Furthermore , the decreases in virulence were positively correlated with the silencing efficiency ( Fig 6C and 6D ) , indicating the virulence reduction of the silenced transformants was caused by the silencing of SsSSVP1 . In vivo inoculation assay showed the virulence of SsSSVP1-silenced mutants was also dramatically reduced on A . thaliana leaves compared to that of the wild-type strain ( S4A and S4B Fig ) , indicating the virulence reduction of SsSSVP1-silenced mutants is not host-specific . Although the growth rate of SsSSVP1-silenced transformants was slightly reduced compared to that of wild-type strain ( Fig 6E ) , statistical analysis indicated the expression reduction of SsSSVP1 had more effect on virulence than growth rate , which means the virulence reduction is not intimately associated with growth rate . In order to further investigate the biological functions of SsSSVP1 , S . sclerotiorum transformants over-expressing SsSSVP1-FLAG were used to perform virulence assay . QRT-PCR results showed that increase in the expression of SsSSVP1 varied in different transformants ( S5C Fig ) . Western blot analysis showed that the SsSSVP1-FLAG could be detected in total protein extracts from the mycelia of these over-expression transformants , of which the OESsSSVP1-3 was used as an example ( S5D Fig ) . However , there was no obvious difference between colonial morphology , virulence and growth rate of the over-expression transformants and the wild-type strain ( S5A , S5B , S5E and S5F Fig ) . The rapid increase of SsSSVP1 expression level in the wild-type strain during infection could possibly explain the lack of difference in virulence between SsSSVP1-overexpression strains and the wild-type strain . To further understand how SsSSVP1 affects the virulence of S . sclerotiorum , yeast two-hybrid ( Y2H ) technique was used to screen an A . thaliana cDNA library to identify the targets that interact with SsSSVP1∆SP in plants . Our Y2H assay showed that SsSSVP1∆SP interacted with itself ( Fig 7A ) , indicating SsSSVP1∆SP may function in plant cells in the form of homo-dimer . Meanwhile , our results demonstrated that SsSSVP1∆SP could interact with QCR8 ( AT3G10860 ) , the subunit 8 of cytochrome b-c1 complex which is the component of mitochondrial respiratory chain ( Fig 7A ) . The QCR8 gene is well conserved in plants , and our Y2H assay further showed that SsSSVP1∆SP could interact with all the homologs of QCR8 in A . thaliana and N . benthamiana ( Fig 7A and S6 Fig ) , indicating the possible universal existence of this necrotrophic interaction during the infection of S . sclerotiorum on many hosts . To determine if SsSSVP1∆SP interacts with QCR8 in plant tissues , we co-expressed the GFP-tagged SsSSVP1∆SP and 3×FLAG-tagged QCR8 in N . benthamiana leaves by A . tumefaciens infiltration method , our co-immunoprecipitation ( co-IP ) assay also supported that SsSSVP1∆SP interacted with QCR8 ( Fig 7B ) . Furthermore , this result was further confirmed in planta using the bimolecular fluorescence complementation ( BiFC ) technique . SsSSVP1∆SP-nYFP ( N-terminal yellow fluorescent protein fragment ) and QCR8-cYFP ( C-terminal yellow fluorescent protein fragment ) were transiently co-expressed in N . benthamiana leaves . Yellow fluorescence was detected in cytoplasm , especially at the periphery of cell membrane ( Fig 7C ) , suggesting that SsSSVP1∆SP interacts with QCR8 in plant cell cytoplasm . As described above , many effectors are cysteine-rich proteins . Additionally , the eight cysteine residues are well conserved in the homologs of SsSSVP1 . In order to examine if these cysteine residues play crucial roles in the function of SsSSVP1 , single site-directed mutagenesis of the eight cysteine residues was conducted in SsSSVP1∆SP . Our results showed that all the single-point mutations had little effects on the dimer formation of SsSSVP1∆SP ( Fig 8A ) , and the interaction between SsSSVP1∆SP and QCR8 ( Fig 8B ) . In addition , the expression of SsSSVP1∆SP with all single-point mutations still could induce plant cell death ( Fig 8C ) . However , our Y2H and single site-directed mutagenesis combined assays showed that SsSSVP1∆SP-C38A could not interact with SsSSVP1∆SP-C44A ( S7 Fig ) . Furthermore , double-point mutation at residues 38 ( C to A ) and 44 ( C to A ) made SsSSVP1∆SP lose the ability to interact with itself and with QCR8 ( Fig 8A and 8B ) . These results further indicated the specificity of homo-dimer formation of SsSSVP1∆SP and the interaction between SsSSVP1∆SP and QCR8 . Meanwhile , SsSSVP1∆SP-C38A-C44A also could not induce plant cell death any more ( Fig 8C ) , although it could express well and was stability in plant ( Fig 8D ) . These results indicated C38 and C44 play a crucial role in maintaining the structure and biological functions of SsSSVP1 . Our BiFC result showed that SsSSVP1∆SP interacted with QCR8 in cytoplasm , especially at the periphery of cell membrane . However , QCR8 is one subunit of cytochrome b-c1 complex , which localizes in mitochondria [37] , so we hypothesize the interaction between SsSSVP1∆SP and QCR8 might change the native subcellular localization of QCR8 , and SsSSVP1∆SP could hijack QCR8 to cytoplasm . To test this hypothesis , SsSSVP1∆SP-mCherry and QCR8-GFP were co-expressed in N . benthamiana leaves using Agrobacterium infiltration method for the observation of their co-localization . As expected , QCR8 alone localized in mitochondria ( Fig 9A ) , because it co-localized with the mitochondria-mcherry marker [38] ( Fig 9B ) . However , SsSSVP1∆SP and QCR8 co-localized in cytoplasm ( Fig 9B ) , which is in accordance with the BiFC results . Additionally , QCR8 still localized in mitochondria when it was co-expressed with the double site-directed mutant SsSSVP1∆SP-C38A-C44A losing the ability to interact with QCR8 ( Fig 9B ) , indicating the specificity of fluorescence distribution of the SsSSVP1∆SP and QCR8 co-localization . Occasionally , the co-localization of SsSSVP1∆SP and QCR8 in nuclei or in cytoplasmic compartments of plant cells could also be observed ( S1B Fig ) . QCR8 is encoded by nuclear genome and translated in cytoplasm . Our results indicated that the interaction between SsSSVP1∆SP and QCR8 could disturb the native localization of QCR8 , and SsSSVP1∆SP might hijack QCR8 to cytoplasm before QCR8 was translocated into mitochondria . Our co-localization and BiFC assays showed the SsSSVP1∆SP-QCR8 interaction disturbed the subcellular localization of QCR8 , which might disable the biological functions of QCR8 . To test this hypothesis , a tobacco rattle virus ( TRV ) -based virus induced gene silencing ( VIGS ) system [39] was used to knock-down the three homologs of QCR8 encoding genes in N . benthamiana . The endogenous tobacco phytoene desaturase gene ( PDS ) was used to examine the effectiveness of the TRV-VIGS system ( S8 Fig ) . QRT-PCR results showed that the transcript abundance of the three QCR8 encoding genes was reduced in both upper leaves and middle leaves of the silenced lines in varying degrees , compared to that in control lines ( Fig 10A ) . Targeted silencing of QCR8 resulted in stunted development of stem apex which caused most of the QCR8-silenced plants to exhibit dwarf phenotype ( Fig 10B ) . More importantly , approximately 78 . 9% ( 45/57 ) of the silenced lines showed plant cell death phenotype on the leaves with and without infiltration sites . No control lines exhibited these phenotypes . Together with the data that the double site-directed mutant SsSSVP1∆SP-C38A-C44A cannot interact with QCR8 and also lost the capability to induce plant cell death , these results indicated the plant cell death induced by SsSSVP1∆SP might be caused by the SsSSVP1∆SP-QCR8 interaction , which disturbed the subcellular localization of QCR8 and hence made the QCR8 lose its biological function .
S . sclerotiorum is a typical necrotrophic fungal pathogen that produces oxalic acid and CWDEs to kill plant cells and subsequently feeds on the dead tissues . However , increasing evidence suggests the pathogenesis of S . sclerotiorum is more complex than originally considered . In this study , a Sclerotinia- and Botryotinia-specific , small , secreted protein SsSSVP1 was identified , and its biological functions in the interactions between S . sclerotiorum and its hosts were explored . SsSSVP1 is a cysteine-rich protein which is predicted to form disulfide bonds intramolecularly . The cysteine residues are essential for the formation of disulfide bonds , which may facilitate the formation of stable homodimers , heterodimers , homopolymers , or heteropolymers , suggesting the important roles for these residues in protein folding and in maintaining the structural stability of some secreted proteins [40 , 41] , particularly those are secreted into the oxidizing environment of extracellular medium [40] . The single site-directed mutagenesis of the eight cysteine residues in SsSSVP1∆SP had little effects on its structure and function because the mutants still can form homo-dimer , interact with QCR8 and induce plant cell death . Although it seemed that the degree of cell death induced by different single-point mutants of SsSSVP1∆SP varied at the early stage ( 10 dpi ) after A . tumefaciens infiltration , the plant cells eventually died at the late stage ( 30 dpi ) . The difference of the degree of cell death at the early stage may be due to different plant growth status and different transmission speed of the virus . However , the double-point mutant SsSSVP1∆SP-C38A-C44A could not induce plant cell death no matter at the early stage or at the late stage after A . tumefaciens infiltration . Meanwhile , SsSSVP1∆SP-C38A-C44A also could not form homo-dimer or interact with QCR8 . The dimer formation may be very important for SsSSVP1 when it is exposed to plant intercellular space during infection . This molecular mechanism has significant meaning in many cysteine-rich proteinase inhibitors , where even cleavage of the reactive site peptide bond does not change its overall conformation and such “modified” inhibitor still possesses antiproteinase activity [42] . QCR8 does not have any cysteine residues , indicating the interaction between SsSSVP1∆SP and QCR8 is not maintained by intermolecular disulfide bonds but by their respective tertiary structure . In conclusion , these results indicated there might be at least two disulfide bonds maintaining the tertiary structure of SsSSVP1 intramolecularly , affecting the stability and rigidity of this small secreted protein . Our results suggested C38 and C44 were essential to maintain the structure and function of SsSSVP1∆SP , however , we do not rule out the case that the other cysteine residues also play important roles . A primary role of effectors is to inhibit host defense mechanisms [43–45] . However , the roles of effectors in biotrophic and necrotrophic fungi might be different , as the former require live host tissues , while the latter prefer dead plant tissues . Most effectors in biotrophic fungi suppress programmed cell death [46] while many effectors in necrotrophic fungi induce plant cell death . Our results also indicated that SsSSVP1∆SP induced significant plant cell death . In different repeated tests , SsSSVP1∆SP-GFP always mainly localized in the plant cytoplasm , occasionally localized in cytoplasmic compartments in a particle-like form or in nuclei in different areas even in the same infiltrated tobacco leaf . The difference in the fluorescence distribution in the plant cells expressing SsSSVP1∆SP might be due to the fact that the cells are at different stages of apoptosis . The mechanisms of the translocation of RXLR effectors in oomycetes or RXLR-like variants in fungi into plant cells has been documented and discussed [47–57] , however , the mechanisms underlying the delivery in host cells of fungal effectors without RXLR motif are poorly understood , although the phenomena of the internalization and cell-to-cell movement of some fungal effectors were observed previously [4 , 58] . Previous research showed Ptr ToxA produced by P . tritici-repentis may be internalized via receptor-mediated endocytosis ( RME ) by sensitive wheat mesophyll cells and the endocytic vesicle-like structure was observed near plasma membrane [58] . Ptr ToxA is compartmentalized after internalization and forms particle-like structures in plant cells [58] . Interestingly , similar situations were observed in SsSSVP1 ( Fig 3C ) . Hence , we infer that SsSSVP1 and Ptr ToxA have similar cell entry mechanism . In the case of Ptr ToxA , one motif Arg-Gly-Asp ( RGD ) was predicted to be involved in its interaction with a putative integrin-like receptor in the host [59 , 60] . However , neither an RGD-like motif nor an RXLR-like motif was found in SsSSVP1 . The exact molecular mechanism of SsSSVP1 crossing the plant plasma membrane from the apoplastic space to the interior of plant cells in the absence of a pathogen should be explored in future . Additionally , the cell-to-cell movement of SsSSVP1 is likely the result of the internalization and translocation of SsSSVP1 in the host apoplastic space , because the fluorescent signal could be clearly detected in the apoplastic space of the surrounding cells of the invaded host cells ( Fig 4 ) . QCR8 is a subunit of the cytochrome b-c1 complex comprising 10 different polypeptide subunits in plants [61] . The cytochrome b-c1 complex is the center component of the mitochondrial respiratory chain , coupling the transfer of electrons from ubihydroquinone to cytochrome c with the generation of a proton gradient across the mitochondrial membrane [37] . We found that SsSSVP1∆SP could hijack QCR8 into the cytoplasm of plant cells and disturb the native localization of QCR8 in mitochondria . This character of SsSSVP1 is similar to that of a rice stripe virus ( RSV ) specific protein RSV SP , which hijacks host PsbP into cytoplasm from chloroplast [62] . Although we do not know that if the deletion of QCR8 is lethal to plants , our results showed silencing of QCR8 caused obvious plant cell death . This phenomenon indicated a link between the SsSSVP1∆SP-QCR8 interaction and the biological function loss of QCR8 . Alteration of QCR8 native subcellular localization or lack of QCR8 may eventually affect the energy metabolism of plant cells , because knock-down of QCR8 significantly affected plant growth and development . Obviously , the interaction model of SsSSVP1∆SP and QCR8 is very different from that of classic effectors and R genes . The ‘gene for gene’ and reverse ‘gene for gene’ model might not apply to this typical necrotrophic fungi-host interaction system , as there are almost no resistant hosts to these canonical necrotrophic pathogens . On the other hand , the components of the cytochrome b-c1 complex are highly conserved in almost all plant cells . Our study provides an intriguing example that the necrotrophic pathogen secretes a small protein which might attack the well conserved component of mitochondrial respiratory chain in plant cells . This hypothesis is also consistent with the broad host range of S . sclerotiorum . In summary , we screened for small , secreted proteins that were significantly up-regulated during infection and identified a "toxin-like" and "effector-like" protein SsSSVP1 in S . sclerotiorum . SsSSVP1 is essential for the full virulence of S . sclerotiorum . SsSSVP1∆SP interacts with QCR8 and hijacks QCR8 into the cytoplasm in plant cells . The SsSSVP1∆SP-QCR8 interaction disturbs the location of QCR8 and hence might interfere with the biological functions of QCR8 . The functional loss of QCR8 may seriously affect the plant energy metabolism and caused significant cell death . Two cysteine residues at 38 and 44 of SsSSVP1 are crucial for its structure and functions . These findings further enhance our understanding of the pathogenic mechanism of S . sclerotiorum , highlighting the necessity for large-scale screening and function analyses of the effector candidates in typical necrotrophic fungi with broad host ranges .
The virulent S . sclerotiorum wild-type strain Ep-1PNA367 [63] and B . cinerea wild-type strain B05 . 10 were used in this study . Fungal cultures were grown on potato dextrose agar ( PDA , Difco , Detroit , MI , USA ) or inoculated in CM liquid medium at 20°C . S . sclerotiorum and B . cinerea transformants were cultured on PDA amended with 80 μg/ml hygromycin B ( Calbiochem , San Diego , CA ) to stabilize the transformants . Escherichia coli strain JM109 and DH5α was used to propagate all plasmids , and A . tumefaciens strains EHA105 and GV3101 were used for the transformation of fungi and plants , respectively . Seedlings from A . thaliana ( ecotype Columbia-0 ) and N . benthamiana were grown in the greenhouse at 20 ± 2°C under a 12 h light/dark cycle with 70% relative humidity . The canola cultivar used for virulence assay was zhongyou 821 [64] , which is slightly resistant to S . sclerotiorum . The Agrobacterium-mediated transformation method was used to transform S . sclerotiorum as previously described [65] , with a modification related to Agrobacterium cultivation: the A . tumefaciens cells were not diluted in minimal medium and directly cultured in induction medium for co-cultivation . The Agrobacterium-mediated transformation method was performed to transform N . benthamiana via infiltration according to published protocols [66] . The publicly available genomic sequence database of S . sclerotiorum 1980 UF-70 ( http://www . broadinstitute . org/annotation/genome/sclerotinia_sclerotiorum/MultiDownloads . html ) was used to characterize all S . sclerotiorum genes examined in this study . SignalP was used to identify secreted proteins and their SPs [67] . BlastP analysis was done on the website of NCBI ( http://www . ncbi . nlm . nih . gov/ ) . The amino acid sequences were aligned using COBALT [68] and viewed and edited in Jalview [69] . The DGE analysis and the identification of differentially expressed genes were performed according to our previous study [35] . To generate SsSSVP1-FLAG fusion construct ( S9A Fig ) , the promoter PEF-1α was PCR amplified using the primers PEF-1α F/R and subsequently digested with Xho I and Sac I . The PCR products of SsSSVP1 were amplified with the primers SsSSVP1-FLAG F/R and subsequently digested with Sac I and Sma I . These two fragments were sequentially ligated into the pCH vector [65] through the formation of intermediate constructs . Based on our experience in gene silencing in S . sclerotiorum , the silencing efficiency of different RNAi strategies varies from gene to gene . To obtain knockdown transformants with a higher silencing efficiency , two RNAi strategies described by Nguyen [70] and Yu et al . [65] were adopted to construct the S . sclerotiorum RNAi vectors: a 320 bp fragment from SsSSVP1 was amplified with the primers RNAi-SsSSVP1 F/R from the S . sclerotiorum cDNA library and ( i ) directly ligated into the digested pCXDPH vector at the Xcm I ( New England Biolabs , Beverly , MA , USA ) site to produce pRNAi-1 vector ( S9B Fig ) or ( ii ) digested with suitable enzymes and subsequently ligated into pCIT [27] between PtrpC , the intron and TtrpC in the opposite orientation via intermediate vectors . Subsequently , the PtrpC-intron-TtrpC fragment containing the two S . sclerotiorum gene fragments in the opposite orientation was digested with Sac I and Xho I and subsequently ligated into pCH to produce pRNAi-2 vector ( S9C Fig ) . Both of these two different RNAi strategies were used to silence SsSSVP1 and similar results were obtained . The transformants used in this study were produced using pRNAi-2 vector . All the constructs were confirmed through sequencing analysis . The primers are shown in S2 Table . These constructs were then introduced into the A . tumefaciens strain EHA105 through electroporation [71] . For SsSSVP1 constitutive expression in N . benthamiana , the recombinant TRV-based A . tumefaciens binary virus vectors pTRV1 and pTRV2 [39] were used for gene expression and gene silencing in N . benthamiana in this study . To generate the constitutive expression constructs , ( i ) SsSSVP1 with and without the SP-encoding sequences were amplified using the primers pTRV-SsSSVP1 F/R and pTRV-SsSSVP1∆SP F/R , respectively; ( ii ) GFP with and without the SP-encoding sequences were amplified using the primers pTRV-GFP F/R and pTRV-SP-GFP F/R , respectively; ( iii ) SsSSVP1∆SP and GFP-encoding sequence were amplified using the primers SsSSVP1∆SP F/R and GFP F/R , respectively . The PCR products were subsequently digested with the appropriate restriction enzymes , followed by ligation to the intermediate vector pBI121 [72] , and then the PCR product of the SsSSVP1∆SP-GFP fusion was amplified from the recombinant pBI121 using the primers pTRV-SsSSVP1∆SP-GFP F/R; ( iv ) The PCR product of the SsSSVP1-GFP fusion was amplified using the primers pTRV-SsSSVP1-GFP F and GFP R from the pTRV2-SsSSVP1∆SP-GFP constructs . The final fragments from ( i ) to ( iv ) were directly cloned into pTRV2 digested with Xcm I to construct pTRV2-SsSSVP1∆SP , pTRV2-SsSSVP1 , pTRV2-GFP , pTRV2-SP-GFP , pTRV2-SsSSVP1∆SP-GFP and pTRV2-SsSSVP1-GFP vectors , respectively ( S9D–S9I Fig ) . For the co-localization assay , SsSSVP1∆SP-C38A-C44A-mCherry fusion protein encoding sequence was constructed by spliced overlap extension PCR . SsSSVP1∆SP-mCherry fusion protein encoding sequence was amplified using the primers pTRV-SsSSVP1∆SP-mCherry F/R from the construct containing SsSSVP1-mCherry-NLS . QCR8 and GFP encoding sequences were amplified using the primers QCR8-F/R and GFP F/R , respectively , and then cloned into the intermediate vector pBI121 , from which the QCR8-GFP fusion protein encoding sequence was amplified using the primers pTRV-QCR8-GFP F/R . The amplified SsSSVP1∆SP-C38A-C44A-mCherry , SsSSVP1∆SP-mCherry and QCR8-GFP fusion protein encoding sequences were finally cloned into the pTRV2 vector , respectively , using the same method described as above ( S9J and S9K Fig ) . For validating the expression of SsSSVP1∆SP-C38A-C44A and SsSSVP1∆SP in tobacco leaf cells using western blotting analysis , the primers pTRV2-SsSSVP1-3×FLAG F/R were used to amplify SsSSVP1∆SP-C38A-C44A and SsSSVP1∆SP from the constructs containing these two fragments , respectively , before they were cloned into the pTRV2 vector ( S9L Fig ) . Constructs containing these fragments in the correct orientation were PCR screened using the primer pTRV F and corresponding downstream primers respectively . To generate the VIGS-pTRV2-QCR8 silencing constructs ( S9M and S9N Fig ) , partical coding regions of the three QCR8 genes were amplified from the cDNA liabrary of N . benthamiana using the primers RNAi-QCR8-1 F/R , RNAi-QCR8-2 F/R and RNAi-QCR8-3 F/R , respectively , and then digested with EcoR І and BamH І prior to be ligated into VIGS-pTRV2 vector digested with the same pair of restriction enzymes . The pTRV1 construct ( S9O Fig ) from Liu Y et al . [39] was directly used . All the constructs were confirmed through sequencing analysis . The primers are shown in S2 Table . These constructs were then introduced into the A . tumefaciens strain GV3101-pM90 through electroporation [71] . Equal amounts of agrobacterium containing the constructs and pTRV1 were mixed respectively for infiltration performed with N . benthamiana leaves as previously described [28] . To determine whether SsSSVP1 was secreted into the liquid cultures , the positive SsSSVP1-FLAG engineered strains were cultured in liquid CM medium at 20°C for 3 days , with shaking at 200 rpm . The culture broth was filtered with 4 layers of Calbiochem Miracloth and centrifuged at 10 , 000 rpm for 5 min to remove the hyphal fragments . Secreted proteins in the fermentation liquid were precipitated with solid ammonium sulfate ( 100% saturated ) . The precipitated secreted proteins were dissolved in PBS buffer ( 137 mM NaCl , 2 . 7 mM KCl , 10 mM Na2HPO4 , and 2 mM KH2PO4 ) and desalinated through dialysis . The protein extracts in dialysis bag were further condensed using saccharose at 4°C and lyophilized overnight before being dissolved in 0 . 1ml of 4×protein loading buffer for western blot analysis after quantification . To screen the positive SsSSVP1-FLAG engineered strains , total proteins extracted from the mycelia of SsSSVP1-FLAG transformants by cell lysis buffer ( Beyotime , Wuhan , Hubei , China ) were used for immunoprecipitation ( IP ) and western blot analysis . About 5 μl ANTI-FLAG M2 monoclonal antibody ( Sigma , Saint Louis , Missouri , USA ) was added to 1 ml protein extracts and then was incubated at room temperature for 2 hours . Afterwards , protein A+G agarose ( Beyotime , Wuhan , Hubei , China ) was added to the protein extracts and was incubated at room temperature for 1 hour before it was collected by centrifugation and washed for five times by the cell lysis buffer , and then protein loading buffer was added for following western blot analysis . Proteins were separated by SDS-PAGE gel ( 12% ) before they were transferred onto a 0 . 22 μm PVDF membrane ( Millipore ) using a Trans-Blot SD Semi-Dry Electrophoretic Transfer Cell ( Bio-Rad ) . A monoclonal α-Anti FLAG M2 antibody ( Sigma-Aldrich , St . Louis , MO , USA ) and a goat anti-mouse IgG conjugated with alkaline phosphatase ( Sigma-Aldrich , St . Louis , MO , USA ) were used as a primary antibody and a secondary antibody respectively . To validate the secretion of SsSSVP1 , total proteins obtained from the liquid CM medium through protein precipitation , dialysis and condensation described as above were directly used for western blot analysis using the same method without the IP procedure . The secondary antibody used in this experiment was a goat anti-mouse IgG conjugated with horseradish peroxidase ( HRP ) ( Sigma-Aldrich , St . Louis , MO , USA ) . The signals of blots were detected using Pierce ECL Western Blotting Substrate ( Thermo Scientific ) . To generate the SsSSVP1-mCherry-NLS and SP-mCherry-NLS fusion constructs , SsSSVP1 and mCherry were PCR amplified using the primers SsSSVP1 F/R and mCherry F/R respectively . The PCR products were digested with appropriate restriction enzymes and then ligated into the pCXH ( a fungal expression vector constructed by our lab ) through the formation of intermediate constructs . The SP-mCherry-NLS and SsSSVP1-mCherry-NLS fragments with a stop codon were amplified from the pCXH vector with SsSSVP1-mCherry fusion using the primers SP-mCherry-NLS F/R and SsSSVP1-mCherry-NLS F/R before they were finally cloned into pDL2 vector [73] , respectively , by the yeast gap repair approach [74] . The SP-mCherry-NLS and SsSSVP1-mCherry-NLS fusion constructs were transformed into the B . cinerea B05 . 10 strain using the PEG-mediated transformation method [75] . Tissues from onion bulb lower epidermal cells infected with the B . cinerea engineered strains expressing SP-mCherry-NLS and SsSSVP1-mCherry-NLS fusion proteins were examined at 48 hpi , respectively . To observe fluorescence , the tobacco tissues were harvested from infiltrated tobacco leaves at 3 dpi and the onion tissues were harvested from inoculated lower epidermis at 36 hpi , and then directly imaged under a confocal laser scanning microscope ( OLYMPUS microscope FV1000 ) . The 488- , 587 , 514- and 458-nm absorption laser lines with corresponding appropriate specific emission filter sets were used when images of GFP , mCherry , YFP and chloroplast autofluorescence were recorded , respectively . Genomic DNA was isolated as previously described [76] and used for the validation of T-DNA insertion in the transformants through PCR with the primers Hyg F/R ( S2 Table ) . To evaluate the expression levels of SsSSVP1 in different transformants , the transformants were inoculated on cellophane placed on PDA plates before pure mycelia of the transformants were collected for RNA isolation . To evaluate the expression levels of SsSSVP1 during different infection stages of the wild-type strain , pure fresh mycelia without culture medium were inoculated on A . thaliana leaves . The inoculated leaves were collected at 0 , 3 , 6 , 9 , 12 hpi and frozen in liquid nitrogen and ground to a powder for RNA extraction . To evaluate the expression levels of QCR8 , the upper and middle N . benthamiana leaves were sampled one month after A . tumefaciens infiltration . Total RNA was extracted using the TRIZOL Reagent ( Huashun Bioengineering Co , Shanghai , China ) according to the manufacturer’s instructions and treated with DNase I ( RNase free , Takara , Dalian , China ) . Synthesis of first-strand cDNA and qRT-PCR were conducted according to Zhu et al . [27] . The expression levels of SsSSVP1 were examined through qRT-PCR using the primers QPCR-SsSSVP1 F/R . The expression levels of the S . sclerotiorum β-tubulin gene ( SS1G_04652 ) [77] were used to normalize the expression of SsSSVP1 in each corresponding qRT-PCR sample using the primers Tub F/R . The expression levels of the three genes encoding the homologs of QCR8 in N . benthamiana were examined through qRT-PCR using the primers QPCR-QCR8-1 F/R , QPCR-QCR8-2 F/R and QPCR-QCR8-3 F/R , respectively . The expression levels of the N . benthamiana actin gene ( AY179605 . 1 ) [27] were used to normalize the expression of QCR8 in each corresponding qRT-PCR sample using the primers Actin F/R . The qRT-PCR assay was repeated at least twice for each gene , with three replicates . The primers used for qRT-PCR are shown in S2 Table . The detached B . napus ( zhongyou 821 ) leaves under the same physiological conditions were used for the virulence assay of S . sclerotiorum wild-type strain and transformants . To evaluate virulence , at least six individual detached B . napus leaves or in vivo A . thaliana leaves were inoculated with a single 0 . 5-cm diameter mycelium-colonized agar plug obtained from the expanding margins of PDA-cultured colonies . Inoculated leaves were maintained at 100% relative humidity at 20°C for 48 h ( for B . napus leaves ) or 36 h ( for A . thaliana leaves ) . Disease severity was measured using the average lesion diameter . To assay growth rates , the wild-type strain and the transformants were cultivated on PDA at 20°C for 3 days . Mycelial agar discs were collected from the active colony edge and inoculated in the center of the PDA Petri dish at 20°C before the hyphal growth was examined . Each experiment was performed independently at least three times . Y2H analysis was performed using a GAL4-based Y2H system ( Matchmaker Gold Systems; Clontech , Palo Alto , CA ) . The construction of Y2H library , autoactivation and toxicity test and the screening of Y2H library were performed according to the manufacturer’s instructions . The primers used to create the corresponding constructs are listed in S2 Table . The bait and prey plasmids were co-transformed into a yeast strain Y2HGold ( Clontech , Palo Alto , CA ) . Yeast transformation was performed according to the manufacturer’s instructions . The transformants were assayed for growth on synthetic dropout ( SD ) /-Trp-Leu plates , and cultured on liquid synthetic SD/-Trp-Leu medium for 36 hours before being collected by centrifugation . The concentration of collected yeast cells were adjusted to 106 ( cells/ml ) using sterile water , and then 5 μl yeast suspension was assayed for growth on SD/-Trp-Leu-His-Ade plates containing the X-α-gal and Aureobasidin A ( AbA ) . For Co-IP assay , to construct pCNF3-SsSSVP1∆SP-GFP and pCNF3-QCR8-3×FLAG ( S9P and S9Q Fig ) , the full-length of the SsSSVP1∆SP-GFP and QCR8-3×FLAG were amplified using the specific primers COIP-SsSSVP1∆SP-GFP F/R and COIP-QCR8-3×FLAG F/R ( S2 Table ) , respectively , and then cloned into the pCNF3 vector ( a plant expression vector constructed by our lab ) . A . tumefaciens containing the pCNF3-SsSSVP1∆SP-GFP and pCNF3-QCR8-3×FLAG constructs were co-infiltrated into N . benthamiana leaves using the same method described as above . Total protein was isolated by homogenizing tissues with RIPA lysis buffer ( Beyotime , Wuhan , Hubei , China ) with a modification [50 mM Tris pH7 . 4 , 150 mM NaCl , 1% NP-40 , 0 . 25% sodium deoxycholate , 1 mM sodium orthovanadate , 1 mM sodium fluoride , 1 mM EDTA , 0 . 5 μg/ml leupeptin , 1 mM phenylmethanesulfonyl fluoride ( PMSF ) and 1% proteinase inhibitor cocktail ( Sigma , Saint Louis , Missouri , USA ) ] . Approximately 3 g plant tissues were lysed by 10 ml RIPA lysis buffer . The total protein was then centrifuged at 13000 rpm for 1 h to remove residues . For anti-GFP IP , approximately 2 ml supernatant RIPA lysis buffer containing the total protein was incubated with 10 μl of anti-GFP monoclonal antibody ( sigma , Saint Louis , Missouri , USA ) and 50 μl of protein G plus-Agarose ( Santa Cruz Biotechnology , Inc . Dallas , Texas , USA ) for 8 h at 4°C on a rotary shaker . The beads were then collected and washed five times with RIPA lysis buffer . The bound protein was eluted from beads by boiling in protein sample buffer . One third of the immunoprecipitated protein was subjected to immunoblot analyses with anti-FALG monoclonal antibody ( sigma , Saint Louis , Missouri , USA ) . Approximately 25 μl of RIPA lysis buffer containing the total protein was loaded as input control . BiFC assay was used to study the interaction of SsSSVP1∆SP and QCR8 based on a previously described method [78] . To construct the pBISPYNE-SsSSVP1∆SP and pBISPYCE-QCR8 vectors ( S9R and S9S Fig ) , respectively , the full-length cDNAs of the SsSSVP1∆SP and QCR8 were amplified using the specific primers BiFC-SsSSVP1∆SP F/R and BiFC-QCR8 F/R ( S2 Table ) , respectively , recombined with the N- and C-termini of YFP , respectively , and subsequently cloned into the pBI121 vector through intermediate vectors pUC-SPYNE and pUC-SPYCE . The constructs were verified by sequencing . All plasmids were transformed into N . benthamiana leaves via the A . tumefaciens strain GV3101-pM90 . The eight cysteine residues of SsSSVP1∆SP were substituted by alanine respectively according to the manual of QuikChange II XL Site-Directed Mutagenesis Kit ( Stratagene ) . The double-point mutant SsSSVP1∆SP-C38A-C44A was constructed by fusion PCR using the primers MutC38A-C44A-1 F/R and MutC38A-C44A-2 F/R . The coding sequences of SsSSVP1∆SP mutants were cloned into the pGBKT7 and pGADT7 vector respectively for Y2H analysis and cloned into the pTRV2 vector respectively for functional analysis . Mutations were confirmed by sequencing analysis . The primers used in this experiment were listed in S2 Table . | To resist biotrophic and hemibiotrophic phytopathogens , plants utilize an innate immune system , mediated through nucleotide binding ( NB ) -leucine rich repeat ( LRR ) proteins , to respond to effectors , most of which are small secreted proteins . Hypersensitive responses ( HRs ) resulting from this type of interaction can effectively restrain the expansion of biotrophic or hemibiotrophic phytopathogens in plant tissues . However , it is not effective against typical necrotrophs with remarkably broad host range , such as S . sclerotiorum , because these necrotrophs have long been thought to just simply kill hosts and complete their life cycles using nutrients derived mostly from dead plant tissues . This type of phytopathogen-plant interaction obviously does not comply with the gene-for-gene or inversed gene-for-gene relationship . The results in present study show that SsSSVP1 of S . sclerotiorum functions as an effector in pathogen-plant interactions . SsSSVP1 is dramatically induced during infection , and required for the full virulence of S . sclerotiorum . SsSSVP1 can be internalized by plant cells after being secreted from fungal cells in the absence of a pathogen during infection . Furthermore , SsSSVP1∆SP interacts with QCR8 , a subunit of cytochrome b-c1 complex , and disturbs the localization of QCR8 in mitochondria , which may disable its biological function . The nonfunctionalization of QCR8 caused significant plant cell death . Hence , SsSSVP1 acts as an effector to manipulate the host cell physiology to facilitate the colonization of S . sclerotiorum . Obviously , this is a completely different interaction model from the gene-for-gene or inversed gene-for-gene paradigm . These findings suggest that the pathogenesis of S . sclerotiorum is more subtle and complex than previously appreciated and highlight the significance to investigate the interaction models between the host-non-specific necrotrophs and their hosts . | [
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] | 2016 | A Small Secreted Virulence-Related Protein Is Essential for the Necrotrophic Interactions of Sclerotinia sclerotiorum with Its Host Plants |
The small GTPase RAB-5/Rab5 is a master regulator of the early endosome , required for a myriad of coordinated activities , including the degradation and recycling of internalized cargo . Here we focused on the recycling function of the early endosome and the regulation of RAB-5 by GAP protein TBC-2 in the basolateral C . elegans intestine . We demonstrate that downstream basolateral recycling regulators , GTPase RAB-10/Rab10 and BAR domain protein AMPH-1/Amphiphysin , bind to TBC-2 and help to recruit it to endosomes . In the absence of RAB-10 or AMPH-1 binding to TBC-2 , RAB-5 membrane association is abnormally high and recycling cargo is trapped in early endosomes . Furthermore , the loss of TBC-2 or AMPH-1 leads to abnormally high spatial overlap of RAB-5 and RAB-10 . Taken together our results indicate that RAB-10 and AMPH-1 mediated down-regulation of RAB-5 is an important step in recycling , required for cargo exit from early endosomes and regulation of early endosome–recycling endosome interactions .
Endocytic recycling , the return of proteins and lipids from endosomes to the plasma membrane , plays a key role in many essential cellular processes including nutrient uptake , cell migration , cytokinesis , synaptic plasticity , immune response , and growth factor receptor modulation [1] . In polarized epithelial cells an additional layer of complexity in the endocytic pathway contributes to formation and/or maintenance of the specialized apical and basolateral domains [2 , 3] . Both the apical and basolateral membranes deliver cargo to early endosomes , often referred to as apical early endosomes and basolateral early endosomes [3–5] . Basolaterally derived and apically derived cargo can reach common recycling endosomes , from which cargo is sorted for delivery to the basolateral plasma membrane or to apical recycling endosomes [3–5] . The apical recycling endosomes are thought to send their cargo to the apical plasma membrane . Small GTPases of the Rab superfamily play key roles in membrane transport , with at least one Rab protein regulating each transport step . In polarized epithelial cells Rab11 is primarily associated with the apical recycling endosomes and is thought to function in the transport of cargo from the apical recycling endosomes to the plasma membrane [3 , 6 , 7] . Rab8 has also been implicated in apical recycling in the intestinal epithelia of mice and worms [8] . Our attention was first brought to bear on the basolateral recycling pathway of C . elegans intestinal epithelia because of the accumulation of grossly enlarged basolateral vesicles in mutants lacking the recycling regulator RME-1/EHD [9] . In the case of rme-1 mutants , these enlarged vesicles accumulated recycling cargo and were positive for the endosomal recycling regulator ARF-6 , but lacked early endosome marker RAB-5 , suggesting that RME-1 functions at a late recycling step [9–11] . Pulse-chase data in mammalian cells showed that loss of mRme-1/EHD1 likewise resulted in a block in recycling endosome to plasma membrane transport [12 , 13] . Similarly rab-10 mutants first caught our attention because they displayed enlarged basolateral vesicles in the C . elegans intestine that accumulated recycling cargo [10] . However , in this case the enlarged endosomes were positive for RAB-5 , indicating an earlier block in basolateral recycling , at the level of early endosome to recycling endosome transport [10] . We extended this work , identifying two RAB-10 effectors that function with RAB-10 in basolateral recycling , EHBP-1 and CNT-1 [11 , 14] . EHBP-1 strongly labeled the tubular elements of the recycling pathway , was required for strong RAB-10 endosomal recruitment , and may link endosomes to the cytoskeleton [14 , 15] . CNT-1/ACAP is recruited to endosomes by RAB-10 and regulates the activity of ARF-6 , acting as part of a small GTPase regulatory loop [11] . In turn ARF-6 regulates PI5-kinase , controlling PI ( 4 , 5 ) P2 levels on basolateral recycling endosomes , and the recruitment of downstream PI ( 4 , 5 ) P2 lipid binding proteins such as RME-1 [11 , 16] . C . elegans RAB-10 and human Rab10 are now known to contribute a wide range of endocytic recycling pathways . Like its C . elegans homolog , mammalian Rab10 functions in basolateral recycling in polarized MDCK cells , where Rab10 localized to basolateral sorting endosomes and the common recycling endosome [17] . C . elegans RAB-10 is also required for the postsynaptic recycling of glutamate receptors in interneurons [18] , and dense-core vesicle secretion of neuropeptides by motor neurons [19] . Mammalian Rab10 is required for toll-like receptor 4 recycling in activated macrophages [20] , membrane insertion of plasmalemmal precursor vesicles during neuronal polarization and axonal growth [21 , 22] , and insulin-stimulated glucose transporter recycling in adipocytes [23] . Expression of human Rab10 in the C . elegans intestine rescues rab-10 mutant defects , indicating a high degree of functional conservation , suggesting that further elucidating RAB-10 function in C . elegans will provide mechanistic insight into RAB-10/Rab10 function in many or all of these related processes [10] . Countercurrent cascades of Rab GEFs and Rab GAPs have been proposed to mediate Rab conversion , a process by which Rab proteins interact , helping to establish vectorial transport of cargo along membrane trafficking pathways [24] . In such cascades early acting Rab-GTPases recruit effectors that activate later acting Rab-GTPases , and in turn later acting Rab-GTPases recruit effectors that inactivate early acting Rab-GTPases [24] . However little is known of how such cascades contribute to endocytic recycling . Here we show that RAB-10 recruits the RAB-5 GTPase-activating-protein TBC-2 to endosomes in a step necessary for early endosome to recycling endosome transport . This negative feedback from RAB-10 to RAB-5 is required for the exit of recycling cargo from early endosomes . We also show that the BAR-domain protein AMPH-1 is a binding partner of TBC-2 important for recruitment of TBC-2 to endosomes , functioning as part of the transition of cargo from the early to recycling endosome compartments .
We have previously reported several proteins that function with RAB-10 in basolateral recycling in the C . elegans intestine , some of which we first identified via a yeast two-hybrid screen that used a predicted constitutively GTP-bound form of RAB-10 ( Q68L ) as bait [14] . In this same yeast two-hybrid screen we also identified a RAB-10 ( Q68L ) interacting clone encoding full-length TBC-2 , a GAP for the earlier acting endosomal GTPase RAB-5 [13 , 22 , 25] . The interaction between RAB-10 ( Q68L ) and TBC-2 was positive in both Leu2 and β-galactosidase expression assays ( Fig 1A ) . Using successive truncations of TBC-2 we narrowed the RAB-10 binding site to a 42 amino acid region of TBC-2 ( amino acids 279–321 ) ( Fig 1A , 1B and 1E ) . We noted several runs of highly charged residues in this region , which may represent hydrophilic surface features , and tested their importance for binding to RAB-10 in groups of 5 by alanine scanning . The interaction was abolished when alanine substitutions were imposed at TBC-2 positions aa283-287 , aa288-292 , and aa294-298 ( Fig 1C ) . These mutations could disrupt binding of TBC-2 to RAB-10 by directly removing surface features involved in the binding interface , or could disrupt the local structure of this region of TBC-2 interfering with binding . Taken together , our results indicate the presence of a predicted coiled-coil domain of TBC-2 that interacts with RAB-10 , a key regulator of the basolateral endocytic recycling process . Since TBC-2 is known to act as a GAP for early endosome master regulator RAB-5 , these results suggest a negative feedback loop from RAB-10 to RAB-5 , potentially acting as part of a RAB cascade in the basolateral recycling pathway . Intestinally expressed GFP-tagged TBC-2 labels abundant cytoplasmic puncta with the typical size and shape of endosomes ( ~250–500 nm diameter ) . If TBC-2 is a physiologically relevant binding partner for RAB-10 , we would expect to find RAB-10 and TBC-2 on the same population of endosomes in vivo . Previous qualitative work indicated some localization of TBC-2 to early and late endosomes , but the extent of localization , and its relationship to recycling endosomes , remained unclear [22 , 25] . To quantitatively test the subcellular localization of TBC-2 we conducted a series of co-localization studies in the intestinal epithelial cells where RAB-10 is known to function , using a set of previously established RFP markers for RAB-10 and a variety of endocytic compartments . The degree of colocalization was measured using Pearson’s correlation coefficient , a statistical measure of the degree of linear dependence of the GFP and RFP signals [26] . Consistent with our binding data , we detected the greatest correlation coefficient of GFP-TBC-2 with RFP-tagged RAB-10 ( + ) and constitutively active RFP-RAB-10 ( Q68L ) ( Fig 2A–2A‴ and 2B–2B‴ and Fig 2D ) . The greater degree of correlation of TBC-2 signal with RAB-10 ( Q68L ) signal is consistent with a model where RAB-10 helps to recruit TBC-2 onto endosomes . GFP-TBC-2 signal also correlated very well with a previously characterized RAB-10 effector , CNT-1 ( CNT-1-mCherry ) ( Fig 2C–2C‴ and Fig 2D ) , which is also required for the recycling process [11] . These results are consistent with TBC-2 acting with RAB-10 and CNT-1 in the basolateral endocytic recycling . GFP-TBC-2 signals also showed lesser , but significant , correlations with early endosomal marker tagRFP-RAB-5 ( S2A–S2A'' and S2D Fig ) and late endosomal marker tagRFP-RAB-7 ( S2B–S2B'' Fig and S2D Fig ) . We also noted that the GFP-TBC-2 signal displays hardly any correlation with that of EHBP-1-mCherry , another RAB-10 interacting protein that labels tubular aspects of the basolateral recycling endosome network ( S2C–S2C'' Fig ) . Collectively , our results indicate that TBC-2 is enriched on a subpopulation of endosomes , where it could function with RAB-10 and RAB-5 to confer effective transport of cargo during the endocytic recycling process . To further test the idea that an interaction with RAB-10 is important for TBC-2 function in vivo , we examined the effect of a rab-10 loss-of-function mutant on the endosomal localization of GFP-TBC-2 in the intestinal epithelia . In the rab-10 mutant background GFP-TBC-2 became very diffusive , losing its typical punctate endosomal localization , indicating a requirement for RAB-10 in TBC-2 endosomal recruitment ( Fig 3A and 3B ) . Western blot analysis also showed that GFP-TBC-2 levels are reduced in rab-10 mutants , suggesting that TBC-2 is less stable in the absence of RAB-10 ( Fig 3E ) . We extended this analysis further , testing a form of TBC-2 impaired for RAB-10 binding ( QRNNE 288–292 AAAAA ) for function in vivo . In previous work we showed that TBC-2 is required for the normal recycling of model cargo hTfR-GFP ( human transferrin receptor–GFP ) [27] . In the absence of TBC-2 , hTfR-GFP accumulates in enlarged intracellular structures ( Fig 4A , 4B and 4F ) . While expression of full length wild-type TBC-2 efficiently rescued the localization of hTfR-GFP in a tbc-2 null mutant background ( Fig 4A–4C and 4F ) , we found that expression of the interaction defective form of TBC-2 failed to rescue the localization of hTfR-GFP in a tbc-2 null mutant background ( Fig 4E and 4F ) . In many cases peripheral membrane proteins of the endosome require multiple protein and/or lipid interactions to direct their localization . Recent work using phage-display to identify the binding preferences of all C . elegans SH3 domains suggested a link between TBC-2 and AMPH-1 , a BAR-domain and SH3-domain protein that is the only C . elegans member of the Amphiphysin/BIN1 protein family [28 , 29] . TBC-2 amino acid sequence 146–160 was identified as the fourth best match for the AMPH-1 SH3-domain binding consensus in the entire predicted C . elegans proteome [28] . Previous work from our laboratory has shown that AMPH-1 participates in the basolateral recycling pathway [28 , 29] . Thus we sought to further examine this potential interaction . We detected interaction of full-length TBC-2 with the AMPH-1 SH3 domain in a yeast 2-hybrid assay ( Fig 1D ) . Importantly , the interaction was abolished when key residues in the consensus sequence , prolines P150 or P153 , or arginine R155 , were mutated to alanine ( Fig 1D and 1E ) . Despite losing their ability to interact with AMPH-1 , the P150A , P153A , and R155A mutant forms of TBC-2 protein retained the ability to interact with RAB-10 ( Q68L ) in the same two-hybrid assay , indicating that the mutant forms of TBC-2 were stable ( S1A Fig ) . We conclude that the AMPH-1 SH3 domain has the potential to bind to the predicted target sequence in TBC-2 ( Fig 1D and S1A Fig ) . If an interaction between AMPH-1 and TBC-2 is important in vivo , we might expect to observe a change in TBC-2 localization in an amph-1 mutant background . Indeed , when we examined the subcellular localization of intestinally expressed GFP-TBC-2 in an amph-1 deletion mutant , we found that the normal punctate endosomal distribution of GFP-TBC-2 was severely disrupted ( Fig 3A , 3C and 3D ) . Instead , GFP-TBC-2 appeared quite diffusive in the absence of AMPH-1 , indicating that AMPH-1 is important for endosomal recruitment of TBC-2 ( Fig 3C ) . GFP-TBC-2 levels as assayed by western blot were not reduced in amph-1 mutants ( Fig 3E ) . We extended this analysis further , testing a form of TBC-2 impaired for AMPH-1 binding ( P150A ) for function in vivo , using the same hTfR-GFP localization assay described above . We found that while expression of full length wild-type TBC-2 efficiently rescued the localization of hTfR-GFP in a tbc-2 null mutant background ( Fig 4A–4C and 4F ) , the expression of the interaction defective form of TBC-2 failed to rescue the localization of hTfR-GFP in a tbc-2 null mutant background ( Fig 4A , 4B , 4D and 4F ) . Our results thus indicate that in addition to RAB-10 , AMPH-1 also contributes to TBC-2 endosomal recruitment . We also determined that AMPH-1 can interact with RAB-10 using a GST-pulldown approach with full length GST-AMPH-1 and HA-tagged RAB-10 ( Q68L ) ( S3C Fig ) . Addition of FLAG-TBC-2 to this assay showed that GST-AMPH-1 can pull down TBC-2 and RAB-10 at the same time , but the presence of TBC-2 in the reaction did not appear to increase the pulldown efficiency of RAB-10 ( S3C Fig ) . Colocalization analysis indicated the presence of AMPH-1-GFP and tagRFP-RAB-10 on a significant fraction of the same endosomes , consistent with physiological significance for the AMPH-1/RAB-10 interaction ( S3A and S3B Fig ) . However , loss of RAB-10 did not reduce association of AMPH-1-GFP with membranes ( S4A–S4C Fig ) . Rather in rab-10 mutants we observed an increase in AMPH-1-GFP puncta and tubule intensity ( S4A–S4C Fig ) . This may be an indirect effect of the increase in endosomal PI ( 4 , 5 ) P2 in rab-10 mutants that we previously showed occurs in part via another RAB-10 effector CNT-1 , an ARF-6 GAP [11] . Alternatively RAB-10 may affect AMPH-1 recruitment or function more directly , perhaps affecting its conformation or interaction with other proteins . If RAB-10 and AMPH-1 contribute to TBC-2 recruitment and function , then loss of RAB-10 or AMPH-1 would be expected to result in abnormally elevated levels of GTP-bound RAB-5 . Furthermore , since the Rab protein nucleotide cycle is linked to Rab protein membrane association , an elevated "active" GTP-bound status for RAB-5 should result in an elevated level of membrane-bound RAB-5 . This model predicts that in tbc-2 , rab-10 , and amph-1 mutants , where the RAB-5 GAP TBC-2 is either completely missing , or is mislocalized , RAB-5 association with membranes should be increased . Previous work showed that RAB-5 labeled endosomes are enlarged and/or more numerous in tbc-2 and rab-10 mutants , consistent with this model [10 , 25 , 27] . In our previous work we had assayed RAB-5 labeled early endosome number in amph-1 mutants and found no significant change [29] . However , in light of the interaction of AMPH-1 with TBC-2 , we analyzed additional parameters , and found that RAB-5 puncta intensity is increased in amph-1 mutants , consistent with elevated RAB-5 membrane association ( S5 Fig ) . Endosome size and number can change for a number of reasons , so we extended this analysis to directly measure RAB-5 membrane association biochemically . We separated membranes from cytosol in C . elegans lysates using ultracentrifugation at 100 , 000g in the appropriate mutant backgrounds , comparing the amount of intestinally expressed GFP-RAB-5 present in each fraction by Western blot . Consistent with the predictions from this model , we observed an elevation in GFP-RAB-5 membrane-to-cytosol ratio in tbc-2 , rab-10 , and amph-1 mutants ( Fig 5A–5C ) . Loss of RAB-10 or AMPH-1 increased the membrane association of RAB-5 to a lesser extent than that caused by loss of TBC-2 , suggesting that some localized TBC-2 remains in rab-10 and amph-1 mutants , although endosome localized TBC-2 is difficult to visualize by microscopy in such mutant backgrounds ( Fig 5A and Fig 5C ) . In summary , our data support a role for rab-10 and amph-1 in TBC-2 membrane recruitment that is required to complete the RAB-5 nucleotide cycle , removing RAB-5 from membranes . Since RAB-10 and AMPH-1 function in the recycling aspect of endocytic trafficking , these results suggest that removal of RAB-5 from endosomal membranes is an integral part of the recycling process , perhaps linked to cargo transition from early to recycling endosome transport . Previous work showed that RAB-5 and RAB-10 display significant spatial overlap in the C . elegans intestine , consistent with functional data indicating that RAB-10 is important for exit of recycling cargo from RAB-5-positive endosomes [10] . To better understand the relationship between RAB-5 and RAB-10 , we assayed for changes in their relative colocalization in tbc-2 and amph-1 mutants . Similar to previously published results , we found that under wild-type conditions tagRFP-RAB-5 and GFP-RAB-10 both label punctate endosomal structures that partially colocalize ( Fig 6A–6A‴ and 6D ) . We detected dramatic morphological changes for both tagRFP-RAB-5 and GFP-RAB-10 labeled endosomes in a tbc-2 mutant background . Aside from some remaining punctate structures , in tbc-2 mutants tagRFP-RAB-5 and GFP-RAB-10 tended to label very large pleiomorphic structures that were never observed in wild-type animals ( Fig 6B–6B‴ ) . Quantification of RAB-5 colocalization with RAB-10 showed a significant increase in the correlation of tagRFP-RAB-5 and GFP-RAB-10 signals in tbc-2 mutants ( Fig 6D ) , with colocalization mostly restricted to the grossly enlarged structures ( Fig 6B–6B‴ ) . amph-1 mutants also displayed a significant increase in the correlation of the tagRFP-RAB-5 and GFP-RAB-10 signals ( Fig 6C–6C‴ and 6D ) , although the morphological size and shape changes were less severe than those in tbc-2 mutants ( Fig 6C–6C‴ ) . Taken together , these data suggest that TBC-2 and AMPH-1 cause recycling defects by altering the normal compartmentalization of RAB-5 and RAB-10 on endosomes . Our previous work on RAB-10 function in the intestine showed that RAB-5 labeled endosomes in rab-10 mutants are grossly enlarged and accumulate an additional model recycling cargo , hTAC-GFP ( human TAC , IL-2 receptor alpha chain ) [10] . hTAC-GFP strongly labels the tubular aspects of the basolateral recycling pathway at steady state , and depends upon RAB-10 , RME-1 , and ARF-6 for its recycling [10–11 , 13] . To better understand the step in recycling transport affected by TBC-2 and AMPH-1 we assayed the relative localization of hTAC-GFP to tagRFP-RAB-5 and tagRFP-RAB-10 in tbc-2 and amph-1 mutants . Under wild-type conditions , hTAC-GFP displays little steady-state overlap with tagRFP-RAB-5 ( Fig 7A–7A‴ ) . In tbc-2 mutant animals , the tubular meshwork of hTAC-GFP appears disrupted , with hTAC-GFP mostly found in enlarged endosomes , many of which label for tagRFP-RAB-5 ( Fig 7B–7B‴ ) . We measured a striking increase in the degree of colocalization between hTAC-GFP and tagRFP-RAB-5 in tbc-2 mutants ( Fig 7D ) . In animals lacking AMPH-1 , we also detected a significantly larger degree of overlap between hTAC-GFP and tagRFP-RAB-5 in comparison to that of wild-type animals ( Fig 7C–7C‴ and 7D ) . Consistent with our previous reports , we observed partial overlap of hTAC-GFP with tagRFP-RAB-10 , mostly restricted to punctate rather than tubular aspects of the hTAC-GFP labeled endosomes ( Fig 8A–8A‴ ) . The degree of colocalization between hTAC-GFP and tagRFP-RAB-10 increased mildly in tbc-2 mutants and was basically unaltered in amph-1 mutants ( Fig 8A–8A‴ , 8B–8B‴ , 8C–8C‴ and 8D ) . Taking into account the aforementioned increase in colocalization between RAB-5 and RAB-10 in these mutant backgrounds , these data suggest that most hTAC-GFP in tbc-2 mutant and in amph-1 mutant animals is trapped in the early endosome .
Given the continuous flow of proteins and membranes along the endocytic and exocytic pathways , cells face a formidable challenge in achieving accurate intracellular transport of membrane cargo . Such transport is likely to require tight regulation that enforces the directionality of sequential flow between membranous compartments [24] . Rab GTPases serve as master regulators of membrane trafficking by controlling the structural and functional characteristics of intracellular organelles [24] . The ability to switch between the "on" and "off" states through the Rab GTP/GDP cycle empowers Rab proteins to control the spatial and temporal regulation of cargo transport [30] . Rabs interact with a cohort of effector proteins that contribute to a variety of functions , ranging from vesicle tethering , to vesicle budding and movement , and regulating the activation state of other small GTPases [31] . An ordered relay of cargo between sequentially acting compartments is thought to entail coordination of Rab activation states , coordinating changes in organelle maturation and/or allowing distinct compartments to interact at the right time and the right place for cargo transfer [32] . A Rab cascade model has been proposed that likely defines a general principle in membrane transport . This model proposes that an upstream GTP-loaded Rab protein recruits the GEF for the next Rab-GTPase along a transport pathway , activating the downstream Rab . In turn a countercurrent activity is initiated by the downstream GTP-loaded Rab , which recruits the GAP for the upstream Rab to deactivate it [24] . Together these activities are proposed to help enforce unidirectional flow . Such Rab cascades have been proposed for maturation based transport steps , such as the early endosome to late endosome transition , as well as transport steps mediated by small vesicle transport between distinct compartments , such as ER to Golgi transport [33–35] . While the molecular details of how such Rab cascades work are beginning to come to light in a small number of cases , little is known of how such activities influence endocytic recycling . In this study , we focused on the transition from early endosomes , controlled by RAB-5 , to recycling endosomes , controlled by RAB-10 , acting in the basolateral recycling pathway of the C . elegans intestinal epithelia . Our study shows that the downstream Rab , RAB-10 , in its GTP-bound form , binds to RAB-5 GAP TBC-2 and is required for its recruitment to endosomes . Consistent with a RAB-10 to RAB-5 negative regulatory loop via TBC-2 , loss of TBC-2 or RAB-10 increases association of RAB-5 with membranes , indicating abnormally high RAB-5 activation . Lack of TBC-2 also causes a dramatic morphological change in the RAB-5 labeled early endosomes . We observed accumulation of abnormally large , RAB-5-positive , pleiomorphic endosome structures , many of which displayed increased overlap with RAB-10 . Thus we propose that TBC-2 can serve as a bridge in the interaction between RAB-10 and RAB-5 . This model suggests that without TBC-2 , RAB-5 cannot be inactivated as part of the recycling pathway , and RAB-10 endosomes cannot properly separate from RAB-5 endosomes . Our cargo localization analysis shows that in tbc-2 mutants the recycling cargo hTAC is mostly trapped in RAB-5 positive endosomes , indicating a defect in the exit of recycling cargo from early endosomes that cannot inactivate RAB-5 . Our work is consistent with , and extends , work in C . elegans neurons that independently identified a connection between RAB-10 and TBC-2 important for neuropeptide secretion [19] . Thus the biogenesis and/or cargo loading of dense-core granules appears to share mechanistic similarities with endocytic recycling . Our results are also reminiscent of a counter-current GAP cascade in Saccharomyces cerevisiae that is required to restrict the spatial overlap of early and late Golgi Rabs Ypt1p and Ypt32p [36] . Our study also showed that cargo transition from early endosomes to recycling endosomes requires the coordination of another regulator of the recycling pathway , BAR-domain protein AMPH-1 . Like RAB-10 , AMPH-1 contributes to endosomal recruitment of TBC-2 . We also detected failure in proper separation of RAB-5 and RAB-10 and failure in the exit of recycling cargo from early endosomes in amph-1 mutants , although the endosomes did not appear as grossly enlarged as in tbc-2 mutants . The AMPH-1 BAR domain binds directly to PI ( 4 , 5 ) P2 enriched membranes , can potentially sense membrane curvature , and can promote tubule formation [29] . An interesting possibility is that AMPH-1 derived membrane tubules could be directly involved in cargo transfer . Our previous work also showed that AMPH-1 binds to RME-1 , a later acting player in the basolateral recycling pathway , potentially acting to coordinate early and late aspects of recycling [29] . Our current study delineated distinct regions of TBC-2 bound by RAB-10 and AMPH-1 . Combined with our previous work showing a connection of CED-10/Rac1 to TBC-2 and recycling [27] , our observations indicate that TBC-2 is a key feedback regulator of RAB-5 , acting as a molecular nexus that integrates signals from recycling endosome regulators RAB-10 , AMPH-1 , and CED-10 . The correct localization of peripheral membrane proteins is often maintained by multiple weak physical interactions , perhaps to more precisely position such proteins at points where multiple binding partners converge , a concept sometimes called coincidence sensing . Precise recruitment of TBC-2 to endosomes during recycling is likely to be quite important in the complex process of endosomal transport , where RAB-5 activity is essential for early aspects of the pathway but needs to be deactivated for later events . Such localization mechanisms may also be easily reversible , an important characteristic in dynamic situations . In wild-type animals we found that RAB-5-labeled endosomes and RAB-10-labeled endosomes appear as distinct puncta that show partial overlap , suggesting that only a subpopulation of RAB-5 and RAB-10 labeled endosomes is interacting at any given time . This could imply the existence of transient interactions between RAB-5 and RAB-10 labeled endosomes that function to transfer cargo , removing recycling cargo as the early endosome matures into the late endosome . Intermediates in this process could be trapped , or delayed in resolution , in tbc-2 , rab-10 , and amph-1 mutants . Such transient interactions between early and recycling endosomes have been proposed in other systems , although the detailed mechanisms remain obscure [37] . Interestingly that work also indicated a BAR domain protein ( Nwk ) was involved in early endosome to recycling endosome transport , perhaps indicating cargo transfer via membrane tubules . More work will be required to understand the dynamic interactions between early and recycling endosomes that mediate cargo transfer .
All C . elegans strains were derived originally from the wild-type Bristol strain N2 . Worm cultures , genetic crosses , and other C . elegans husbandry were performed according to standard protocols [38] . Strains expressing transgenes were grown at 20°C . A complete list of strains used in this study can be found in S1 Table . Secondary structures of TBC-2 protein were predicted using the Quick2D from the Bioinformatics Toolkit ( Max-Planck Institute for Developmental Biology ) . ( Web link: http://toolkit . tuebingen . mpg . de/quick2_d ) The yeast two-hybrid experiments were performed according to the procedure of the DupLEX-A yeast two-hybrid system ( OriGene Technologies ) . All two-hybrid plasmids were generated as PCR products with Gateway attB1 . 1 and attB2 . 1 sequence extensions and were introduced into the Gateway entry vector pDONR221 by BP clonase II ( Invitrogen ) reaction . The bait vector pEG202-Gtwy and target vector pJG4-5-Gtwy have been described previously [39] . Origene plasmid pSH18-34 ( URA3 , 8 ops . -LacZ ) was used as a reporter in all yeast two-hybrid experiments . Constructs were introduced into the yeast strain EGY48 ( MATα trp1 his3 ura3 leu2::6 LexAop-LEU2 ) included in the system . Transformants were selected on plates lacking leucine , histidine , tryptophan , and uracil and containing 2% ( wt/vol ) galactose/1% ( wt/vol ) raffinose at 30°C for 3 days and were assayed for the expression of the LEU2 reporter . The constructs of mutated forms of TBC-2 with alanine substitution were constructed by Q5—Site Directed Mutagenesis Kit ( New England Biolabs , Inc . ) using the cDNA sequence of TBC-2 as template . To construct GFP or RFP/mCherry fusion transgenes that express specifically in the worm intestine , we used a previously described vha-6 promoter-driven vector modified with a Gateway cassette inserted at the Asp718I site just upstream of the GFP or RFP coding region [10] . The PCR products of the genes of interest were first cloned into the Gateway entry vector pDONR221 by BP reaction ( Invitrogen ) . Then the PDONR221 plasmids carrying the sequences of interest were transferred into the intestinal expression vectors by Gateway recombination cloning , in a LR clonase II ( Invitrogen ) reaction , to generate N-terminal/C-terminal fusions [10] . Low-copy integrated transgenic lines for all of these plasmids were obtained by the microparticle bombardment method [40] . Transgenic strains pwEx142-144 were generated as following . Full-length TBC-2 , TBC-2 ( P150A ) , and TBC-2 ( 288-292AAAAA ) was first cloned into entry vector pDONR221 . pSM47 pSNX-1::tagRFP , pDONR221 containing TBC-2 , or TBC-2 ( P150A ) , TBC-2 ( 288-292AAAAA ) , pCM1 . 36-TBB-2 3'-UTR was inserted into the pCFJ1001 vector via multi-site LR reaction ( Gateway LR Clonase II Plus Enzyme by Life Technologies ) . Rescue plasmids pCFJ1001::pSNX-1::tagRFP::TBC-2 ( full length , P150A , or 288-292AAAAA ) ( 10 ng/ul ) , pCFJ601 ( 50 ng/ul ) and pmyo-2::GFP ( coinjection marker ) ( 10 ng/ul ) were microinjected and resulting extrachromosomal arrays were used in this study [41] . For yeast two-hybrid analysis pEG202-RAB-10 ( Q68L ) , pEG202-AMPH-1 ( SH3 ) , and pJG4-5-TBC-2 were constructed by gateway cloning as described previously [10 , 29] . For GST pull-down experiments rab-10 ( Q68L ) and tbc-2 cDNA clones were transferred to in-house modified pcDNA3 . 1 ( + ) ( Invitrogen ) vectors containing 2xHA or 3xFLAG epitope tags and a Gateway cassette ( Invitrogen ) as described previously [11] . Live worms were mounted on 2% agarose pads with 10mM levamisole as described previously [39] . Multiwavelength fluorescence colocalization images were obtained using an Axio Imager . Z1 microscope ( Carl Zeiss Microimaging ) equipped with a YOKOGAWA CSU-X1 spinning disk , Photometrics Evolve 512 EMCCD camera , captured using Metamorph software ( Universal Imaging ) , and then deconvolved using AutoQuant X5 ( AutoQuant Imaging ) . Images taken in the DAPI channel were used to identify broad-spectrum intestinal autofluorescence caused by lipofuscin-positive lysosome-like organelles [42 , 43] . Quantification of colocalization images was done using the open source Fiji ( Image J ) software [44] . GFP/RFP colocalization experiments were performed on L4 larvae expressing GFP and RFP markers as previously described . To obtain images of GFP fluorescence without interference from autofluorescence , we used argon 488-nm excitation and the spectral fingerprinting function of the Zeiss LSM710 Meta confocal microscope system ( Carl Zeiss Microimaging ) . Quantification of images was performed with Metamorph Version 6 . 3r2 ( Universal Imaging ) . Worms expressing intestinal GFP-RAB-5 in wild-type , tbc-2 ( tm2241 ) , rab-10 ( q373 ) and amph-1 ( tm1060 ) genetic backgrounds were synchronized and cultured on NGM . Mixed stage worms were washed off with M9 buffer , pelleted and resuspended in 500μl of lysis buffer ( 50 mM Tris-HCL PH 8 . 0 , 20% Sucrose , 10% Glycerol , 2 mM DTT and protease inhibitors ) . The worms are then disrupted using a Mini-Beadbeater-16 ( BioSpec Products ) . Carcasses and nuclei were removed by centrifugation at 1000g for 5 min at 4°C . 200 μl of the postnuclear lysate was centrifuged at 100 , 000g for 1h . Pellets were reconstituted in the same volume of lysis buffer as that recovered as supernatant . Worms expressing intestinal GFP-TBC-2 in wild-type , rab-10 ( ok1494 ) , and amph-1 ( tm1060 ) genetic backgrounds were synchronized and cultured on NGM plates . 50 young adult animals of each genotype were handpicked into 10 μl of lysis buffer ( 100 mM Tris pH 6 . 8 , 8% SDS , 20 mM β-mercaptoethanol ) and boiled at 100°C for 10 min . Extracted worm proteins were separated by 10% SDS-PAGE and blotted to nitrocellulose . After blocking , the blot was probed with HRP-conjugated anti-GFP antibody . rab-10 ( Q68L ) and tbc-2 cDNA clones were transferred to in-house modified pcDNA3 . 1 ( + ) ( Invitrogen ) vectors containing 2xHA or 3xFLAG epitope tags and a Gateway cassette ( Invitrogen ) for in vitro transcription/translation experiments using the TNT-coupled transcription-translation system ( Promega ) . Full length GST and GST-AMPH-1 was expressed and purified as previously described [29] . Eluted proteins were separated by ExpressPlus PAGE ( 4–20% ) ( GenScript ) , blotted to nitrocellulose , and stained with Ponceau S to detect GST fusion proteins . After blocking , the blot was probed with anti-HA ( 16B12 ) antibody and anti-FLAG M2-Peroxidase antibody ( Sigma-Aldrich ) . | When cargo is internalized from the cell surface by endocytosis , it enters a series of intracellular organelles called endosomes . Endosomes sort cargo , such that some cargos are sent to the lysosome for degradation , while others are recycled to the plasma membrane . Small GTPase proteins of the Rabs family are master regulators of endosomes , functioning by acting as molecular switches . As cargo moves through the endosomal system , it must pass from the domain controlled by one Rab-GTPase to the domain controlled by another . Little is known about how transitions along the recycling pathway are controlled . Here we analyze a group of protein interactions that act along the early-to-recycling pathway . Our work shows that RAB-5 deactivation mediated by TBC-2 and its recruiters RAB-10 and AMPH-1 is important for cargo recycling . This work provides mechanistic insight into how Rab proteins controlling different steps of trafficking interact during endocytic recycling . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
"and",
"Methods"
] | [] | 2015 | Basolateral Endocytic Recycling Requires RAB-10 and AMPH-1 Mediated Recruitment of RAB-5 GAP TBC-2 to Endosomes |
The large extracellular loop of the Schistosoma mansoni tetraspanin , Sm-TSP-2 , when fused to a thioredoxin partner and formulated with Freund's adjuvants , has been shown to be an efficacious vaccine against murine schistosomiasis . Moreover , Sm-TSP-2 is uniquely recognised by IgG1 and IgG3 from putatively resistant individuals resident in S . mansoni endemic areas in Brazil . In the present study , we expressed Sm-TSP-2 at high yield and in soluble form in E . coli without the need for a solubility enhancing fusion partner . We also expressed in E . coli a chimera called Sm-TSP-2/5B , which consisted of Sm-TSP-2 fused to the immunogenic 5B region of the hookworm aspartic protease and vaccine antigen , Na-APR-1 . Sm-TSP-2 formulated with alum/CpG showed significant reductions in adult worm and liver egg burdens in two separate murine schistosomiasis challenge studies . Sm-TSP-2/5B afforded significantly greater protection than Sm-TSP-2 alone when both antigens were formulated with alum/CpG . The enhanced protection obtained with the chimeric fusion protein was associated with increased production of anti-Sm-TSP-2 antibodies and IL-4 , IL-10 and IFN-γ from spleen cells of vaccinated animals . Sera from 666 individuals from Brazil who were infected with S . mansoni were screened for potentially deleterious IgE responses to Sm-TSP-2 . Anti-Sm-TSP-2 IgE to this protein was not detected ( also shown previously for Na-APR-1 ) , suggesting that the chimeric antigen Sm-TSP-2/5B could be used to safely and effectively vaccinate people in areas where schistosomes and hookworms are endemic .
Schistosomiasis ranks among the most important infectious diseases in tropical regions , resulting in a loss of between 4 . 5 and 92 million Disability-Adjusted Life Years ( DALYs ) annually and almost 300 , 000 deaths in sub-Saharan Africa alone [1] , [2] , [3] . High rates of post-treatment reinfection [1] , the inability of periodic chemotherapy to interrupt transmission [4] , the exclusive reliance on praziquantel as the only chemotherapeutic option [5] , [6] and the unsustainability of mass drug administration [7] has led to the development of new anti-schistosomiasis control measures , inlcuding vaccines , to complement existing initiatives [5] , [8] , [9] . Molecules lodged in the apical membrane of the schistosome tegument represent vulnerable targets for immunological attack by host antibodies due to their intimate association with the host immune system . One such family of molecules – predicted by proteomic analyses of the schistosome tegument to be accessible to host immunoglobulin [10] – is the tetraspanin integral membrane proteins . Tetraspanins contain four transmembrane domains and two extracellular loops that are predicted to interact with exogenous ligands [11] , [12] . Indeed , the second extracellular loop of one of these schistosome tetraspanins , Sm-TSP-2 , has proven to be an effective anti-schistosomiasis vaccine , eliciting 57–64% protection in mice vaccinated with the antigen followed by challenge with S . mansoni cercariae [12] . Other schistosome tetraspanins are protective in mouse models of schistosomiasis [10] , including Sm23 [13] , [14] and Sj-TSP-2 , an S . japonicum orthologue of Sm-TSP-2 [15] . Moreover , Sm-TSP-2 was strongly recognised by IgG1 and IgG3 from putatively resistant but not from chronically infected individuals [12] , further highlighting the promise of this antigen as a subunit vaccine against human schistosomiasis . The tegument of adult and schistosomula of S . mansoni is thinner and distinctly more vacuolated compared to controls after in vitro treatment with Sm-tsp-2 double-stranded RNA ( dsRNA ) [16] . Moreover , injection of mice with schistosomula pre-treated with Sm-tsp-2 dsRNA resulted in the recovery of 83% fewer parasites from the mesenteries compared to controls [16] , highlighting the importance of Sm-TSP-2 in proper tegument development and worm survival , and providing a potential mechanism by which the vaccine exerts its protective effect . In an earlier study , we reported the production of a chimeric form of Sm-TSP-2 , consisting of Sm-TSP-2 fused to the immunodominant and neutralizing 5B region of the hookworm aspartic protease Na-APR-1 , termed Sm-TSP-2/5B [17] . Hookworm infection and schistosomiasis caused by S . mansoni are co-endemic in much of sub-Saharan Africa and Brazil , and there is potential interest in developing a vaccine that targets both of these high prevalence and high disease burden helminths [18] . Na-APR-1/5B is a 40 amino acid fragment of the protease that contains an immunodominant alpha helix , A291Y , which is the target epitope recognized by polyclonal and monoclonal antibodies that are capable of neutralizing the catalytic activity of Na-APR-1 [17] . Na-APR-1/5B could not be produced in soluble form , but when fused to Sm-TSP-2 , it was produced in soluble form by E . coli and induced antibodies upon vaccination that neutralized the enzymatic activity of Na-APR-1; the chimera is currently under investigation as a hookworm vaccine . Using a mouse model of schistosomiasis , we explored the efficacy of the Sm-TSP-2/5B chimera in comparison to Sm-TSP-2 alone when both antigens are formulated with alum/CpG . Given the recent safety concerns of helminth vaccines that elicit an IgE response in individuals residing in an endemic area [19] , we also assessed the recognition of Sm-TSP-2/5B by IgE from individuals chronically infected with S . mansoni , a crucial step in determining whether or not this antigen could be used to safely and effectively vaccinate people in areas endemic for both hookworms and schistosomes .
All work involving experimental procedures with laboratory animals was approved by the animal ethics committee of James Cook University according to the regulations of the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes , 7th edition ( reference EA16 ) . All work involving human subjects research was approved by the Human Research Ethics Committees or Institute Review Boards of Instituto René Rachou-FIOCRUZ , the Brazilian National Committee for Ethics in Research ( CONEP ) , George Washington University Medical Center , and the London School of Hygiene and Tropical Medicine . Informed written consent was obtained from all adults or the parents and guardians of all children involved in the study . Oligonucleotide primers incorporating NdeI and XhoI restriction sites ( forward primer: GCGCATATGGAAAAGCCCAAGGTCAAAAAACAC; reverse primer GCGCTCGAGGTGCGCTTTGCTTAGATCGCTGAC ) and pfu turbo DNA polymerase ( Stratagene ) were used to amplify the extracellular loop 2 region ( Glu-107 – His-184 ) of the S . mansoni tetraspanin Sm-TSP-2 from the pBAD/TOPO/Sm-TSP-2 plasmid [12] in our laboratory . The amplicon was then cloned into the NdeI and XhoI sites of the pET41a expression vector ( Novagen ) , removing the GST fusion tag to allow for native N-terminal expression of the protein , but retaining the vector's C-terminal 6×his tag to facilitate purification by Immobilised Metal Affinity Chromatography ( IMAC ) . The ensuing plasmid was then transformed into chemically competent E . coli BL21-AI cells ( Invitrogen ) . Sm-TSP-2 was expressed using the auto-induction method and media formulations established by Studier [20] . Briefly , 10 ml of minimal media supplemented with 50 µg/ml kanamycin ( MDGkan ) was inoculated with a single , recombinant BL21-AI colony and grown overnight at 37°C with shaking ( 225 rpm ) . The entire overnight culture was then used to seed 1 . 0 L of defined media supplemented with 50 µg/ml kanamycin ( ZYM-5052-Akan ) , which was incubated for 24 hours at 37°C with shaking ( 225 rpm ) . Bacteria were pelleted , lysed and the resultant homogenate purified by ( IMAC ) as described previously [17] . Purified Sm-TSP-2 was buffer-exchanged in a dialysis bag ( Pierce ) with a cut-off size of 3 kDa against two changes of 50 mM sodium phosphate , pH 6 . 5 , 10 mM NaCl ( CEX buffer ) ( 2 . 0 L each ) at 4°C for at least 2 hours and then further purified by passing through a pre-packed 5 . 0 ml Hi-Trap SP-FF column ( GE Healthcare ) ( equilibrated with 10 column volumes of CEX buffer ) at a flow rate of 1 . 0 ml/min using an AKTA Prime UPC FPLC unit ( GE Healthcare ) . Bound protein was purified by washing with resuspension buffers containing a rising concentration ( 10–500 mM ) of NaCl and eluting in 5 column volumes of elution buffer ( 50 mM sodium phosphate , pH 6 . 5 , 1 . 0 M NaCl ) . Sm-TSP-2 was desalted in a dialysis bag ( Pierce ) with a cut-off size of 3 kDa against two changes of PBS ( 2 . 0 L each ) at 4°C for at least 2 hours and the final protein concentration was adjusted to 1 . 0 mg/ml using an Amicon Ultra-15 centrifugal concentration device ( Millipore ) . Sm-TSP-2/5B was produced in E . coli and purified as previously described [17] . The pMal-4E plasmid encoding Maltose Binding Protein ( MBP ) was kindly provided by Dr F . Cardoso and MBP was expressed in E . coli and purified on amylose resin according to the manufacturer's instructions ( New England Biolabs ) . An emulsion containing 100 µg of Sm-TSP-2 or Sm-TSP-2/5B ( 1 . 0 mg/ml ) and an equal volume of Freund's complete adjuvant was subcutaneously injected into a single New Zealand White rabbit . The same amount of antigen emulsified in an equal volume of Freund's incomplete adjuvant was similarly administered 2 and 4 weeks later . The rabbit was bled 2 weeks later and the serum collected by centrifugation . Freshly perfused adult S . mansoni were fixed in 100% methanol overnight at 4°C , embedded in Tissue-tek Optimal Cutting Temperature compound ( ProSciTech ) and cryostatically sectioned into 7 . 0 µm sections . Sections were rehydrated in PBS and blocked with PBS/0 . 05% Tween 20 ( PBST ) /1% Foetal Calf Serum ( FCS ) for 1 hour at RT . After washing twice ( 5 minutes each ) with PBST , sections were incubated with either anti-Sm-TSP2 , anti-Sm-TSP2/5B or naive rabbit sera ( 8 . 0 µl in 200 µl PBST/1% BSA ) and 5 . 0 µl methanolic Alexa Fluor 488-Phalloidin ( Invitrogen ) for 1 hour at RT and then washed again ( 3×5 minutes each ) . The sections were then probed with goat anti-rabbit IgG-Cy3 ( Jackson ) ( 1∶500 in PBST/1% BSA ) for 1 hour at RT . After a further 3 washes with PBST , slides were air dried briefly and mounted with cover slips using PBS/50% Vectorshield mounting medium with DAPI ( Vector Industries ) to stain nuclei . These were examined using a Leica IM1000 DMIRB inverted fluorescence microscope . The inhibition of hemoglobin digestion by Na-APR-1 using anti-Sm-TSP2/5B IgG was performed as described previously [17] . An equal amount of anti-Sm-TSP-2 IgG was used as a negative control . The study was conducted in Americaninhas , a rural community in northeast Minas Gerais state , Brazil and has been described in detail [12] . The study design was a total population survey , with all individuals in a 10 km2 area eligible for inclusion . All participants excluded from the study were offered a fecal exam and treated for all helminth infections , but were not considered part of the data set for analysis . Women who were evidently pregnant , or who tested positive on a urine pregnancy test received treatment for all helminth infections after the end of the pregnancy or the termination of breast-feeding . The parasitological survey and blood draw were performed during April-July 2004 , the results of which can be found in Table 1 . Subjects were asked to provide two fecal samples on two separate days , which were examined qualitatively by formalin-ether sedimentation . Helminth-positive samples were then examined by Kato–Katz fecal thick smear to quantify the intensity of infection , as eggs per gram of feces ( epg ) . Two slides were counted from each day's sample , i . e . 2–4 slides from each individual , as some individuals only provided one sample . Individuals who were egg-positive by sedimentation but negative by Kato-Katz were assigned a count of 3 epg , half the Kato-Katz detection limit . Hookworm was exclusively N . americanus . Adults or children positive for gastrointestinal nematodes were offered a single 400 mg dose of albendazole and individuals infected with S . mansoni were treated with praziquantel . Egg-negative individuals were not treated . Treated individuals were examined post-treatment to confirm treatment efficacy , and offered repeat treatment ( s ) until egg-negative . Approximately 20 ml of blood was collected from 666 volunteers in siliconized tubes for separation of serum . In brief , the level of IgE against Sm-TSP-2 was measured by indirect ELISA using Polysorp 96-well microtiter ELISA plates ( NUNC F96 , Fisher Scientific ) which were incubated overnight at 4°C with antigen ( 1 µg/ml in 0 . 15 M PBS , pH 7 . 2 ) . After washing with PBST , the plates were blocked for 2 hours at RT with 250 µl of PBST/3% BSA . One hundred microliters of sera ( 1∶25 in PBST/3% BSA ) were added to the wells and incubated overnight at 4°C , then the plates were washed with PBST and 100 µl of mouse biotin-conjugated monoclonal anti-human IgE FC ( Human Reagent Laboratory , Baltimore , MD ) ( 1∶200 in PBST/3% BSA ) was added to the plates . Plates were incubated for 2 hours at RT and then washed with PBST . Plates were developed by adding o-Phenylenediamine dihydrochloride in 0 . 05 M phosphate-citrate buffer ( pH 5 . 0 ) plus 30% hydrogen peroxide H2O2 for 30 minutes at RT in the dark . Fifty microliters of 2N H2SO4 was added to stop the colorimetric reaction , which was read at a wavelength of 490 nm on a SpectraMax 340 PC ( Molecular Devices ) microplate reader . SOFTmax Pro for Windows was used for the analysis and storage of data . Approval for the work described in this study was obtained from the James Cook University Animal Ethics Committee . Groups of ten female C57BL/6 mice were immunised with Sm-TSP-2 , Sm-TSP-2/5B , or the control protein MBP . Each antigen ( 25 µg per dose ) was formulated with an equal volume ( 25 µl ) of a 13 mg/ml colloidal suspension of aluminium hydroxide gel ( alum ) ( Sigma ) and 5 µg of CpG oligodinucleotide 1826 ( CpG ) ( Invivogen ) and injected intraperitoneally on days 0 , 14 and 28 . Mice were challenged on day 42 with 120 S . mansoni cercariae by abdominal penetration [21] . Trials were conducted twice on different dates and with different batches of cercariae . Serum samples were collected at day −2 ( pre-immunisation ) , day 40 ( pre-challenge ) and day 91 ( necropsy ) to assess antibody responses . Mouse necropsy and worm and egg burden assessments were performed as described previously [12] . Reductions in parasite loads were calculated as percentages of the parasite burden in the control group . Statistical significance was assigned a threshold of P = 0 . 05 and values were determined using the student's t test function in Graph Pad Prism . Individual anti-Sm-TSP-2 titres ( total IgG , IgG1 and IgG2a ) were determined for all trial 1 animals just prior to cercarial challenge and at necropsy using standard ELISA techniques . Antigen was coated on microtiter plates at 1 . 0 µg/ml . Sera were serially diluted ( 1∶1 , 000 to 1∶16 , 384 , 000 for total IgG and IgG1 measurements and 1∶1 , 000 to 1∶256 , 000 for IgG2a assessment ) and 100 µl was added to each well . After addition of the appropriate horseradish peroxidase-conjugated goat antibody ( Jackson ) , peroxidase activity was detected with tetramethyl benzidine chromogenic substrate and measured at 655 nm . Spleens were taken from all animals from trial 2 , and single cell suspensions prepared by passing through a 70 µm filter ( BD Biosciences ) . Red blood cell lysis buffer ( Sigma ) was used to remove red blood cells . Splenocyte preparations were counted , and cultured in duplicate at 1×106 cells/well in 96-well plates . Schistosome egg antigen ( SEA ) and soluble adult worm antigen preparation ( SWAP ) were prepared as described respectively [18] , [19] and added to the cultures at 10 µg/ml and cultured at 37°C , 5% CO2 for 72 h . Levels of IL-4 , IL-10 , and IFN-γ in cell-free supernatants were assessed by ELISA ( OptEIA , BD Biosciences ) .
The large extracellular loop of Sm-TSP-2 ( Sm-TSP-2 ) ( molecular weight including 6×His tag = 10 kDa ) was expressed in E . coli using the auto-induction technique of Studier [20] instead of the more conventional method of IPTG induction normally used to drive protein expression in T7 promoter-based , inducible systems . In addition to producing an increased biomass despite using identical seeding conditions and culture volumes , Sm-TSP-2 was produced by auto-induction and purified by IMAC to a final concentration of 100 mg/L ( Fig . 1A ) , more than twice the yield of Sm-TSP-2 obtained by IPTG-induction ( data not shown ) . To obtain reasonable yields of soluble chimeric Sm-TSP-2/5B ( molecular weight including 6×His tag = 16 . 1 kDa ) , the protein required expression in the less reductive cytoplasmic environment of the slow-growing Rosetta-Gami strain of E . coli , in addition to being cultured at a sub-optimal growth temperature of 23°C; as a result , auto-induction of Sm-TSP-2/5B was not a feasible production method . Nevertheless , when expressed using IPTG-induction and purified by IMAC , we obtained a yield of 20 mg/L of soluble Sm-TSP-2/5B ( Fig . 1B ) . The localization of Sm-TSP-2 to the outer tegument of S . mansoni has previously been documented using an antibody raised to the thioredoxin fusion protein [12] . The recognition of native Sm-TSP-2 by anti-Sm-TSP-2/5B antibodies ( Fig . 2A ) indicated that parasite-derived Sm-TSP-2 epitopes were faithfully reproduced in the recombinant protein and were not disrupted by the addition of the 5B region of Na-APR-1 to the C-terminus of Sm-TSP-2 . No reaction was observed with naive rabbit serum ( Fig . 2B ) . Similarly , the ability of anti-Sm-TSP-2/5B IgG to bind ( and inhibit ) Na-APR-1 hemoglobinase activity demonstrates the preservation of 5B epitopes within the chimeric protein . No hemoglobinase inhibition of the enzyme was observed when anti-Sm-TSP-2 IgG was used in the assay ( Fig . 2C ) . Sera from 666 individuals from Minas Gerais state , Brazil – an area of high S . mansoni transmission – were assessed for the presence of an IgE response against Sm-TSP-2 . No detectable levels of anti-Sm-TSP-2 IgE antibodies were observed , despite the presence of a strong IgE response to SEA in some individuals ( Fig . 3 ) . Mice vaccinated with alum/CpG adjuvanted Sm-TSP-2 and Sm-TSP-2/5B mounted strong Sm-TSP-2-specific IgG responses ( Table 2 ) . IgG1 responses dominated and IgG2a responses were generally weak ( not shown ) . Pre-challenge IgG endpoint titers ( four-fold serial dilutions ) ranged from 256 , 000–1 , 024 , 000 for Sm-TSP-2 vaccinated mice and 256 , 000–4 , 096 , 000 for Sm-TSP-2/5B vaccinated mice . At necropsy ( post-challenge ) , titers had waned to 64 , 000–256 , 000 for Sm-TSP-2 vaccinated mice and 64 , 000–1 , 024 , 000 for Sm-TSP-2/5B vaccinated mice . Mean and median anti-Sm-TSP-2 antibody titers were higher in the group vaccinated with Sm-TSP-2/5B ( means 486 , 400 vs 1 , 450 , 667; medians 256 , 000 vs 1 , 024 , 000 ) , implying that mice vaccinated with the chimera made a stronger antibody response against the Sm-TSP-2 region of the immunogen , and increased titers were not due to anti-5B antibodies . No obvious association between antibody titer and parasite burden was detected . Of the mice vaccinated with Sm-TSP-2/5B , two mice had no worms , one mouse had two worms and one mouse had four worms . All four mice had the lowest liver egg burdens and high antibody titers ( ≥1 , 024 , 000 ) . However , two other mice had equally strong antibody titers but had higher parasite burdens ( 29 and 34 worms ) , precluding determination of a robust correlation between worm burdens and antibody titers . Sm-TSP-2/5B and Sm-TSP-2 formulated with alum/CpG protected against experimental challenge with S . mansoni . Vaccinated groups had respective decreases in worm burden of 54–58% ( Sm-TSP-2/5B , P<0 . 01 ) and 25–27% ( Sm-TSP-2 , P<0 . 05 ) , compared to controls over two independent trials ( Fig . 4A and 4B ) . A comparative reduction in liver egg burdens was also observed in these groups – 48–56% ( Sm-TSP-2/5B , P<0 . 01 ) and 20–27% ( Sm-TSP-2 , P<0 . 05 ) , respectively ( Fig . 5A and 5B ) . When the data from both trials were combined , significant decreases in worm and liver egg burdens were seen between the group vaccinated with Sm-TSP-2/5B and the group vaccinated with Sm-TSP-2 ( P<0 . 01 and P<0 . 05 , respectively ) . Liver egg burdens were not disproportionately reduced compared with burdens of worms , suggesting no additional effect on parasite fecundity ( Table 2 ) . Splenocytes from vaccinated and challenged animals were restimulated with SEA and SWAP to assess the cytokine responses to vaccination and parasite challenge . Levels of IL-4 , IL-10 and IFN-γ from splenocytes were elevated in all infected animals compared to uninfected MBP-vaccinated animals when restimulated ex vivo with SEA and SWAP ( Figure 6 ) , indicating that infection-related cytokine responses were produced , although responses to SEA were generally higher . SEA and SWAP-specific IL-4 responses tended to increase in Sm-TSP-2/5B-vaccinated animals compared to control ( MBP-vaccinated ) infected animals , however this only reached significance with SWAP restimulation . IL-10 production in response to SWAP , but not SEA , was also increased due to Sm-TSP-2/5B vaccination . IFN-γ production in response to both SEA and SWAP were also highly significantly increased ( P<0 . 01 ) in response to Sm-TSP-2/5B vaccination .
We have previously demonstrated that the large extracellular loop of the S . mansoni tegument tetraspanin , Sm-TSP-2 , when linked to a thioredoxin fusion partner and formulated with Freund's adjuvants , is an efficacious vaccine antigen , eliciting high levels of protection in a murine schistosomiasis model of infection [12] . Herein , we show that modified and chimeric forms of the Sm-TSP-2 vaccine antigen are also protective , even when formulated with a human-approved adjuvant combination , and that a schistosomiasis vaccine based on Sm-TSP-2 ( or Sm-TSP-2/5B ) satisfies additional selection criteria for progression into clinical trials , such as safety concerns around the potentially deleterious effects of pre-existing IgE responses in helminth endemic populations [1] , [19] . There is a paucity of funding - driven by the lack of a commercially viable market - available for the production of vaccines against the neglected tropical diseases , and so a vaccine antigen must be amenable to low-cost manufacture [22] . Despite attempts at optimisation of production of these two antigens being preliminary at best , both Sm-TSP-2 and Sm-TSP-2/5B have been expressed at yields that , at this initial stage , may be indicative of cost-effective up-scaling and clinical development . Indeed , Sm-TSP-2 has been recently produced in Pichia pastoris fermentation cultures in our laboratory at a yield or over 500 mg/L ( data not shown ) and efforts are currently underway to express Sm-TSP-2/5B in a similar fashion . We recently suggested that the presence of a pre-existing human serum IgE response to a helminth vaccine antigen is a down-selection criterion [1] when considering a molecule for progression towards clinical trials because of the safety risks involved [19] . No detectable levels of Sm-TSP-2-specific IgE were found in individuals chronically infected with S . mansoni , despite very strong IgE responses to proteins found within SEA . This is also the case for the hookworm antigen , Na-APR-1 [23] , the origin of the 5B domain in Sm-TSP-2/5B . Despite the absence of a detectable IgE response , previous studies have shown that humans from schistosome- and hookworm-endemic areas mount IgG1 responses to Sm-TSP-2 [12] and Na-APR-1 [23] , indicating that both antigens are recognized by the immune system in a natural infection . What determines the isotype response ( IgG vs IgE ) mounted by an infected individual to a helminth antigen is multifactorial and an unresolved topic of debate [24] . What is clear , however , is the potential danger of developing a vaccine based on an antigen that is the target of a naturally acquired IgE response in the target population . Of the two test groups , mice vaccinated with Sm-TSP-2/5B had the highest level of protection against experimental schistosomiasis . We initially hypothesized that this increased protection was due to cross-reactive epitopes within the 5B region of hookworm Na-APR-1 and its S . mansoni orthologue , Sm-catD [25] . However , numerous attempts to show binding of anti-Sm-TSP-2/5B to recombinant Sm-catD and schistosome extracts using Western blotting and immunoprecipitation coupled to tandem mass spectrometry ( A . Dougall and A . Loukas , unpublished ) were unsuccessful . Anti Sm-TSP-2/5B did , however , bind strongly to recombinant Na-APR-1 and inhibited the ability of the enzyme to cleave a synthetic substrate in a previous study [17] and has likewise been shown to neutralise the hemoglobinase capacity of Na-APR-1 in this study; indeed , the 5B region of Sm-TSP-2/5B is highly immunogenic and was the target of a panel of IgG1 mAbs raised to recombinant Na-APR-1 [17] . Production of a chimeric antigen comprising Sm-TSP-2 and the 5B region of Sm-CatD from S . mansoni instead of Na-APR-1 is currently underway in our laboratory and may have the additional benefit of being able to induce an antibody-mediated neutralization of Sm-TSP-2 in the tegument and Sm-CatD in the gut of the intra-mammalian stages of S . mansoni . Given the absence of an obvious cross-reactive schistosome epitope for antibodies to the Na-APR-1/5B fragment , we therefore sought to confirm whether the increased protection obtained with Sm-TSP-2/5B compared to Sm-TSP-2 alone was due to the increased size and therefore increased immunogenicity of the chimera . When microtiter plates were coated with Sm-TSP-2 and probed with antisera from mice immunized with Sm-TSP-2 or Sm-TSP-2/5B , the IgG endpoint titers were higher on average for the group immunized with Sm-TSP-2/5B , implying that vaccination with the larger immunogen resulted in an increased TSP-2-specific antibody titer . We also noted that individual mice with the highest antibody titers had the fewest worms , as highlighted in Table 2 . Studies are also in progress to determine whether the chimeric protein generates similar levels of protection against hookworm infection caused by Necator americanus . Restimulation of splenocytes from vaccinated and infected mice prior to necropsy showed a general increase in both Th2 ( IL-4 ) , regulatory ( IL-10 ) and Th1 ( IFN-γ ) responses to parasite antigens , which was especially marked in increased IFN-γ production by mice vaccinated with Sm-TSP-2/5B compared to those vaccinated with the control non-parasite protein MBP . This implies that every animal was effectively challenged , indicating that the recovery of very few or no parasites in some mice was not due to an unsuccessful infection but successful vaccination . These data also suggest that Th1 cytokines have a role in the protective response against schistosomiasis , a finding that has been documented in infection studies with the parasite [26] , [27] and vaccination experiments with recombinant vaccine candidate antigens from the tegument such as Sm29 [28] and Sm14 [29] . A caveat of using human-approved adjuvants to test vaccine antigens in the early stages of process development is that the full potential of a candidate antigen may not be realized due to the increased immunostimulatory properties of adjuvants containing mycobacteria and other toxic components , such as Freund's adjuvants . However , the levels of protection reported herein for Sm-TSP-2/5B were similar to those reported for Freund's formulated thioredoxin-Sm-TSP-2 , and well exceed the 40% benchmark set by the WHO for progression of an antigen into clinical trials irrespective of the adjuvant used [30] . Sm-TSP-2 immunolocalizes to the surface of schistosomula [16] and adult worms [12] and has been found in the outer tegument of mature schistosomes [10] in abundance using proteomic techniques [31] . The ultrastructural morphology of adult worms and schistosomula treated in vitro with Sm-tsp-2 double-stranded RNA displayed a distinctly vacuolated and thinner tegument compared to controls , suggesting that Sm-TSP-2 may play a pivotal role in tegument development in the early stages of intra-mammalian development [16] . These insights into Sm-TSP-2 function , along with the apparent importance of humoral immunity in anti-Sm-TSP-2 vaccination , lead us to hypothesize that the surface of the schistosomulum and adult fluke are potential sites of immune attack where these crucially important membranes are being opsonized by anti-Sm-TSP-2 antibodies for further attack by complement , antibody-dependent cellular mechanisms , or both . We are currently exploring the immunologic mechanisms responsible for vaccine-induced efficacy using genetically modified mice . The Sm-TSP-2-based vaccine antigens reported in this study appear to exhibit all the early-stage characteristics of a vaccine targeting developing countries where schistosomiasis is endemic , based on their ease of production , absence of IgE reactivity , preferential recognition by resistant humans [12] , essential nature of the protein for parasite survival [16] and vaccine efficacy in animal models . These features , coupled with the recent finding of a lack of polymorphism between geographical isolates of Sm-TSP-2 throughout Africa [32] provide a compelling argument for the use of Sm-TSP-2-based antigens as safe and effective anti-schistosomiasis vaccines . These additional studies also open the door to exploring more than a single helminth target with a single antigen . | There are currently no vaccines available to combat helminth ( worm ) infections in humans . The most devastating of the diseases caused by human helminths are schistosomiasis ( or bilharzia ) and hookworm disease . By fusing one of the lead schistosomiasis vaccine antigens , Sm-TSP-2 , with a protective fragment from one of the lead hookworm vaccine antigens , Na-APR-1 , we have produced a chimeric vaccine , termed Sm-TSP-2/5B that might provide protection against two debilitating and co-endemic neglected tropical diseases . Sm-TSP-2/5B provided increased protection compared to Sm-TSP-2 alone when formulated with human approved adjuvants and tested in a mouse model of schistosomiasis . Moreover , IgE against Sm-TSP-2 or Na-APR-1 has not been detected in the blood of residents from an area in Brazil that is endemic for schistosomes and hookworms , indicating that vaccines based on these molecules would be unlikely to generate allergic reactions in recipients from developing countries . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases"
] | 2012 | Enhanced Protective Efficacy of a Chimeric Form of the Schistosomiasis Vaccine Antigen Sm-TSP-2 |
Sir2 is an NAD+-dependent histone deacetylase required to mediate transcriptional silencing and suppress rDNA recombination in budding yeast . We previously identified Tdh3 , a glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) , as a high expression suppressor of the lethality caused by Sir2 overexpression in yeast cells . Here we show that Tdh3 interacts with Sir2 , localizes to silent chromatin in a Sir2-dependent manner , and promotes normal silencing at the telomere and rDNA . Characterization of specific TDH3 alleles suggests that Tdh3's influence on silencing requires nuclear localization but does not correlate with its catalytic activity . Interestingly , a genetic assay suggests that Tdh3 , an NAD+-binding protein , influences nuclear NAD+ levels; we speculate that Tdh3 links nuclear Sir2 with NAD+ from the cytoplasm .
The yeast Sir2 protein is the founding member of a large family of NAD+-dependent protein deacetylases ( “sirtuins” ) conserved among all three domains of life [1] , [2] . Yeast Sir2 deacetylates histones , particularly lysine 16 of histone H4 , as part of a silencing mechanism that suppresses the transcription of telomere-proximal genes and the silent mating type loci . At these locations , Sir2 acts in conjunction with the Sir3 and Sir4 proteins [3] , [4] . Sir2 also acts to reduce recombination and silence expression of RNA polymerase II transcribed genes at the rDNA repeats [5] , [6] , [7] . Sir2 family members in yeast and other organisms have both histone and non-histone substrates and regulate a variety of cellular processes . Sir2 and other sirtuins link cleavage of NAD+ to their deacetylation reaction . Sir2's NAD+-dependence led to the suggestion that it might be regulated by changes in metabolism that affect NAD+ concentrations [2] , [8] , [9] . In support of this proposal , Sir2-related functions can be affected by manipulating the levels of enzymes in the NAD+ biosynthetic pathway , or by varying the concentrations of NAD+ precursors in the growth media . For example , NAD+ levels are reduced in yeast cells lacking the NPT1 gene , which codes for a key enzyme in the salvage pathway , reforming NAD+ from nicotinic acid [10] . This drop in NAD+ is accompanied by a decrease in rDNA and telomeric silencing and an increase in rDNA recombination [10] . Addition of the NAD+ precursor nicotinamide riboside restores NAD+ levels in npt1 mutants and also suppresses their rDNA silencing and recombination defects in a Sir2-dependent manner [11] . In a prior genetic screen for candidate Sir2 regulators we identified Tdh3 , a yeast glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) , which converts NAD+ to NADH while executing a key step in glycolysis [12] . Given the links between metabolism , NAD+ , and Sir2 activity , we investigated possible influences of this protein on Sir2 . We found that yeast Tdh3 is a Sir2-interacting protein that regulates silencing , influences Sir2's association with chromatin , and modulates nuclear NAD+ levels .
There are three GAPDH enzymes in yeast , coded for by the TDH1 , TDH2 , and TDH3 genes [13] , 14 . Deletion of any one of the three TDH genes is not lethal , but elimination of both TDH2 and TDH3 causes inviability , indicating these genes have a redundant , essential function [14]; the Tdh1 protein appears to be exclusively expressed in stationary phase [15] , [16] , and can be deleted in combination with either Tdh2 or Tdh3 without compromising viability . To examine whether GAPDH enzymes influence silencing in yeast we deleted TDH1 , TDH2 , or TDH3 in a strain bearing a URA3 reporter gene at the telomere [17] . We observed that deletion of TDH3 caused a decrease in telomeric silencing ( Figure 1A ) . Loss of Tdh1 or Tdh2 did not lead to strong phenotypes in this assay . Since we initially identified TDH3 by its overexpression phenotype we also determined its influence on silencing when expressed at high levels . We transformed a plasmid containing the TDH3 gene under the control of GAL1 promoter into a strain containing the ADE2 gene integrated at the HMR locus . In this assay we find that silencing of the ADE2 gene is improved in strains overexpressing TDH3 ( Figure 1B ) . Since phenotypic assays based on the URA3 reporter gene may in some cases be subject to influences independent of transcriptional silencing [18] , [19] , we also examined Tdh3's influence on the transcription of a naturally occurring telomere-linked gene , YFR057W ( Figure 1C ) [20] . An increase in YFR057W's mRNA levels in strains lacking Sir2 indicates that this gene is subject to Sir-dependent silencing ( Figure 1C ) . We observed that loss of Tdh3 caused a significant increase in the expression of this gene , consistent with a role for Tdh3 in mediating telomere position effect . Control experiments indicated that deletion or overexpression of Tdh3 did not alter Sir2 levels in the cell ( Figure 1D ) . Sir2 regulates recombination and RNA polymerase II transcription at the rDNA . To examine the influence of Tdh3 on silencing and recombination at the rDNA locus , we monitored the expression of a URA3 reporter gene integrated into the rDNA [6] . Based on the pattern of growth on the FOA assay plates , which counterselect for URA3 expression , loss of Tdh3 leads to a decrease in rDNA silencing and/or increased loss of the URA3 marker ( Figure 2A ) . To determine if Tdh3 affects recombination at the rDNA we used fluctuation analysis to measure the loss of the URA3 marker from the rDNA repeats ( Figure 2B ) . In agreement with prior studies we find that deletion of Sir2 increases the rate of loss of the rDNA marker [6] . We also observe a significant increase in recombination in strains lacking Tdh3 . Loss of Sir2 in a Δtdh3 strain does not cause an additive increase in the recombination rate , suggesting that Sir2 and Tdh3 act in a common pathway to suppress rDNA recombination . Silencing may be influenced by flux through the glycolytic pathway , controlled in part by Tdh3 in yeast . To examine the relationship between Tdh3's enzymatic activity and its effect on silencing we assessed the effects of mutations in the TDH3 gene . We replaced the endogenous TDH3 gene with alleles predicted to code for proteins that reduce Tdh3's catalytic activity ( C150G ) [21] and/or to alter its multimeric state ( T227A , T227K ) [22] . These Tdh3 proteins were expressed at similar levels to wild type ( not shown ) . We then measured the effects of these mutants on cellular GAPDH activity and on silencing at the telomere ( Figure 3 ) . We found that GAPDH activity in the strains does not correlate with silencing efficiency . While the C150G amino acid substitution showed diminished GAPDH activity and also exhibited a decrease in silencing similar to cells lacking Tdh3 , the T227A change caused a silencing defect with no change in GAPDH activity . Finally , the T227K strain exhibited no change in silencing in the phenotypic assay ( Figure 3A ) , and only a slight loss of silencing as assessed by mRNA levels of a telomere proximal gene ( Figure 3B ) , despite a significant drop in GAPDH activity . Thus , Tdh3 likely contributes to silencing in a manner that is at least partly independent of its role in glycolysis . Interestingly , we observed that expression of specific Tdh3 mutants ( e . g . , C150G and T227K ) caused GAPDH activity to drop below levels seen in the Δtdh3 null strain ( Figure 3C ) . The active form of the GAPDH enzyme is a tetramer of GAPDH monomers . The existence of mixed Tdh2/Tdh3 tetramers has been suggested [13]; we speculate that expression of specific Tdh3 alleles could decrease overall GAPDH activity by recruiting Tdh2 into inactive complexes . We find that yeast GAPDH , which participates in glycolysis in the cytoplasm , also influences silencing and recombination in the nucleus . This influence could be indirect , reflecting in some way the key role these enzymes play in basic cell metabolism . However , GAPDH enzymes in other organisms have been shown to exist in the nucleus and execute functions independent of their role in glycolysis [21] , [23] , [24] . We examined the possibility that yeast Tdh3 protein is a nuclear factor in yeast with a direct role in silencing . We first used a strain expressing a Tdh3-GFP fusion protein to determine the cellular localization of Tdh3 . Monitoring GFP by fluorescence microscopy indicated that Tdh3 in present in both the nucleus and cytoplasm ( Figure 4A ) , consistent with reports from large-scale localization efforts [25] . We observed a similar pattern performing immunofluorescence of a strain expressing a Tdh3-myc fusion protein ( not shown ) . We next asked whether nuclear localization was important for Tdh3's function in silencing by fusing a nuclear export sequence ( NES ) to the C-terminus of Tdh3 . We used a 12 amino acid NES derived from the HIV Rev1 protein , previously shown to be functional in yeast [26] . As a control we fused Tdh3 to a non-functional sequence ( “nes” ) that differs at two key amino acid positions [27] . We created strains expressing this allele in otherwise wild-type strains , and in strains lacking the TDH2 gene . In both TDH2 and Δtdh2 strains , addition of NES or nes sequences to Tdh3 did not lead to noticeable changes in cell growth , nor did they significantly alter overall GAPDH levels in the cell ( Figure 3C ) . We did not observe a difference in silencing between the NES- and nes-tagged strains in an otherwise wild-type strain , but observed a significant , specific loss of silencing when the NES sequence is fused to Tdh3 in a strain lacking the Tdh2 protein ( Figure 4B ) . We used GFP-tagged versions of these strains to show that addition of the NES sequence in Δtdh2 strains , but not the nes sequence , led to a redistribution of Tdh3 protein ( Figure 4C ) . We did not observe a significant change in the distribution of Sir2 in these strains ( Supplementary Figure S1C ) . Overall these experiments suggest that Tdh3 is present in the nucleus , and that nuclear localization is important for its role in silencing . They also suggest that Tdh2 affects Tdh3's localization in the cell . Finally , we note that the Δtdh2 TDH3-NES strain that exhibits defective silencing has normal levels of GAPDH activity ( Figure 3C ) , further suggesting that Tdh3's contribution to silencing is independent of its ability to perform catalysis . To examine the possibility that Sir2 and Tdh3 physically interact , we fused Sir2 and Tdh3 to the DNA binding domain ( BD ) or transcriptional activation domain ( AD ) of the Gal4 protein and expressed the fusion proteins in a strain bearing Gal4 binding sites in the HIS3 promoter . In initial experiments we failed to see evidence of a Tdh3-Sir2 interaction , but we noticed that the Tdh3-BD protein significantly repressed basal expression of the HIS3 reporter gene ( Figure 5A ) . To determine if the repression mediated by Tdh3 required DNA binding , we expressed Tdh3 lacking the DNA binding domain . Basal HIS3 expression is restored in these conditions , suggesting that tethering Tdh3 caused transcriptional repression ( Figure 5A , lower panel ) . When Sir2 was tethered to the HIS3 promoter via fusion with the Gal4 DNA binding domain and Tdh3 was expressed as an activation domain fusion , we again failed to observe evidence of a Tdh3-Sir2 interaction . In these experiments tethered Sir2 alone does not repress the reporter , consistent with previous reports . However , expression of Tdh3 in conjunction with tethered Sir2 caused repression of HIS3 . Thus , increased Tdh3 in the cell appears to increase an intrinsic ability of Sir2 to mediate tethered silencing ( Figure 5B ) . The presence of a positive interaction in two hybrid assays can be masked by the ability of the query proteins to repress transcription of the reporter gene . To reduce this possibility we repeated the two-hybrid assay in a strain lacking the endogenous SIR2 , SIR3 , and SIR4 genes [28] . In contrast to the Sir+ strain , expression of the Tdh3-BD protein in the sir2 sir3 sir4 mutant strain does not alter basal expression of HIS3 ( Figure 5C ) . Finally , when the Tdh3-BD fusion is expressed along with Sir2-AD , we observed increased growth on –HIS media , indicating an interaction between the two proteins ( Figure 5C ) . As an independent approach to assess a possible Tdh3-Sir2 interaction we carried out a co-immunoprecipitation experiment . For this experiment we made a strain expressing a Tdh3-myc fusion protein , transribed from the endogenous TDH3 locus . Extracts were made from this strain , and from a control strain lacking the myc tag . Tdh3-myc and associated proteins were separated from crude cellular extracts using antibodies to myc conjugated to agarose beads . Western blotting demonstrated that Tdh3-myc was specifically detected in the cell lysate and in immunopurified fractions ( Figure 6 , left panel ) . We then ran the immunopurified material and conducted a western blot using an antibody to Sir2 . The right panel of Figure 6 demonstrates that we readily detected Sir2 in immunoprecipitations from strains with tagged Tdh3 , but not from control lysates treated identically but from strains lacking the myc tag on Tdh3 . Our results are consistent with the results of a systematic mass spectrometry study that also suggested the existence of a complex containing Tdh3 and Sir2 [29] . Interestingly , we have failed to observe a Sir2-Tdh2 interaction under the same conditions ( R . Ryznar , unpublished ) . Thus , our two hybrid and co-immunoprecipitation results indicate that Tdh3 specifically associates with Sir2 in yeast . To examine the possibility that Tdh3 is a chromatin protein , we conducted chromatin immunoprecipitation ( ChIP ) experiments using a strain expressing a Tdh3-myc fusion protein . Using probes to the non-transcribed spacer ( NTS ) regions of the rDNA and a telomere proximal sequence , we found that Tdh3 is specifically associated with these regions of the chromosome ( Figure 7A ) . We next determined whether Tdh3 association with chromatin depended on the presence of Sir2 by repeating these measurements in a Δsir2 strain . We find that association of Tdh3 is eliminated at the telomere and strongly reduced at the rDNA in strains lacking Sir2 . We then conducted the reciprocal experiment , examining the association of Sir2 with the rDNA and telomeres in strains lacking the TDH3 gene ( Figure 7B ) . In these experiments we observe a reduction of Sir2 association with telomeres , but don't observe a significant decrease at the rDNA ( Figure 7B ) . Therefore , Tdh3 is a chromatin protein that regulates the ability of Sir2 to associate with some silent loci . Sir2 requires NAD for its enzymatic activity , and mutations in genes that affect NAD+ biosynthesis are known to influence silencing [10] , [11] . GAPDH enzymes bind NAD+ to catalyze a key step in glycolysis in which NAD+ is reduced to NADH . Tdh3 could be affecting Sir2 activity by influencing NAD+ levels in the cell . To examine whether Tdh3 gene dosage affects overall cellular NAD+ levels , we measured cellular NAD+ in strains lacking or overexpressing Tdh3 ( Figure 8A ) . As a control for these experiments , we also determined the relative levels of NAD+ in a strain lacking the NPT1 gene , a mutation reported to decrease cellular NAD+ [10] . We readily detected a decrease in NAD+ levels in the Δnpt1 strain relative to its wild-type control , but failed to detect a significant change in strains lacking Tdh3 ( Figure 8A , left panel ) or overexpressing Tdh3 ( Figure 8A , right panel ) . Several studies suggest that NAD+ concentration may vary depending on cellular compartment [30] . To examine the possibility that Tdh3 specifically affects levels of NAD+ within the nucleus , we used the NAD+-sensitive transcriptional reporter described by Anderson et al [31] . In this strain the bacterial NadR protein is fused to the Gal4 activation domain , while binding sites for NadR are present in the HIS3 gene promoter . NadR's binding to DNA depends on the presence of NAD+; thus , transcription of HIS3 is tightly linked to nuclear NAD+ availability ( Figure 8B ) . We used this assay to measure the effects of eliminating Tdh1 , Tdh2 , Tdh3 , Sir2 , or Bna6 , an enzyme known to influence nuclear NAD+ levels [31] . We observed a significant and specific decrease in reporter expression in a strain lacking the TDH3 gene , suggesting that the Tdh3 protein helps maintain normal nuclear NAD+ levels ( Figure 8B ) . HIS3 expression is also reduced in this assay in Δtdh2 TDH3-NES and Δtdh2 TDH3-nes strains ( Supplementary Figure S4 ) . Proteins contributing to common pathways in the cell can often be identified by defining synthetic phenotypes caused by combining mutations in the genes for these proteins [32] . To further examine Tdh3's possible role in maintaining cellular NAD+ levels we created strains combining TDH3 deletions with the loss of genes involved in the synthesis of NAD+ , and then compared the doubling times of strains containing the single and double mutations . Interestingly , we observed a significant slow-growth phenotype in a strain lacking both the TDH3 and NPT1 genes ( Figure 8C ) , consistent with an observation made in a large-scale assay [33] . We detected a similar growth defect in a Δtdh2 Δnpt1 strain ( Figure 8C ) . Npt1 is largely found in the nucleus [10] , [34] , where it participates in the salvage pathway of NAD+ synthesis . Consistent with prior studies [10] , [35] , we observed that Δnpt1 strains exhibited silencing defects; we also found that cells lacking both TDH3 and NPT1 have silencing defects similar to those seen in Δnpt1 or Δtdh3 strains ( Figures 1C and 8D ) .
GAPDH is a well-described “moonlighting” protein , shown to have diverse functions independent of its role in glycolysis [23] , [36] . These functions may include a conserved interaction with Sir2 family members , as GAPDH enzymes have been shown to interact with sirtuins in other organisms . In Drosophila , a large-scale two-hybrid interaction study indicated an interaction between GAPDH and dSir2 [37] , while in human cells the nitrosylated form of GAPDH was shown to bind to SIRT1 , the closest human homologue to yeast Sir2 , and lead to SIRT1 nitrosylation [38] . GAPDH translocation to the nucleus promotes apoptosis in mammalian cells; an independent study found that SIRT1 depletion led to nuclear translocation of GAPDH in the absence of apoptotic stress [39] . Sir2-GAPDH links have also been observed in yeast cells . A recent report found that Sir2 and the Sir2 homolog Hst1 associate with the open reading frame of TDH3 and several other glycolysis genes , and may mediate repression of these genes following the diauxic shift [40] . Overexpressing Sir2 in GAPDH-deficient yeast cells caused elevated plasmid recombination [41] , prompting a proposal that GAPDH enzymes influence Sir2 activity , possibly by affecting availability of its cofactor , NAD+ [41] , [42] . We previously identified Tdh3 in a screen for possible regulators or substrates of Sir2 [12] . Here we report that strains lacking Tdh3 have defects in telomere position effect and rDNA silencing . We also found that Tdh3 physically interacts with Sir2 , and specifically binds to both telomeres and rDNA sequences in a Sir2-dependent manner . Finally , Sir2's association with telomeres was reduced in strains lacking Tdh3 . Taken together , these observations suggest that Tdh3 acts directly at the sites of Sir2 action to influence silencing . Our experiments suggest that Tdh3 promotes silencing in yeast cells independently of its role in glycolysis . First , Tdh3's silencing activity was decreased by the addition of sequences that promoted its export from the nucleus . Thus , unlike its function in glycolysis , Tdh3's role in silencing likely occurs in the nucleus . Second , our analysis of a small set of Tdh3 mutants indicated that its ability to promote silencing did not correlate with catalytic activity . Given its association with Sir2 at its chromatin targets , Tdh3 may affect silencing directly by influencing Sir2's catalytic activity or its interaction with other silencing factors . Since Tdh3 is an NAD+-binding protein that reduces NAD+ to NADH during glycolysis , we also investigated this possible link to Sir2 . While we observed that overall NAD+ levels are unchanged in cells lacking Tdh3 , using an NAD+-sensitive reporter assay we found that Tdh3 is specifically required to maintain normal levels of NAD+ in the nucleus . This result is consistent with the proposal that NAD+ is non-uniformly distributed within the cell , in part due to compartmentalization of enzymes responsible for NAD+ synthesis or consumption [30] . For instance , the yeast Npt1 enzyme involved in the NAD+ salvage pathway in yeast is preferentially found in the nucleus [10] , [34] . The effect of Tdh3 on nuclear NAD+ levels suggests that this GAPDH protein may influence Sir2-dependent silencing by affecting the level of NAD+ available to Sir2 . The Km for NAD+ in Sir2's deacetylase reaction is approximately 30 µm [43] while the concentration of NAD+ in yeast is between 1 and 2 mM [11] . However , genetic alterations in NAD+ biosynthetic enzymes that cause silencing defects do not reduce NAD+ concentrations below 1 mM; this suggests that most of the NAD+ in the cell is not freely available , and is likely protein bound [11] , [44] . Perhaps the NAD+ bound to Tdh3 , one of the most abundant proteins in the cell , is specifically accessible to Sir2 within the nucleus . We observed that both the Δtdh2 TDH3-NES and Δtdh2 TDH3-nes strains exhibited nuclear NAD deficits , as assessed by the NadR reporter system , yet a silencing phenotype was specifically observed in the TDH3-NES strain , in which Tdh3's nuclear localization is reduced . Thus , silencing may be sensitive to the presence of NAD+-bound Tdh3 at silenced locations , rather than overall nuclear NAD levels . Finally , we note that the C150G amino acid substitution in Tdh3 that eliminates catalytic activity and which is defective in silencing is also predicted to be deficient in NAD+ binding [44] , [45] . Due to its role in regulating aging in yeast and in other organisms , particularly for its proposed role in mediating the effects of calorie restriction in the aging pathway , potential links between metabolism and Sir2 function have been actively sought [2] , [8] , [9] , [42] . The effects of calorie restriction ( CR ) on yeast lifespan act through Sir2-dependent and Sir2 independent mechanisms [45] , [46] , and it is not clear if CR influences Sir2 activity by modulating NAD+ levels [45] , [47] , [48] , [49] . We have found that Tdh3 has functions in basic cell metabolism and control of Sir2-induced transcriptional silencing . Tdh3 thus exhibits the hallmarks of a factor that could link cellular metabolism with Sir2-dependent silencing .
Strains used in this study are listed in Table 1 . Genes were eliminated by PCR-mediated gene deletion [50] , using MX-series plasmids as templates [51] . Epitope tags were fused to the 3′ end of targeted via PCR-mediated insertion using plasmid pYM5 as template [52] . To introduce mutated alleles of the TDH3 gene a strain was made in which TDH3 was replaced by the pCORE construct [53] . DNA fragments containing specific point mutations in TDH3 were made by hybrid PCR [54] and used to transplace the pCORE sequences . Alleles were confirmed by sequencing . Nuclear export sequences were fused to the 3′ end of TDH3 by transforming a DNA fragment with 3′ homology to the TDH3 ORF , the nuclear export sequence , and an hphMX4 sequence into the appropriate yeast strain . Strains lacking both TDH3 and specific NAD+ biosynthetic genes were generated by crossing Δtdh3 strain YSH969 with selected strains from the yeast deletion collection [55]; following sporulation haploid strains were identified by selecting for histidine auxotrophs [56] . Semisquash preparations were adapted from published protocols [57] , [58] with minor modifications [59] . Immunostaining was performed using a mouse monoclonal antibody against Nsp1p ( ab4641; Abcam ) at a 1∶100 dilution to mark the nuclear periphery and Alexa Fluor 568–goat anti-mouse IgG ( H+L ) ( A11004; Molecular Probes ) at a 1∶200 dilution as the secondary antibody . A chicken monoclonal antibody against GFP ( ab13970; Abcam ) at a 1∶100 dilution was used to recognize the Tdh3-nes-GFP or Tdh3-NES-GFP fusion constructs and FITC conjugate from Jackson Immuno Research at 1∶200 dilution was used as the secondary antibody . Nuclear to cytoplasmic ratio of GFP fluorescence was determined using the arbitrary line tool of Softworx software , in conjunction with the Deltavision RT imaging system ( Applied Precision ) adapted to an Olympus ( IX70 ) microscope . Image stacks at 0 . 2-µm spacing were acquired along the z axis . The line tool was used to generate GFP fluorescence histogram profiles reflecting relative fluorescence units of the nucleus as compared to the cytoplasm . ChIP was performed as previously described [60] . Yeast cell growth and chromatin preparation were performed as described [61] . Prior to the addition of antibody for precipitation , 50 µl of lysate was precleared with 7 µl of Protein A magnetic beads ( New England Biolabs ) by incubating at 4°C for 30–60 minutes on a Labquake tube rotator . The samples were applied to a magnet to separate the beads from the supernatant; the supernatant was transferred to a new eppendorf tube and 1 µl myc-epitope antibody ( 9B11; Cell Signaling Technology ) was added for an overnight incubation at 4°C ) . 15 µl of Protein A magnetic beads were added to precipitate the chromatin . Control ( mock ) immunoprecipitations were conducted in an identical manner , but without the addition of antibody . Immunoprecipitated , control , and input DNAs were analyzed by quantitative PCR analysis . Serial dilutions of the whole cell lysate ( from 1∶5 to 1∶1250 ) and immunoprecipitates ( from 1∶2 to 1∶625 ) were used in a standard Taq PCR to determine a linear range for the samples , using the following cycling parameters: 94°C for 4 min; 30 cycles of 94°C for 30 s , 50°C for 30 s , and 72°C for 1 . 5 min; and 72°C for 5 min . For control detection of ACT1 DNA 25 cycles of PCR was used . Data was derived only from amplifications performed within the linear range . Primers flanking non-transcribed rDNA spacers NTS1 and NTS2 were used to determine enrichment at the rDNA repeats; primers located 1 . 0 kb and immediately adjacent to Tel V were used to determine telomeric enrichment . Primer sequences are shown in Supplementary Table S1 . PCR products were run on 5% native polyacrylamide gel electrophoresis and stained with SYBR Gold ( Invitrogen ) . Gels were scanned on a Storm 860 phosphorimager and quantitated using ImageQuant software ( Molecular Dynamics , Inc . ; Sunnyvale , CA ) . A sequence within the ACT1 open reading frame was used was an internal control in all experiments . Each reported value represents the average of at least three independent ChIP experiments . For the data shown in Figure 2 the signal from each mock immunoprecipitation experiment was subtracted from the value derived from the experimental immunoprecipitation; values were then normalized to the signal observed from input DNA for each individual experiment , and then expressed as a ratio to the normalized ACT1 value from the same experiment . The data is alternatively presented in Supplementary Figure S2 as the percentage of input chromatin precipitated , in which the signal observed from mock immunoprecipitations is reported separately . For western blots protein was isolated from yeast cells as described [62] . 5 µg ( for TDH3-myc probe ) or 10 µg ( for SIR2-myc probe ) of protein was loaded onto a 5% resolving gel and 10% running gel . Protein was transferred to a nitrocellulose membrane and primary antibody applied for one hour at room temperature in 5% non-fat dry milk plus 0 . 1%Tween TBS solution . Anti-c-Myc ( clone 9E11 from Chemicon International ) was used at 1∶250 dilution . Secondary antibody ( goat anti-mouse from Santa Cruz Biotechnology at 1∶3000 dilution ) was applied for one hour at room temperature in the same solution . Detection was performed using the ECL Western Blotting Reagents from Amersham according to the manufacturer's specifications . Chemiluminescence was measured on a Storm PhosphorImager using the blue channel at 200 micron resolution . For co-immunoprecipitation experiments a yeast extract was made from cells as previously described [62] ) , except that the triton X-100 was added to the lysis buffer to 1 . 5% . For immunoprecipitations 40 to 100 µl of the 1∶1 suspension of the anti-Myc agarose conjugate ( Sigma ) was added to a microcentrifuge tube . The resin was allowed to settle by a short microfuge spin . Liquid was discarded and washed 5 times with 1 ml ice cold PBS . Yeast cell lysate was added to the settled resin . Volume was brought to at least 200 µl ( 60–80 µg total protein ) . Tubes were incubated overnight on an orbital shaker at 4°C . The resin was washed 4 times with 1 ml of PBS . After the final wash , the supernatant was aspirated and ∼10 µl was left above the beads . 20 to 50 µl of 2× SDS sample buffer was added to the tube . The tube was incubated for 10 minutes at 92°C with frequent agitation , vortexed , and then centrifuged for 5 seconds . Carefully avoiding the agarose , the supernatant was transferred to a new tube and boiled for 5 minutes . Protein concentration was determined by Bradford assay; 20–40 µg was loaded into an SDS-PAGE gel and ran at 110 volts for 1 . 5 hours . Detection of the c-Myc-tagged fusion protein was determined by immunoblotting , using monoclonal anti-c-Myc for cell lysate and IP at the recommended concentration . For detection of Sir2 bound to myc tagged protein , Santa Cruz sc 2020 Sir2 antibody was used at a concentration of 1∶20 . Blots were scanned using a SynGene apparatus . Assays for GAPDH activity were performed as previously described [63] with the modifications described by Ralser et al . [64] . Cell fractionation was carried out as described [65]; western blotting of cell fractions was performed using antibodies to GFP ( Abcam ab13970 ) and histone H3 ( Abcam ab17911 ) . RNA was extracted using the hot acidic phenol extraction method ( Ausubel et al 1993 ) . DNAse treatment was carried out using Ambion's RNAse-free DNAse I and reaction buffer for degrading DNA ( Catalog #1906 ) . 1 µg of RNA was used in a total of 16 µl of DEPC deionized water in a microcentrifuge tube . The sample was heated for 3 minutes at 95°C and then placed on ice for 3–5 minutes . 2 µl of 10× DNAse I buffer and 2 µl DNAse I was added and the tubes incubated at 37°C for one hour . To remove the DNAse and divalent cations that can catalyze heat-mediated degradation of RNA , 5 µl of DNAse inactivation reagent was added to the tubes and the samples were mixed well . The tubes were incubated at room temperature for two minutes during which the tubes were flicked once to re-disperse the slurry . The tubes were then microcentrifuged at room temperature for two minutes to pellet the DNAse inactivation reagent . The DNAse treated RNA was transferred to a new tube and stored at −20°C . cDNA synthesis was carried out using Ambion's Retroscript kit ( Catalog #1710 ) . To prepare cDNA from RNA , 5 µl of the DNAse treated RNA was transferred to a new microcentrifuge tube . 1 µl of oligo ( dT ) primer ( 50 µM ) was added to each tube and the samples then incubated at 85°C for 3 minutes . The tubes were then placed on ice for 3 minutes and microcentrifuged briefly at 4°C . 1 µl of RT buffer , 2 µl of dNTP mix , 0 . 5 µl reverse transcriptase and 0 . 5 µl RNAse inhibitor were added to each tube . After vortexing the tubes well , the tubes were then incubated for 60–90 minutes at 42°C and then heated at 92°C for 10 minutes . The cDNA was then spun down in a microcentrifuge at 4°C to collect the condensate . 0 . 6 µl was used for PCR; cycling conditions were 94°C for 4 minutes and then 25 cycles ( for ACT1 ) or 35 cycles ( YFR057W ) of 94°C for 30 seconds , 50°C for 30 seconds and 72°C for 90 seconds , followed by a final cycle for 72° for 5 minutes . Primer sequences are shown in Supplementary Figure S1; primer sequences useful for detecting YFR057W were previously described [66] . | Cells respond to changing signals or environmental conditions by altering the expression of their genes . For instance , our cells respond to the presence of glucose or insulin in the bloodstream by regulating the expression of genes involved in basic cell metabolism . The sirtuin family of proteins has been proposed to serve as a link between a cell's metabolic state and gene expression , although the molecular mechanisms that connect metabolic status with Sir2 activity remain unclear . The expression of genes is controlled in part by the structural organization of the local chromatin region within which they reside . The yeast sirtuin protein , Sir2 , mediates repression ( “silencing” ) of sets of genes by modulating the structural organization of specific chromatin regions . In this study we describe a novel link between a key metabolic enzyme and Sir2 function . We show that a yeast GAPDH protein , which plays a central role in glucose metabolism , also associates with Sir2 in the nucleus and promotes Sir2-dependent gene silencing . Sirtuin activity requires a small molecule , NAD+ , whose availability may fluctuate depending on the metabolic state of the cell . Based on our data , we suggest that Tdh3 may promote silencing by maintaining sufficient levels of NAD+ available to Sir2 within the nucleus . | [
"Abstract",
"Introduction",
"Results",
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"and",
"Methods"
] | [] | 2013 | Yeast Tdh3 (Glyceraldehyde 3-Phosphate Dehydrogenase) Is a Sir2-Interacting Factor That Regulates Transcriptional Silencing and rDNA Recombination |
Kingella kingae is an encapsulated gram-negative organism that is a common cause of osteoarticular infections in young children . In earlier work , we identified a glycosyltransferase gene called csaA that is necessary for synthesis of the [3 ) -β-GalpNAc- ( 1→5 ) -β-Kdop- ( 2→] polysaccharide capsule ( type a ) in K . kingae strain 269–492 . In the current study , we analyzed a large collection of invasive and carrier isolates from Israel and found that csaA was present in only 47% of the isolates . Further examination of this collection using primers based on the sequence that flanks csaA revealed three additional gene clusters ( designated the csb , csc , and csd loci ) , all encoding predicted glycosyltransferases . The csb locus contains the csbA , csbB , and csbC genes and is associated with a capsule that is a polymer of [6 ) -α-GlcpNAc- ( 1→5 ) -β- ( 8-OAc ) Kdop- ( 2→] ( type b ) . The csc locus contains the cscA , cscB , and cscC genes and is associated with a capsule that is a polymer of [3 ) -β-Ribf- ( 1→2 ) -β-Ribf- ( 1→2 ) -β-Ribf- ( 1→4 ) -β-Kdop- ( 2→] ( type c ) . The csd locus contains the csdA , csdB , and csdC genes and is associated with a capsule that is a polymer of [P- ( O→3 ) [β-Galp- ( 1→4 ) ]-β-GlcpNAc- ( 1→3 ) -α-GlcpNAc-1-] ( type d ) . Introduction of the csa , csb , csc , and csd loci into strain KK01Δcsa , a strain 269–492 derivative that lacks the native csaA gene , was sufficient to produce the type a capsule , type b capsule , type c capsule , and type d capsule , respectively , indicating that these loci are solely responsible for determining capsule type in K . kingae . Further analysis demonstrated that 96% of the invasive isolates express either the type a or type b capsule and that a disproportionate percentage of carrier isolates express the type c or type d capsule . These results establish that there are at least four structurally distinct K . kingae capsule types and suggest that capsule type plays an important role in promoting K . kingae invasive disease .
Kingella kingae is being recognized increasingly as an important cause of bone and joint infections in young children , reflecting more sensitive cultivation techniques and increased availability of molecular-based diagnostic tools [1 , 2] . Among the key surface factors expressed by K . kingae is a polysaccharide capsule [3 , 4] . Capsules are recognized as important virulence factors in many gram-positive and gram-negative bacteria and have a variety of functions , including inhibiting complement deposition , reducing phagocytosis , and preventing desiccation [5–7] . Polysaccharide capsules conjugated to an immunogenic carrier protein also serve as effective vaccine antigens and have dramatically reduced morbidity and mortality caused by bacteria such as Streptococcus pneumoniae [8] , Haemophilus influenzae type b [9] , and Neisseria meningitidis [10] . In previous work , we described the structure of the capsule expressed by K . kingae strain 296–492 as a polymer of [3 ) -β-GalpNAc- ( 1→5 ) -β-Kdop- ( 2→] and identified the genes essential for capsule synthesis , assembly , and export [3 , 11] . In the course of this work , we established that the CsaA glycosyltransferase contains both a GalNAc-transferase domain and a Kdo-transferase domain and is sufficient for creating both the β-GalpNAc- ( 1→5 ) -β-Kdop linkage and the β-Kdop- ( 2→3 ) -β-GalpNAc linkage . In addition , the CsaA glycosyltransferase may catalyze addition of β-GalpNAc to the terminal β-Kdo residue of the poly-β-Kdo linker [3] . Bendaoud et al . recently reported that the structure of the capsule isolated from the surface of another K . kingae strain is a polymer of [6 ) -α-GlcpNAc- ( 1→5 ) -β-Kdop- ( 2→] [12] , suggesting the existence of at least two different K . kingae capsule types . The presence of multiple capsule types is well documented in a variety of bacterial species , with examples including S . pneumoniae , N . meningitidis , H . influenzae , and Klebsiella pneumoniae . In some cases , specific capsule types are associated more commonly with carriage or more commonly with invasive disease . For example , there are at least 90 different S . pneumoniae capsule types , but 23 types account for more than 90% of invasive pneumococcal disease worldwide [13 , 14] . Similarly , in N . meningitidis 6 of the 13 characterized capsule types are responsible for 90% of invasive disease cases globally [15 , 16] . In K . pneumoniae , capsule types 1 , 3 , and 4 are associated with respiratory tract infection , and capsule types 9 and 10 are associated with urinary tract infection [17] . In this study , we set out to define the genetic and structural basis of capsule diversity in a large collection of K . kingae clinical isolates from Israel . In addition , we examined the relationship between specific capsule types and clinical presentations .
In initial experiments , we screened a collection of 417 Israeli invasive and carrier isolates for the presence of csaA , the capsule synthesis gene in our prototype K . kingae strain KK01 . Using csaA-specific primers and PCR , we found that only 47 percent of all isolates contained the csaA gene . We hypothesized that other capsule types exist and that the region containing csaA represents the K . kingae capsule synthesis locus and differs in genetic content depending on the enzymatic machinery required to synthesize a specific capsule polysaccharide structure . To test this hypothesis , we designed a forward primer annealing to arg , the gene upstream of csaA in strain KK01 , and a reverse primer annealing to hemB , the gene downstream of csaA in strain KK01 , to amplify across the suspected capsule synthesis locus . As shown in Fig 1A , amplification across this locus in a group of representative isolates in the collection yielded four different amplicon sizes . Restriction mapping of these amplicons with NruI revealed similar banding patterns for strains with the same amplicon size ( Fig 1B ) . Nucleotide sequencing of the amplicons from multiple strains with the same amplicon size revealed an absolute correlation between the amplicon size and the gene content , indicating that the four discrete amplicons represent four discrete loci ( Fig 1C ) . After determining the predicted open reading frames ( ORFs ) in each amplicon , we searched for the presence of predicted domains or motifs using BLASTP and PHYRE2 . The ~3500 bp amplicon contained only the csaA gene ( identical to our prototype strain KK01 ) and was named the csa locus . The ~4000 bp amplicon contained a gene encoding a predicted GT-B type glycosyltransferase with homology to a GlcNAc transferase ( designated csbA ) , a gene encoding a putative capsule synthesis enzyme with homology to a Kdo transferase ( designated csbB ) , and a gene encoding a putative enzyme with homology to an acetyltransferase ( designated csbC ) and was named the csb locus . The ~5000 bp amplicon contained two genes encoding putative enzymes with homology to halo-acid dehydrogenases ( designated cscA and cscB ) and a gene encoding a predicted glycosyltransferase ( designated cscC ) and was named the csc locus . The ~5500 bp amplicon contained a gene encoding a predicted galactosyltransferase ( csdA ) , a gene encoding a predicted GlcNAc transferase ( csdB ) , and a gene encoding a predicted GT-A type glycosyltransferase ( csdC ) and was named the csd locus . Based on the sequence of the four unique loci , specific internal primer pairs were generated , producing locus-specific amplicons , as shown in Fig 1D–1G . To confirm that each of the four capsule synthesis loci is associated with a specific capsule type , we examined the glycosyl composition of purified polysaccharide capsule from representative strains that contain either the csa , csb , csc , or csd locus . In order to eliminate contamination with the galactan exopolysaccharide produced by K . kingae , we first deleted the pam locus from these strains [11 , 18] . As summarized in Table 1 , strains KK01 , PYKK98 , and PYKK93 harbor the csa locus and produce a capsule containing GalNAc and Kdo , which we named capsule type a . Strains PYKK89 , PYKK121 , PYKK58 , and PYKK59 harbor the csb locus and produce a capsule that contains GlcNAc and Kdo , which we named capsule type b . Strains PYKK60 and D7674 harbor the csc locus and produce a capsule that contains ribose and Kdo , which we named capsule type c . Finally , strains E3339 , D7453 , and BB270 contain the csd locus and produce a capsule that contains galactose and GlcNAc , which we named capsule type d . Considered together , these findings demonstrate complete agreement between the capsule synthesis locus and capsule glycosyl composition , indicating that genetic screening of the capsule synthesis locus is predictive of capsule type . In previous work , we reported that the type a polysaccharide capsule is a polymer of [3 ) -β-GalpNAc- ( 1→5 ) -β-Kdop- ( 2→] [11] . To determine the chemical structure of the K . kingae type b , type c , and type d capsules , surface polysaccharide was purified from derivatives of strains PYKK58 ( type b ) , PYKK60 ( type c ) , and BB270 ( type d ) lacking the pam locus and was analyzed with a combination of linkage analysis and 1-D and 2-D NMR spectroscopy . Linkage analysis of the type b capsule gave 1 , 5 , 6-tri-O-acetyl-2-deoxy-2-methylacetamido-3 , 4-di-O-methyl-1-2H-glucitol , derived from 6-linked GlcpNAc , and 1 , 2 , 5 , 6-tetra-O-acetyl-3-deoxy-4 , 7 , 8-tri-O-methyl-1 , 1 , 2-tri-2H-octitol , derived from 5-linked Kdo ( S1A and S2A Figs ) . Absolute configuration analysis gave D-GlcNAc . Characteristic peaks in the 1-D proton spectrum ( Fig 2A ) included one major anomeric signal at 5 . 08 ppm , two signals corresponding to the H-3 protons of Kdo , one N-acetyl peak from GlcNAc , and one O-acetyl of unknown origin . Tracing the connectivities of GlcNAc from H-1 and of Kdo from H-3 in the COSY and TOCSY spectra together with the carbon chemical shifts derived from the HSQC spectrum led to the complete assignment of the chemical shifts belonging to each residue ( Table 2 and Fig 3A ) . Due to the high molecular weight of the sample , the peaks in the spectra were broad and not suitable to measure proton-proton coupling constants for the determination of the anomeric configurations of GlcNAc and Kdo . However , the proton and carbon chemical shifts of the GlcNAc residue agreed with the α-configuration . Comparison of the chemical shifts of the Kdo residue with literature values [19] showed that Kdo was in the β-configuration . The downfield displacement of carbon chemical shifts GlcNAc-C6 and Kdo-C5 indicated the linkage positions as 6-linked GlcNAc and 5-linked Kdo . The downfield displacement of the proton chemical shifts of Kdo-H8 together with the intensity ( 3H ) and chemical shifts of the O-acetyl signal ( 2 . 13/23 . 1 ppm ) indicated acetylation on O-8 of Kdo . Taken together , these results indicated that the polymer is composed of a disaccharide repeating unit with the structure [6 ) -α-D-GlcpNAc- ( 1→5 ) -β- ( 8-OAc ) Kdop- ( 2→] . Linkage analysis of the type c capsule gave 1 , 3 , 4-tri-O-acetyl-2 , 5-di-O-methyl-1-2H-ribitol , derived from 3-linked ribofuranose , 1 , 2 , 4-tri-O-acetyl-3 , 5-di-O-methyl-1-2H-ribitol , derived from 2-linked ribofuranose , and 1 , 2 , 4 , 6-tetra-O-acetyl-3-deoxy-5 , 7 , 8-tri-O-methyl-1 , 1 , 2-tri-2H-octitol , derived from 4-linked Kdo ( S1B and S2B Figs ) . Absolute configuration analysis gave D-ribose . Characteristic peaks in the 1-D proton NMR spectrum ( Fig 2B ) included three anomeric signals at 5 . 34 , 5 . 29 , and 5 . 18 ppm , two pairs of signals corresponding to the H-3 protons of Kdo , an acetyl methyl signal , and several resonances in the carbohydrate ring region . The presence of two sets of Kdo signals of unequal intensity ( ratio 2:3 ) together with the presence of an acetyl signal with an area three times that of the larger Kdo-H3 peak suggested that 60% of the Kdo residues in the polysaccharide were O-acetylated . To reduce the heterogeneity of the sample , we performed de-O-acetylation . The 1-D proton NMR spectrum of the de-O-acetylated material ( Fig 2C ) was simplified compared to the native polysaccharide and displayed only a single set of Kdo H-3 peaks . Tracing the connectivities of the three anomeric signals from H-1 and of Kdo from H-3 in the COSY and TOCSY spectra together with the carbon chemical shifts derived from the HSQC spectrum led to a complete chemical shift assignment and revealed the presence of two 2-linked and one 3-linked ribofuranose residues as well as one 4-linked Kdo residue ( Table 3 and Fig 3B ) . The proton and carbon chemical shifts of the ribose residues agreed with β-anomeric configuration [20 , 21] . Comparison of the chemical shifts of the Kdo residue with literature values [19] showed that Kdo also had the β-configuration . The NOESY ( S3 Fig ) and HMBC ( Fig 3B ) spectra showed inter-residue cross peaks , allowing the determination of the sequence of the four monosaccharide residues in the polysaccharide repeating unit . Thus , the three ribose anomeric protons were correlated with their respective non-reducing end neighbors in both NOESY and HMBC spectra , and C-2 of Kdo ( and of 8-OAc-Kdo ) was correlated in HMBC to H-3 of Residue C . Taken together , these results indicated that the polymer is composed of a tetrasaccharide repeating unit with the structure [3 ) -β-D-Ribf- ( 1→2 ) -β-D-Ribf- ( 1→2 ) -β-D-Ribf- ( 1→4 ) -β-Kdop- ( 2→] . The main PMAA derivatives found in the linkage analysis of type d capsule were 1 , 5-di-O-acetyl-2 , 3 , 4 , 6-tetra-O-methyl-1-2H-galactitol , derived from terminal galactopyranose , and 1 , 3 , 5-tri-O-acetyl-2-deoxy-2-methylacetamido-4 , 6-di-O-methyl-1-2H-glucitol , derived from 3-linked GlcpNAc ( S1C Fig ) . Absolute configuration analysis gave D-GlcNAc and D-galactose . The 1-D proton spectrum ( Fig 2D ) included two α-anomeric signals ( ratio 3 . 4:1 ) , a cluster of several β-anomeric signals , and a group of N-acetyl peaks . The β-anomeric cluster was resolved into four distinct resonances in the HSQC spectrum . Tracing the connectivities from these anomeric signals in the COSY and TOCSY spectra together with the carbon chemical shifts obtained from the HSQC spectrum allowed a complete assignment of a total of six different residues that were grouped into three pairs of residue types ( Table 4 and Fig 3C2 ) . The chemical shifts of two of the six residues identified them as 3-linked α-GlcNAc ( A and A’ ) , another two of the six as 3 , 4-linked β-GlcNAc ( B and B’ ) , and the final two of the six as terminal β-Gal ( C and C’ ) . This information suggested the presence of two similar trisaccharide repeating units in the polysaccharide . Inter-residue linkages were assigned from HMBC and NOESY correlations . The HMBC ( Fig 3C2 ) and NOE correlations ( S4 Fig ) between H1 of B/B’ and H3 of A/A’ and between H1 of C/C’ and H4 of B/B’ confirmed the presence of two slightly different 3 ) -[β-Gal- ( 1→4 ) ]-β-GlcNAc- ( 1→3 ) -α-GlcNAc- ( 1→ trisaccharides , but there were no HMBC or NOE correlations to H1 of A or H3 of B that would link these trisaccharides together . The unusual downfield shifts of A-H1 , B-H3 , and B’-H3 suggested substitution by an electronegative group , such as acetate , sulfate , or phosphate . The fact that no significant amount of 3 , 4-linked GlcNAc was detected in the linkage analysis supports the presence of phosphate , as a phosphorylated PMAA would not be detected in GC-MS due to low volatility . Indeed , 31P NMR showed signals at 0 . 42 and -1 . 20 ppm , consistent with phosphomono- and diesters , respectively ( S5 Fig ) . A 2D-1H-31P-HMQC spectrum confirmed the presence of phosphodiester and its attachment to O1 of Residue A and O3 of Residue B and of a phosphomonoester and its attachment to O3 of Residue B’ ( Fig 3C1 ) . Taken together , these results strongly indicated that the polymer is composed of a trisaccharide repeating unit with the structure [P- ( O→3 ) [β-D-Galp- ( 1→4 ) ]-β-D-GlcpNAc- ( 1→3 ) -α-D-GlcpNAc-1-] , whereby Residues A , B , and C make up repeating units at the non-reducing end and the interior of the polysaccharide and Residues A’ , B’ , and C’ constitute the reducing end trisaccharide repeat . The intensity ratio ( 3 . 4:1 ) of the anomeric signals of A and A’ points to an average of about 4–5 repeating units per polysaccharide chain . This conclusion was confirmed by SEC and NSI-MS , which showed the presence of polysaccharide chains consisting of a small number of repeating units ( S6 Fig ) . NMR spectra and chemical shift assignments for the type b , c , and d capsules are shown in Fig 3A–3C and Tables 2–4 . Structural analysis revealed that the type b capsule structure is a polymer of [6 ) -α-D-GlcpNAc- ( 1→5 ) -β- ( 8-OAc ) Kdop- ( 2→] , the type c capsule structure is a polymer of [3 ) -β-D-Ribf- ( 1→2 ) -β-D-Ribf- ( 1→2 ) -β-D-Ribf- ( 1→4 ) -β-Kdop- ( 2→] , and the type d capsule structure is a polymer of [P- ( O→3 ) [β-D-Galp- ( 1→4 ) ]-β-D-GlcpNAc- ( 1→3 ) -α-D-GlcpNAc-1-] . A comparison of the structures of the type a , type b , type c , and type d capsules is shown in Fig 4 . To confirm that the csa , csb , csc , and csd loci are essential for production of capsule , we deleted each of these loci and then examined the resulting strains for surface material that stains with Alcian blue . As shown in Fig 5 , targeted deletion of the csa , csb , csc , or csd locus resulted in loss of surface extractable capsule from strains KK01 , PYKK58 , PYKK60 , and BB270 , respectively . Chromosomal complementation of each of these regions at the native locus resulted in restoration of encapsulation . These results demonstrate that the capsule synthesis loci are essential for production of capsule in representative type a , type b , type c , and type d K . kingae strains . In additional experiments , we examined the ability of the type a , b , c , and d loci to complement a deletion of the csa locus in prototype strain KK01 and produce the corresponding capsule . In performing these studies , we engineered a deletion of csaA with no effect on the flanking arg and hemB genes , producing a strain called KK01Δcsa . Subsequently , we generated a plasmid called pSwap , which contains the arg and hemB genes , a kanamycin resistance marker , and a partial pUC19 multiple cloning site ( MCS ) ( Fig 6A ) . Using this plasmid , we inserted each of the four capsule synthesis loci into the MCS , generating pSwapcsa , pSwapcsb , pSwapcsc , and pSwapcsd . Each of these plasmids was linearized and transformed into strain KK01Δcsa , producing strains KK01Swapcsa , KK01Swapcsb , KK01Swapcsc , and KK01Swapcsd . As shown in Fig 6B , each of these strains produced a capsule as assessed by Alcian blue staining of surface extracts . To confirm that the capsule in each of these strains corresponded to the specific capsule synthesis locus , surface polysaccharide was extracted and examined initially by Alcian blue staining . As expected , the Alcian blue staining profile of the capsule extracted from the csa , csb , csc , or csd swap strains was similar to the profile of the parental capsule locus source strain , suggesting that the capsules produced in a KK01Δcsa background strain retain their native migration pattern ( Fig 6B ) . 1-D Proton NMR analysis demonstrated that strain KK01Swapcsa produced the type a capsule , strain KK01Swapcsb produced the type b capsule , strain KK01Swapcsc produced the type c capsule , and strain KK01Swapcsd produced the type d capsule ( Table 5 ) . These results demonstrate that the csa , csb , csc , and csd loci encode the synthesis components of the four K . kingae capsule types and are functional in an isogenic strain background containing the capsule export and assembly machinery [3 , 4] . With our knowledge of the type a , type b , type c , and type d capsule loci in hand , we used a PCR approach to examine a large collection of K . kingae clinical isolates for capsule type . A total of 417 Israeli strains isolated between 1990 and 2014 were investigated . The collection contains 239 strains isolated from healthy pharyngeal carriers and 178 strains recovered from patients with a variety of invasive infections , including skeletal system infections , bacteremia , and endocarditis , allowing characterization of the capsule types elaborated by the full range of K . kingae isolates in the country . Overall , 413 of the 417 ( 99 . 0% ) strains were genotyped by pulsed-field gel electrophoresis ( PFGE ) [22] and found to belong to 60 distinct clones , including 16 clones that were represented in the collection by ≥7 strains and that collectively accounted for 345 ( 83 . 5% ) of all typed strains . One of the four capsule synthesis loci was identified in all of the strains , except for strain KK183 belonging to PFGE clonal group Tnc , which was isolated from the synovial fluid of a child with septic arthritis . This strain was shown to be nonencapsulated and did not generate capsule locus flanking or capsule locus specific PCR amplicons ( S7 Fig ) . Therefore , this strain was not included in the analysis of the association between capsule type and invasiveness or clonal distribution . A second nonencapsulated strain ( KK56 , PFGE clone S ) was isolated from a child with arthritis and was found to have a csaA gene with a 512 bp internal deletion in the ORF , which is predicted to introduce a frameshift mutation leading to a truncated CsaA protein ( S7 Fig ) . This strain was included among organisms with capsule type a for the purposes of the data analysis . While capsule type a was common among both carrier and invasive isolates , the distribution of capsule types b , c , and d in the invasive versus carrier groups showed statistically significant differences using the χ2 test ( P<0 . 001; Fig 7A and S1 Table ) . Overall , capsule type a or type b was found in 171 of 178 ( 96 . 1% ) invasive strains but in only 163 of 239 ( 68 . 2% ) carrier strains ( P<0 . 001 ) . Employing capsule type d as the reference , the logistic regression showed that capsule type a had an OR of 15 . 9 for invasive disease ( P<0 . 001 , 95% CI: 3 . 8–67 . 5 ) , capsule type b had an OR of 48 . 0 for invasive disease ( P<0 . 001 , 95% CI: 11 . 2–206 . 7 ) , and capsule type c had an OR of 3 . 2 for invasive disease ( P = 0 . 346 , 95% CI: 0 . 4–15 . 4 ) . The different capsule types showed significant associations with specific invasive clinical syndromes using the χ2 test ( P<0 . 001 ) . While capsule type b was more frequent among cases of bacteremia ( 45 of 71 , 63 . 4% ) , capsule type a was found in one-half ( 48 of 96 ) of skeletal system infections ( Fig 7B and S2 Table ) . Statistical analysis using the χ2 test demonstrated a significant association of capsule type and PFGE clones ( P<0 . 001 ) . Of note , all 16 common PFGE clones showed a clear predominance of a single capsule type: capsule type a in PFGE clones A , B , C , H , J , M , P , and S; capsule type b in PFGE clones K , N , and V; capsule type c in PFGE clones D and R; and capsule type d in PFGE clones F , G , and U ( Fig 7C and S3 Table ) .
In this study we examined a large collection of K . kingae clinical isolates and established that there are four different K . kingae capsule types . In addition , we identified the underlying capsule synthesis genes for each capsule type . Using a combination of mass spectroscopy and NMR , we also determined the structure of two previously uncharacterized capsule types ( type c and type d ) , complementing previous work on the structure of the type a capsule and the type b capsule [11 , 18] . Finally , we used a genetic screen to determine the capsule type of invasive disease isolates and healthy carrier isolates and discovered that capsule type a and type b account for 96% of all invasive disease isolates and that capsule type c and type d are disproportionately present among healthy carrier isolates . At the outset of our study , we hypothesized that our large collection of K . kingae clinical isolates would contain multiple polysaccharide capsule types . Bacterial polysaccharide capsules are traditionally typed using one of two methods: 1 ) genetically , based on the presence of specific capsule synthesis genes in the capsule locus , or 2 ) immunologically , based on agglutination reactions using capsule-specific sera . In this study we used a PCR-based genetic screening method , similar to methods that assess the capsule polysaccharide synthesis region for capsule typing of K . pneumoniae [23–25] , Pasteurella multocida [26] , and N . meningitidis [27] . Using this approach , we established that there are four different capsule types in K . kingae , with each strain containing only one of four distinct capsule synthesis loci . 1-D Proton NMR analysis of purified capsule from isogenic capsule synthesis locus swap strains confirmed that capsule type is determined by the gene content of the capsule synthesis locus ( Table 5 ) . The presence of multiple capsule types in a species is well documented for a variety of encapsulated pathogens , with examples including S . pneumoniae ( >90 types ) , E . coli ( >80 types ) , Klebsiella pneumoniae ( 78 types ) , N . meningitidis ( 13 types ) , and H . influenzae ( 6 types ) . Of the four capsule structures that we describe , two have been previously described in other species . In particular , the type a capsule containing [3 ) -β-GalpNAc- ( 1→5 ) -β-Kdop- ( 2→] is identical to the capsule of Moraxella nonliquefaciens strain 3828/60 [28] , and the type b capsule containing [6 ) -α-GlcpNAc- ( 1→5 ) -β- ( 8-OAc ) Kdop- ( 2→] is identical to the Actinobacillus pleuropneumoniae serotype 5a capsule [18 , 29] . In contrast , the type c capsule containing [3 ) -β-Ribf- ( 1→2 ) -β-Ribf- ( 1→2 ) -β-Ribf- ( 1→4 ) -β-Kdop- ( 2→] and the type d capsule containing [P- ( O→3 ) [β-Galp- ( 1→4 ) ]-β-GlcpNAc- ( 1→3 ) -α-GlcpNAc-1-] are novel . Uropathogenic E . coli are typically encapsulated with acidic polysaccharides , often containing Kdo together with one or two ribose moieties ( di- or tri-saccharide ) in the repeating unit . For example , the E . coli K16-antigen [2 ) -β-D-Ribf- ( 1→3 ) -β-D-Ribf- ( 1→5 ) -α-Kdop- ( 2→] [30] and the E . coli K74 antigen [3 ) -β-D-Ribf- ( 1→2 ) -β-D-Ribf- ( l→6 ) -β-Kdo- ( 2→] [31] both contain Kdo and ribose in unequal ratios , similar to the type c capsule in K . kingae . The Kdo-ribose polysaccharides form a group of closely related but serologically distinct E . coli capsule antigens , and the serologic variability is increased by different degrees of O-acetylation at various sites [32] . We also found acetylation in the K . kingae type c capsule , with 60% of the R groups being acetylated . The functional consequence of type c capsule acetylation in terms of serological reactivity remains to be investigated . It is interesting to speculate regarding the potential for interstrain capsule type switching in K . kingae . In N . meningitidis , capsule switching has been shown to result from recombination of the polysialyltransferase gene ( siaD ) or the capsule biosynthesis operon [33] , with evidence for capsule switching between strains implicated in carriage and strains associated with invasive disease [34] . Pneumococcal isolates can also undergo capsule switching , with the serotype of a clone changing due to alteration in the capsule biosynthesis locus via mutations or through genetic recombination [35–37] . In support of the possibility of intraspecies capsule switching in K . kingae , several K . kingae clonal groups are associated with multiple capsule types . Asymptomatic carriage provides an ideal environment for interspecies exchange of genetic material among bacteria that occupy the same niche [38] . The human nasopharynx has been shown to harbor diverse bacteria , including N . meningitidis , H . influenzae , and S . pneumoniae as well as nonpathogenic Neisseria spp . and Moraxella spp . Evidence supporting horizontal gene transfer between phylogenetically distant species is seen in the meningococcal genome , which harbors three independent domains of Haemophilus-like DNA . Uptake and integration of DNA in the upper respiratory tract is a probable mechanism to explain the capsule diversity observed in K . kingae in this study . Actinobacillus spp . , Moraxella spp . , and Kingella spp . are all found in normal human flora of the upper respiratory tract , providing the opportunity for horizontal gene transfer from other genera as the genesis of the four K . kingae capsule synthesis loci . M . nonliquefaciens has been shown to be present in the respiratory tract of young children [39 , 40] . A . pleuropneumoniae is primarily a swine pathogen , but other Actinobacillus spp . can be found in humans [41] . All of the K . kingae strains in our collection gave a PCR product for the capsule export and assembly genes ctrABCD , lipA , and lipB , suggesting that all of these strains contain the machinery necessary to display a capsule polymer on their surface [3] . However , out of 417 isolates , two strains demonstrated atypical PCR capsule typing results . First , strain PYKK56 yielded a csa locus PCR product , but the product was smaller than expected ( S7C Fig ) . Sequencing of the csa locus in this strain revealed a 512 bp internal deletion in the csaA gene , resulting in frameshift that is predicted to lead to a truncated CsaA protein ( S7G Fig ) . Alcian blue staining of surface extracts from this strain revealed no capsule , suggesting that the csaA mutation resulted in abrogation of capsule expression ( S7A Fig ) . Second , strain PYKK183 yielded no capsule locus flanking product and no capsule locus-specific PCR product ( S7B–S7G Fig ) . Alcian blue staining of surface extracted material revealed that this strain is not encapsulated , suggesting that this strain lacks capsule synthesis genes , rather than possessing a unique capsule synthesis locus ( S7A Fig ) . Amit et al . determined that K . kingae PFGE clonal groups B , H , K , N , and P account for 72 . 9% of all invasive isolates and that PFGE clonal groups A , C , D , F , G , J , R , S , and U are rare among invasive disease isolates [42] . Interestingly , only capsule types a and b are represented in the B , H , K , N , and P clonal group isolates . Overall , the type a and type b capsules account for 96% of invasive isolates but less than 70% of the carrier isolates . The type c capsule is most prevalent in the D and R clonal groups , and the type d capsule is most prevalent in the F , U , and G clonal groups , all of which fall into the subset of rare-disease PFGE clonal groups . The significant difference in the capsule type distribution between strains carried by asymptomatic children and those isolated from patients with invasive infections may suggest that the type c and type d capsules provide incomplete protection to K . kingae organisms , enabling them to colonize the oropharyngeal epithelium but not allowing their survival in the bloodstream , the skeletal system , or the endocardium . This phenomenon of specific capsule type association with invasive disease is reminiscent of encapsulated H . influenzae , an upper respiratory tract colonizer that elaborates six distinct polysaccharide capsules , with strains elaborating the type b capsule accounting for almost all cases of disease prior to the introduction of the conjugate vaccines [43] . However , the association between capsule type and virulence may not be causal . Close examination of the data in Fig 7C and S3 Table shows that 49 of 51 ( 96 . 1% ) strains belonging to the A , C , and M clonal groups , which appear to have diminished virulence and were collectively associated with only 4 of 181 ( 2 . 2% ) invasive infections in Israel according to a study published in 2012 [42] , elaborate polysaccharide capsule types a or b , indicating that determinants other than capsule type likely play an important role in the potential of the organism to cause invasive disease . This possibility is indirectly supported by the fact that two nonencapsulated strains , KK183 and KK56 , were able to cause septic arthritis in otherwise healthy children . Considering the effectiveness of many polysaccharide-conjugate vaccines in reducing childhood morbidity and mortality , it is interesting to speculate that a K . kingae capsule polysaccharide-conjugate vaccine may be an effective strategy to prevent K . kingae disease , pending additional analysis of the global burden of K . kingae disease . While more studies are needed , the discovery that the capsule repertoire of a diverse collection of K . kingae carrier and invasive disease isolates is represented by only 4 capsule types , with two capsule types accounting for >95% of invasive disease , is an important first step in establishing the feasibility of a vaccine for the prevention of K . kingae disease .
The strains representative of each capsule type that were used for the fundamental studies in the work are listed in Table 6 . The complete list of clinical isolates that were examined for capsule type are shown in S4 Table . K . kingae strain 269–492 was isolated from the joint fluid of a child with septic arthritis at St . Louis Children’s Hospital , St . Louis , MO . K . kingae strain KK01 is a stable natural variant of strain 269–492 that grows as a non-spreading , non-corroding colony type and was used as the primary strain in this study [44] . K . kingae and E . coli strains were grown and stored as previously described [4 , 11] . K . kingae isolates were selected from a large assortment of Israeli strains that have been gathered at the Soroka University Medical Center since the early 1990’s . The collection contains isolates from patients with a variety of invasive infections and from healthy pharyngeal carriers in the course of epidemiological studies on K . kingae carriage and transmission . A total of >200 K . kingae invasive strains and >600 K . kingae carrier isolates have been typed by pulsed field gel electrophoresis ( PFGE ) [42] , and a sample of the predominant PFGE clones has been further characterized by MLST and rtxA gene sequencing [46] . Based on genotyping results , strains were selected to meet the following study goals while maintaining a manageable number for analysis: strains isolated over more than two decades , clones that collectively cause the vast majority of invasive infections in Israel [47] , clones that are primarily associated with asymptomatic pharyngeal colonization [48] , strains isolated from patients with a variety of clinical syndromes ( bacteremia , skeletal system infection , or endocarditis ) [42] , and strains associated with clusters of disease in daycare center facilities [49] . Because the different genotyping schemes of K . kingae exhibit remarkable congruency [46] , it was assumed that studying strains belonging to rare PFGE clones would increase the chances to detect novel capsule types . Thus , the strain collection was enriched with a large number of uncommon invasive as well as colonizing isolates . The Israeli isolates used in this study are part of a preexisting anonymized collection and as such did not require IRB approval for use . Targeted gene disruptions and complementation constructs in K . kingae were generated as previously described [4 , 45] . Briefly , plasmid-based gene disruption constructs were created in E . coli , linearized , and introduced into K . kingae using natural transformation . Transformants were recovered by selectively plating on chocolate agar plates with the appropriate antibiotic . Gene disruptions and complementation constructs were confirmed by PCR . The primers used in this study are listed in Table 7 . To delete the capsule synthesis locus , we generated the plasmid pSwapEmpty . Briefly , fragments of homologous recombination targeting sequence corresponding to ~1 kb upstream of csaA and ~1 kb downstream of csaA were PCR amplified from strain KK01 genomic DNA using primers pSwapFor5’/pSwapRev5’ and pSwapFor3’/pSwapRev3’ , respectively , and were ligated into pUC19 . A kanamycin resistance cassette was then ligated into the pUC19 KpnI site , which is located between the cloned upstream and downstream homologous recombination targeting sequences , to generate pSwapEmpty . The plasmid was linearized with NdeI and transformed into strain KK01 . To create the complementation/capsule swap constructs , the capsule synthesis loci were PCR amplified as follows: for the csa locus , using genomic DNA from strain KK01 and primers csaswapFor/csaswapRev; for the csb locus , using genomic DNA from strain PYKK58 and primers csbswapFor/csbswapRev; for the csc locus , using genomic DNA from strain PYKK060 and primers cscswapFor/cscswapRev; and for the csd locus , using genomic DNA from strain BB270 and primers csdswapFor/csdswapRev . The csa and csb locus amplicons were cloned into pSwapEmpty using standard restriction cloning , generating pSwapcsa and pSwapcsb , respectively . The csc and csd amplicons were cloned into pSwapEmpty using the Gibson Assembly Cloning kit ( New England Biolabs , Ipswich , MA ) , generating pSwapcsc and pSwapcsd , respectively . For the capsule swap studies , we transformed each swap construct ( pSwapcsa , pSwapcsb , pSwapcsc , or pSwapcsd ) harboring a kanamycin cassette into the nonencapsulated isogenic strain KK01Δcsa ( ErmR ) and screened for loss of ErmR and gain of KanR . For capsule synthesis locus complementation , the capsule swap plasmids pSwapcsa , pSwapcsb , pSwapcsc , and pSwapcsd were transformed into KK01Δcsa , PYKK58Δcsb , PYKK60Δcsc , and BB270Δcsd , respectively , using the unmarked transformation protocol described below . To generate unmarked gene disruptions and complements , we used the following procedure without antibiotic selection . First , K . kingae was grown overnight on chocolate agar , resuspended in Brain Heart Infusion ( BHI ) broth containing 50 mM MgCl2 to an OD600 of 0 . 7 , and diluted 1:25 in BHI broth . The initial dilution was then serially diluted 1:4 a total of 9 times , and 5 μl of each dilution was transferred to a microfuge tube containing 5 μl of linearized transforming plasmid at a concentration of 50 ng/μl . The 10 μl total mixture was then plated on chocolate plates and allowed to dry in ambient air conditions ( approximately 5 minutes ) before placement into the CO2 incubator at 37°C . Single colonies were screened by PCR for recombination at the locus of interest after two rounds of single colony purification of the potential transformants . In preparation for extraction and purification of capsule , the pam locus involved in synthesis of the galactan exopolysaccharide was deleted from the relevant strains [11] . Extraction , purification , and visualization of migration patterns on 7 . 5% SDS-PAGE gels using Alcian blue staining of capsule material were performed as previously described [4 , 11] . K . kingae sequence outside of the capsule synthesis locus from strain 269–492 was used to design flanking primers hemBFor and argRev in the hemB ( delta aminolevulinic-acid dehydratase ) and arg ( arginine-succinate synthase ) genes flanking csaA ( Table 7 ) . PCR amplicons were sequenced , and the resulting sequence was the basis for design of interior primers specific for each of the four capsule synthesis loci . To screen for the presence of each locus , we used both universal flanking primers that amplified all capsule loci and locus-specific primers that annealed to the interior portion of each locus . The presence of either csaA , csbABC , cscABC , or csdABC ( the csa , csb , csc , or csd capsule synthesis locus ) was determined by PCR amplification using interior primers ( see Table 7 ) and confirmed by determining the size and restriction map of the flanking primer amplicon . To obtain restriction digest patterns , PCR products were amplified using the primers hemBFor and argRev listed in Table 7 in a total reaction volume of 25 μl . 2 . 5 μl of 10x digestion buffer and 1 μl of NruI enzyme ( New England Biolabs ) were mixed with PCR products and incubated overnight at 37°C . The digests were resolved on a 1 . 2% agarose gel and visualized for banding pattern . The statistical significance of the differences in the distribution of the different capsule types among carrier vs . invasive strains , among bacteremia vs . skeletal system infections , and among the PFGE clones was determined by the χ2 test using the Statistical Package for the Social Sciences ( SPSS ) version 21 software . The link between the different capsule types and invasiveness was further explored with a logistic regression model in which the capsule type with the lowest percentage of associated invasive strains was employed as a reference and the odds ratio ( OR ) for invasiveness , p-value , and 95% confidence intervals ( CI ) for the other capsule types were calculated . A p-value <0 . 05 was considered significant for all comparisons . | Kingella kingae is a gram-negative pathogen that is being recognized increasingly as a cause of joint , bone , and other bloodborne infections in young children , reflecting advances in cultivation techniques and molecular methods of detection . Previous work established that K . kingae expresses a polysaccharide capsule , a surface factor that likely plays a key role in allowing the organism to transition from colonization of the oropharynx to survival in the bloodstream . We analyzed a large collection of epidemiologically diverse K . kingae isolates and found that there are at least four structurally distinct capsule types in the K . kingae population . In addition , we found that two of the four capsule types account for >95% of all cases of K . kingae invasive disease , suggesting that these two polysaccharide structures may have unique properties related to virulence . Given the widespread success of polysaccharide capsule-based vaccines in preventing invasive bacterial disease , this study lays the foundation for a promising strategy to prevent K . kingae disease . | [
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] | 2016 | Kingella kingae Expresses Four Structurally Distinct Polysaccharide Capsules That Differ in Their Correlation with Invasive Disease |
Previous work has demonstrated the presence of ribonucleotides in human mitochondrial DNA ( mtDNA ) and in the present study we use a genome-wide approach to precisely map the location of these . We find that ribonucleotides are distributed evenly between the heavy- and light-strand of mtDNA . The relative levels of incorporated ribonucleotides reflect that DNA polymerase γ discriminates the four ribonucleotides differentially during DNA synthesis . The observed pattern is also dependent on the mitochondrial deoxyribonucleotide ( dNTP ) pools and disease-causing mutations that change these pools alter both the absolute and relative levels of incorporated ribonucleotides . Our analyses strongly suggest that DNA polymerase γ-dependent incorporation is the main source of ribonucleotides in mtDNA and argues against the existence of a mitochondrial ribonucleotide excision repair pathway in human cells . Furthermore , we clearly demonstrate that when dNTP pools are limiting , ribonucleotides serve as a source of building blocks to maintain DNA replication . Increased levels of embedded ribonucleotides in patient cells with disturbed nucleotide pools may contribute to a pathogenic mechanism that affects mtDNA stability and impair new rounds of mtDNA replication .
Human mitochondrial DNA ( mtDNA ) is a double-stranded circular molecule of 16 . 6 kb that encodes for key components of the oxidative phosphorylation system . Previous studies have demonstrated the presence of about 10 to 30 ribonucleotides in each mtDNA molecule [1 , 2] . These ribonucleotides can either be incorporated by mtDNA polymerase γ ( POLγ ) during DNA synthesis or , alternatively , be remnants of partially processed RNA primers used for initiation of DNA synthesis . The high levels of ribonucleotides in mtDNA are intriguing , but their precise localization or biological functions remain to be established . Recent data suggest that incorporation of ribonucleotides during nuclear DNA replication is more frequent than previously anticipated and that the presence of these nucleotides may cause genome instability [3] . The incorporated ribonucleotides disturb the structure of DNA as they contain reactive 2´-hydroxyl groups that can attack the sugar-phosphate backbone and cause strand breaks [4] . In nuclear DNA , several processes contribute to ribonucleotide incorporation . The major replicative DNA polymerases ( Pol α , Pol δ , and Pol ε ) in eukaryotic cells , have all been shown to incorporate ribonucleotides during DNA synthesis [5] . Ribonucleotide incorporation into DNA may also be a consequence of inefficient removal of RNA primers used to initiate DNA synthesis . Normally , RNA primers are exchanged to DNA during RNA maturation . This process involves several nucleases , including RNase H1 and H2 , Flap endonuclease 1 ( FEN1 ) , and DNA2 [6–9] . RNase H1 leaves two ribonucleotides behind and it is not clear if these two ribonucleotides are removed by further processing [10 , 11] . RNase H1 has a mitochondrial isoform and deleterious mutations in the RNASEH1 gene can disturb mtDNA replication and lead to mitochondrial disease [12] . Repair pathways exist in eukaryotes that remove incorporated ribonucleotides from nuclear DNA to prevent genomic instability . RNase H2 is required in the nucleus for ribonucleotide excision repair ( RER ) and mismatch repair ( MMR ) [13 , 14] . During RER , RNase H2 nicks double stranded DNA ( dsDNA ) at the 5´-side of the embedded ribonucleotide . Next , Pol δ DNA synthesis leads to displacement of the incised DNA strand and the flap-cleavage by FEN1 . Finally , the nicked DNA strand is ligated by DNA ligase [13] . An alternative system for ribonucleotide excision has been identified in yeast . This second system is dependent on topoisomerase 1 ( TOP1 ) , which can cleave DNA at ribonucleotides and initiate their removal [15–17] . Whether RER exists in mitochondria remains unclear , but the absence of RNase H2 argues against RER in the organelle . Mitochondria do contain a topoisomerase 1 , TOP1mt , but the contribution of this enzyme to the removal of ribonucleotides from mtDNA has not been investigated [18] . The two DNA strands in the mitochondrial genome are referred to as the heavy ( H ) and light ( L ) strand , respectively , due to their different buoyant densities [18] . Each of these strands is replicated by a set of core proteins , which includes the POLγ , the mitochondrial helicase TWINKLE , the mitochondrial single stranded DNA-binding protein ( mtSSB ) , and the mitochondrial RNA polymerase ( POLRMT ) . According to the strand displacement model , DNA synthesis is first initiated at the origin of H-strand DNA replication ( OriH ) [19] . Primers required for initiation at OriH are produced by POLRMT-dependent transcription initiated at the Light Strand promoter ( LSP ) . After initiation , replication proceeds in one direction to produce the nascent H-strand . In the process , the template H-strand is displaced and covered with mtSSB [20] . When the replication machinery has synthesized approximately two thirds of the H-strand , it reaches the origin of L-strand replication ( OriL ) . When OriL is exposed in a single-stranded conformation , it adopts a stem-loop structure that is used by POLRMT to initiate primer synthesis from a poly-dT stretch in the loop region [21] . POLγ initiates DNA synthesis from the RNA primer and proceeds to produce the nascent L-strand using the displaced parental H-strand as template . DNA synthesis is continuous on both strands and continues until two complete daughter molecules have been formed . An alternative model for mtDNA replication has also been suggested . The Ribonucleotide Incorporation ThroughOut the Lagging Strand model ( RITOLS ) resembles the strand displacement model in many aspects , but suggest that poly-adenylated RNA covers the displaced H-strand , playing a role similar to that of mtSSB [22–25] . If these poly-adenylated RNA molecules covering replicating mtDNA can be used as primers for DNA synthesis remains unclear . Pathogenic variants in both mtDNA and nuclear-encoded mitochondrial genes are a common cause of mitochondrial disease and have also been implicated as a driving force in biological aging [26 , 27] . As examples , adult-onset progressive external ophthalmoplegia ( PEO ) and Alpers syndrome , have been linked to mitochondrial genome instability due to either multiple ( variable ) deletions or depletion of mtDNA [28] . The underlying causes of these mtDNA instability diseases are mutations in nuclear genes whose gene products are targeted to mitochondria . These genes can be grouped into two different classes . The first class corresponds to genes encoding proteins that are directly involved in mtDNA replication e . g . POLγA , POLγB , and TWINKLE , and the second class encodes for proteins involved in supplying mitochondria with dNTPs required for DNA synthesis , e . g . thymidine kinase 2 ( TK2 ) [29] , deoxyguanosine kinase ( DGUOK ) [30] and MPV17 [31 , 32] . Mutations in the respective TK2 , DGUOK and MPV17 genes can lead to imbalanced dNTP pools which subsequently might lead to genome instability , however , the underlying molecular mechanisms are in most cases not fully elucidated [32–35] . In the present work , we map free 5´-ends and ribonucleotides embedded in mtDNA isolated from human control cells and fibroblasts derived from patients with disturbed dNTP pools . Our data demonstrate that mtDNA contain relative high levels of embedded ribonucleotides in both strands . In combination with a detailed biochemical analysis , our results strongly suggest that DNA polymerase γ-dependent incorporation is the main source of ribonucleotides in mtDNA and argues against the existence of a mitochondrial ribonucleotide excision repair pathway in human cells . Our study provides insights into the mechanisms of mtDNA replication during normal conditions and elucidates the consequences of limited dNTP pools associated with mitochondrial disease .
To study the distribution of ribonucleotides in mtDNA in vivo , we performed genome-wide mapping of HeLa cell mtDNA . In the HydEn-seq method [36] , free 5´-ends are identified by combining 5´-end sequencing ( 5´-End-seq ) with alkaline cleavage of mtDNA at embedded ribonucleotides , allowing single nucleotide resolution mapping . We identified the positions of free 5´- ends as well as incorporated ribonucleotides to the H- and L- strand ( Fig 1A and S1 Fig ) . HydEn-seq libraries displayed a 14-fold higher coverage on the L-strand relative to the H-strand ( Fig 1B , upper panel , left part ) . We did not observe a similar strand bias in the nuclear genome ( Fig 1B , upper panel , right part ) . A possible reason for the observed strand bias is the distinct differences in nucleotide composition between the H- and L-strand , and to correct for such an effect we introduced eleven double strand breaks in the mtDNA by digesting genomic DNA with the restriction enzyme HincII . At these cleavage sites , we have a similar proportion of reads on the L- versus H-strand as in the uncut libraries . These reads can be used as a reference to normalize the sequencing reads ( for calculation see Materials and methods and S2 Fig ) . Before normalization , 31-fold more reads mapped on the L-strand than on the H-strand at HincII sites , demonstrating a strong strand bias in our libraries . After normalization , we could estimate that about 20 ribonucleotides are present throughout the H-strand and about 16 ribonucleotides in the L-strand in each mtDNA molecule from HeLa cells ( Fig 1C ) . We concluded that ribonucleotides are evenly distributed between both strands . To confirm this conclusion , we also performed Southern blotting using strand specific probes ( H-strand or L-strand ) . In this experiment , mtDNA was linearized with BamHI and treated with KCl or KOH and separated using alkaline agarose gel electrophoresis followed by Southern blotting ( Fig 1D ) . The control samples , treated with KCl , migrated as a band corresponding to the size of linearized full-length mtDNA ( 16 . 6 kb ) . The samples treated with KOH however , generated a smear of hydrolyzed products . The smears ranged from the linearized full-length product down to 7S DNA with an approximate size of 0 . 6 kb , suggesting that ribonucleotides are randomly distributed throughout the entire mitochondrial genome . Consistent with a previous report , the degradation patterns for the blots probed against H- and L-strand were similar , and thus supporting the even distribution of ribonucleotides between the strands of mtDNA [22] . Using 5´-End-seq , we found that more than 60% of the free 5´-ends in the H-strand were located near the OriH region ( between positions 16 , 200–16 , 569 and 1–300 ) ( Fig 2A , upper panel ) . The most abundant 5´-ends were mapped to positions 111 , 149 and 191 , which were consistent with previous findings [37–40] . We also observed a number of free 5´-ends in a broad , 300-nt zone , between positions 16 , 200–16 , 500 ( Fig 2A , upper panel ) , located downstream of the OriH region . A previous report identified free 5´- ends on both the H-strand and L-strand in this region , which was used as evidence for initiation of bidirectional replication in the NCR [41] . However , our analysis revealed no free 5´-ends on the L-strand , but only on the H-strand , arguing against the idea that these ends were produced by bidirectional initiation of DNA synthesis ( Fig 2A , lower panel ) . Furthermore , we could not observe any changes in the pattern of 5´-ends before and after alkaline treatment , arguing against the presence of attached ribonucleotides at the observed 5´-ends ( S3 Fig ) . On the L-strand , we identified a peak of free 5´-ends near OriL , at positions 5 , 768 to 5 , 776 ( Fig 2B , lower panel ) . After alkaline-treatment , the 5´-ends shifted a couple of nucleotides downstream ( Fig 2B , upper panel ) , suggesting that the identified 5´-ends contained ribonucleotides at the very end . The presence of ribonucleotides argues for RNase H1 dependent processing of the OriL RNA primer , since RNase H1 leaves the last two ribonucleotides at a junction between RNA and DNA . Replicative DNA polymerases can incorporate ribonucleotides during DNA synthesis and kinetic analyses of single-nucleotide incorporation events have demonstrated that mitochondrial POLγ discriminates efficiently , but not completely , against ribonucleotides [42] . To test whether POLγ could be the source of the many embedded ribonucleotides identified in mtDNA , we monitored ribonucleotide incorporation by POLγ during synthesis of longer DNA stretches in vitro . To this end , we generated a primed-template with a 30-nt single-stranded 5´-tail by annealing a 70-nt oligonucleotide to a complementary 40-nt oligonucleotide radioactively labeled on the 5´-end ( Fig 3A ) . Purified recombinant POLγ was incubated with the template together with dNTPs and increasing concentrations of rNTP . Full-length products were excised , purified and subjected to alkaline hydrolysis to identify the ribonucleotide incorporation sites . As a control , we used KCl treatment , which will not hydrolyze the DNA . The levels of incorporated ribonucleotides increased with increasing rNTP concentrations ( Fig 3B , lanes 1–5 ) . The idea that alkaline sensitivity truly reflected ribonucleotide incorporation was also confirmed by processing our products using RNase H2 ( Fig 3C ) . It is important to note that , during alkaline treatment , the extended DNA oligonucleotide will be hydrolyzed at the 3´-side , whereas RNase H2 will cleave at the 5´-side , of the embedded ribonucleotide . The product generated by RNase H2 will therefore differ by ~1 nt from the product generated by KOH treatment . We compared the ribonucleotide incorporation frequency in primer extension assays for POLγ and proofreading deficient POLγ ( EXO- ) when the reaction mixture contained 1 mM of each of the four rNTPs and 4 μM each of the four dNTPs ( Fig 3D ) . These concentrations and the ratio ( a 250-fold excess of rNTPs ) were decided on the basis of estimates of nucleotide pools in vivo in mammalian mitochondria [43 , 44] . In order to simplify the in vitro experiments , we kept the nucleotide pools balanced even though it is known that nucleotide pools in vivo are slightly unbalanced . We estimated product quantities from band intensities on the gels and calculated that POLγ under these conditions incorporates 1 ribonucleotide for every 2 . 0 ± 0 . 6 × 103 dNTPs . We also investigated if the proofreading activity of POLγ could affect the levels of ribonucleotides incorporated during DNA synthesis . The exonuclease activity was inactivated by a D274A substitution in the second exonuclease motif in the POLγA subunit [45 , 46] . Our analysis revealed that EXO- incorporates 1 ribonucleotide for every 3 . 0 ± 1 . 1 × 103 dNTPs . A Student’s t-test showed no statistically significant differences in ribonucleotide incorporation between POLγ and EXO- , suggesting that the exonuclease activity of POLγ has no discernable effect on ribonucleotide incorporation in vitro . Given the mtDNA size of 16 . 6 kb , these in vitro data would correspond to the incorporation of about 8 ribonucleotides in each strand during replication of mtDNA . The estimated number is in agreement with our estimates of embedded ribonucleotide levels in vivo , indicating that POLγ is the main driver behind ribonucleotide incorporation in human mitochondria and that an efficient mitochondrial RER pathway does not remove those ribonucleotides . Alkaline treatment of the DNA synthesized in vitro by POLγ generated an uneven band pattern on the gel ( Fig 3B , 3C and 3D ) , suggesting that the frequency of ribonucleotides incorporation is sequence dependent . To precisely map the observed sites , we performed in vitro DNA synthesis with the same template , but with only one type of ribonucleotide added at the time ( Fig 3E ) . In this manner , we could verify the incorporation pattern of individual ribonucleotides . The DNA template used in our experiments contained roughly equivalent numbers of the four bases ( 30% A , 23% C , 27% T , 20% G ) . From reaction mixtures containing POLγ or EXO- , we quantified the band intensities and calculated the mean frequency and standard error from three independent experiments ( Fig 3F and 3G ) . Both POLγ and EXO- generated similar incorporation pattern , again demonstrating that the proofreading activity does not affect ribonucleotide incorporation during DNA synthesis . Of the four ribonucleotides , rCTP was the most frequently incorporated into the newly synthesized strand , followed by rGTP , whereas rATP and rUTP were less frequently incorporated ( Fig 3F , inset and Fig 3G , inset ) . Our data were thus in agreement with a previous single nucleotide incorporation study , which demonstrated that POLγ is less efficient in discriminating against rCTP and rGTP compared to rATP and rUTP [42] . To follow up these observations , we determined the relative frequencies of each individual ribonucleotide in mtDNA from HeLa cells in vivo . Again , we focused our analysis on positions 300–16 , 200 of mtDNA and excluded the OriL region from 5 , 747–5 , 847 . The H- and L- strands had different profiles of embedded ribonucleotides ( Fig 3H ) . Whereas rGTP was the most frequent ribonucleotide in the H-strand it was only the third most frequent in the L-strand . Similarly , rCTP was the most frequent ribonucleotide in the L-strand , but it was the third most frequent in the H-strand , whereas rUTP was the least frequent ribonucleotide in both strands . Mitochondrial DNA has a higher proportion of guanine ( G ) in the H-strand and cytosine ( C ) in the L-strand ( Fig 3I ) . We investigated if the strand bias seen in rGTP and rCTP frequencies could be explained by the strand bias in DNA base composition in mtDNA . Using HydEn-seq data from HeLa cells and the DNA base composition in mtDNA , the raw reads for each individual rNTP were normalized to obtain incorporation frequencies per 1 , 000 complementary nucleotides in mtDNA ( Fig 3J , see Materials and methods for details ) . In this normalized data , we do not observe strand bias between the two strands in terms of which rNTP is incorporated . There is a preferential incorporation of rGTP on both strands , intermediate levels of rCTP and rATP , and low levels of incorporated rUTP . The frequency of incorporation for individual ribonucleotides was therefore in nice agreement with the in vitro biochemical profile of POLγ-dependent ribonucleotide incorporation ( Fig 3 and [42] ) . The only exception was the frequency of rATP incorporation , which was somewhat higher than expected based on in vitro biochemical experiments . This deviation may indicate that the relative concentration of rATP present in mitochondria is higher than what was used in our in vitro assays . Taken together , these results are consistent with POLγ incorporation being the main contributor to the incorporation of ribonucleotides in mtDNA . If POLγ incorporates ribonucleotides during DNA synthesis , one would expect that altered or unbalanced mitochondrial dNTP and rNTP pools could affect ribonucleotide incorporation frequencies and profiles , as observed in vitro ( Fig 3B ) . A number of reported pathogenic mutations causing mitochondrial disease have been linked to unbalanced mitochondrial nucleotide pools and , as a consequence , mtDNA instability . To test whether disturbed nucleotide pools leads to changes in ribonucleotide incorporation rates in mtDNA in vivo , we performed HydEn-seq using patient-derived cell lines harboring recessively-inherited pathogenic variants in the TK2 , DGUOK or MPV17 genes . For TK2 , we used three different patient cell lines and for both DGUOK and MPV17 we used two different cell lines , each harboring distinct recessively-inherited pathogenic gene variants ( details provided in Materials and methods ) . We first investigated if the mtDNA ribonucleotide profiles of the mutant fibroblasts differed from that of controls by performing hierarchical clustering on z-scores calculated from the ribonucleotide incorporation percentages ( Fig 4A ) . For each of the cell lines , sequencing reads from libraries made with undigested or HincII-digested DNA cluster together , showing that the treatment does not change the detected overall ribonucleotide distribution . Two main cell line clusters can be observed , one with the control Fibroblast ( FB ) and cells with TK2 mutations and the other with pathogenic DGUOK and MPV17 variants . The FB and TK2 mutant clusters are characterized by relatively high incorporation of rATP and rCTP levels whereas the DGUOK and MPV17 mutant clusters have relatively high incorporation of rGTP . The ribonucleotide profile of TK2-Q125* M132T is intermediate to FB and the other TK2 mutant lines , indicating that this compound heterozygous variant may have a milder phenotype ( Fig 4A ) . The patient fibroblasts displayed changes in ribonucleotide incorporation that were consistent with the underlying pathological mutations ( Fig 4B ) . Fibroblasts with mutations in genes encoding DGUOK or MPV17 have decreased mitochondrial dGTP pool size , and in our experiments , the relative levels of embedded rGTPs were increased relative to the other ribonucleotides . Fibroblasts with mutations in the gene encoding TK2 have a decreased dCTP pool , and correspondingly the relative incorporation of rCTP is increased in our experiments . The incorporation rates of rATP , rCTP and rGTP appeared to be different on the H- and L-strand ( Fig 4B ) . Here we again normalized the data to obtain incorporation frequencies per 1 , 000 complementary DNA bases ( Fig 4C , see Materials and methods for details ) . After this data normalization we could no longer observe any strand bias , indicating that the difference seen in incorporation percentages ( Fig 4B ) can be explained by the DNA base composition and not due to a different replication mode in each strand . The rCTP incorporation frequency was somewhat elevated in TK2 compared to FB whereas the rGTP incorporation frequencies were 2 to 3-fold higher in DGUOK and MPV17 compared to FB . All three mutant lines appear to have a somewhat lower rATP incorporation frequency . All four lines showed very low rUTP incorporation frequencies . We conclude that imbalanced mitochondrial dNTP pools change the pattern of rNTP incorporation in a manner consistent with the biochemical properties of POLγ ( Fig 4C ) . Next , we calculated the number of ribonucleotides in each strand in each of the cell lines and found that the fibroblast cells have a slight increased ratio ( 23 ribonucleotides in H-strand and 31 in L-strand ) compared to HeLa cells ( Fig 4D ) . Fibroblasts from controls and patients with defects in TK2 contain slightly more ribonucleotides in the L-strand , while the patients with defects in DGUOK and MPV17 have more ribonucleotides in the H-strand . The increased number of ribonucleotides in DGUOK and MPV17 in the H-strand may be a direct consequence of changes in rGTP:dGTP ratio in the combination with the high G content in the H-strand . Similarly cell lines with defects in TK2 have changed rCTP:dCTP ratio and increased incorporation frequency of rCTP on the L-strand , which has a high C content .
In the current report , we use a genome-wide approach to map the distribution and identity of free 5´-ends and embedded ribonucleotides in vivo with single-nucleotide resolution . We find that mtDNA isolated from HeLa cells on average contains 36 embedded ribonucleotides ( Fig 1C ) with similar levels in mtDNA from fibroblasts ( 54 ribonucleotides per genome , Fig 4D ) . The number of incorporated ribonucleotides is almost evenly distributed between the H- and L-strand in mtDNA from both HeLa cells and fibroblasts ( Figs 1C , 1D and 4D ) . The fact that equal levels of ribonucleotides are embedded in both strands also argues against the idea that these nucleotides represent remnants from priming events during DNA replication . Priming at other sites than OriH and OriL would take place during L-strand synthesis ( i . e . lagging strand ) and if these priming events left embedded ribonucleotides behind , we would expect to see higher levels in the L-strand ( Fig 1C ) . The relative levels of the individual ribonucleotides embedded in vivo , correlates well with in vitro observations using recombinant POLγ to investigate the incorporation of ribonucleotides during synthesis of longer DNA stretches ( Fig 3F ) . Ribonucleotide rCTP is most frequently incorporated , followed by rGTP , whereas rATP and rUTP are less efficiently incorporated . These observations are also consistent with a previous report from the Copeland laboratory , which used single-nucleotide extension assays to demonstrate that the frequency of ribonucleotide incorporation is dependent on the base [42] . POLγ is much more efficient in discriminating rUTP from dTTP ( 77 , 000-fold ) than rGTP from dGTP ( 1 , 100-fold ) . The ribonucleotide incorporation by POLγ in vitro depends on the rNTP/dNTP ratio , and under the conditions used here 1 rNTP is incorporated for every 2 , 000 dNTPs . This rate is similar to what was reported for the three replicative DNA polymerases in S . cerevisiae , where pol ε incorporates 1 ribonucleotide for every 1 , 250 dNTPs , pol δ 1 rNTP for every 5 , 000 dNTPs , and pol α 1 rNTP for every 625 dNTPs [5] . The rNTP:dNTP ratio also affects the relative levels of ribonucleotide incorporation in vivo ( Fig 4A and 4B ) . Mutations in DGUOK or MPV17 lead to depletion of dGTP and , as demonstrated here , result in increased rGTP incorporation during mtDNA synthesis ( Fig 4C , green panel ) . Mutations in TK2 cause a decrease in dCTP , which in turn cause increased incorporation of rCTP ( Fig 4C , purple panel ) . Our data also reveal a difference in primer maturation between OriL and OriH . At least two ribonucleotides remain associated with the free 5´-end at OriL ( characteristic of RNase H1 processing ) . The free 5´-ends observed at OriH do not contain associated ribonucleotides . These findings support the idea that RNase H1 cannot be the only nuclease required for primer processing at OriH , but that this process involves more complex mechanisms and additional enzymes , including MGME1 [9 , 40 , 47–49] . We also map additional free 5´-ends in a broad , 300-nt zone , between positions 16 , 200–16 , 500 ( Fig 2A , upper panel ) . These ends have previously been seen as an indication of bidirectional replication , but since they are only present on the H-strand , we speculate that the observed ends instead represent intermediates in 7S DNA breakdown . Being the third strand of the D-loop , 7S DNA strand is synthesized at much higher levels than other regions of the mtDNA genome , but how this strand is degraded after displacement have not been characterized [50] . Further experiments are clearly required to define the nature of the observed 5´-ends in the 16 , 200–16 , 500 region . POLγ does not seem to proofread ribonucleotides as the inactivation of the exonuclease activity fail to cause higher levels of rNTP incorporation in vitro ( Fig 3D ) . Likewise , neither yeast pol ε nor human pol δ can proofread ribonucleotides [51 , 52] . A certain level of embedded ribonucleotides seems to be well tolerated by the mtDNA replication and transcription machineries . It has been shown that POLγ efficiently performs single-nucleotide reverse transcription reactions but that longer stretches of embedded ribonucleotides in template DNA cause POLγ stalling [42] . Our genome-wide mapping of ribonucleotides also does not support the existence of longer stretches of ribonucleotides in the mtDNA , which can cause a problem for mtDNA maintenance . However , we cannot rule out that mutations in e . g . TK2 , DGUOK or MPV17 may cause imbalanced ribonucleotide pools in specific tissues , which can lead to the formation of longer stretches of embedded ribonucleotides , which in turn may cause problems during mtDNA replication and transcription . Further efforts to perform tissue specific HydEn-seq analysis may address this interesting possibility . Finally , embedded ribonucleotides may play functional roles in DNA maintenance . In support of this notion , ribonucleotides in nuclear DNA help to identify the nascent DNA strand during MMR . When RNase H2 removes ribonucleotides during RER , nicks are formed in the nascent strand , and the MMR system can thereby distinguish which strand to correct [14] . Possible mitochondrial processes regulated by embedded ribonucleotides remain to be identified , but we however conclude that when dNTP pools are limiting , ribonucleotides may serve as a second line of building blocks for mtDNA synthesis . Without ribonucleotide incorporation , cells may suffer more acute mtDNA instabilities due to replication stalling .
Primary skin fibroblast cultures were obtained from healthy age-matched controls and patients with confirmed pathogenic variants in one of three genes implicated in disorders of mtDNA maintenance , TK2 , DGUOK and MPV17 , leading to mtDNA depletion myopathy ( patients with TK2 variants ) or hepatocerebral mtDNA depletion ( patients with DGUOK and MPV17 variants ) . The following patient cells were studied: TK2-M1V ( homozygous p . ( Met1Val ) TK2 variant ( Patient 25 in [53] ) ; TK2-Q87* N100S ( compound heterozygous p . ( Gln87* ) ; p . ( Asn100Ser ) TK2 variant ( Patient II-4 in [54] ) ; TK2-Q125* M132T ( compound heterozygous p . ( Gln125* ) ; p . ( Met132Thr ) TK2 variants ( Garone et al , manuscript under review ) ) ; DGUOK-N46S delGC ( compound heterozygous p . ( Asn46Ser ) ; c . 13_14delGC DGUOK variants ) ; DGUOK-F256* ( homozygous p . ( Phe256* ) DGUOK variant ( Patient 10 in [55] ) ; MPV17-A23P E45D ( compound heterozygous p . ( Ala23Pro ) ; p . ( Glu45Aspfs*8 ) MPV17 variants ( Patient 2 in [56] ) ; MPV17-Q93P ( homozygous p . ( Gln93Pro ) MPV17 variant ( Patient 11 in [56] ) . Ethical approval was granted by the Newcastle and North Tyneside Local Research Ethics Committees ( REC 2002/205 ) , the study was performed under the ethical guidelines issued by each of our institutions and complied with the Declaration of Helsinki . Cells were grown in 70 ml of DMEM GlutaMAX medium ( Gibco ) , 10% fetal bovine serum ( Gibco ) in 250 ml Spinner flasks ( Bellco Glass Inc . ) . A total of 5x106 cells were collected by centrifugation for 5 min at 200 × g and washed once with PBS . Pellets were resuspended in 2 ml of lysis buffer ( 75 mM NaCl , 50 mM EDTA , 1% SDS , 20 mM HEPES pH 8 . 0 , 200 μg/ml Proteinase K ) and incubated at 42°C for 30 min . One volume of phenol-chloroform was added to the samples , then mixed and centrifuged at 15 , 000 × g for 5 min ( 4°C ) . The water phase was then transferred to a new tube for precipitation with 100 mM NaCl and 1 V of isopropanol . Samples were incubated at -20°C at least for 1 h . After precipitation , the samples were centrifuged ( 15 , 000 × g , 20 min , 4°C ) and washed with 70% EtOH . Pellets were dissolved in 100 μl of TE buffer . DNA concentrations for library preparations , was measured with a Qubit fluorometric instrument ( ThermoFisher Scientific ) . Patient and control fibroblast cells were grown in DMEM GlutaMAX medium , supplemented with 10% FBS , PEST and 50 μg/ml uridine . Between 4 and 11 × 106 cells were collected and total DNA was isolated using the Gentra Puregene Cell Kit ( Qiagen ) according to the manufacturer´s protocol . Free 5´-ends in mtDNA of HeLa and primary fibroblast cells were mapped by 5´-End-seq by treating 1 μg DNA with 0 . 3 M KCl for 2 h at 55°C . RNA residues in mtDNA of HeLa and primary fibroblast cells were mapped by HydEn-seq by hydrolyzing 1 μg DNA with 0 . 3 M KOH for 2 h at 55°C . To calculate the number of ribonucleotides per mtDNA molecule , we treated 1 μg DNA with 10 U of HincII and the digests were purified with HighPrep PCR beads ( MagBio ) , before the KCl or KOH treatment . After ethanol precipitation , the DNA fragments were treated for 3 min at 85°C , phosphorylated with 10 U of 3′-phosphatase-minus T4 polynucleotide kinase ( New England BioLabs ) for 30 min at 37°C , heat inactivated for 20 min at 65°C and purified with HighPrep PCR beads ( MagBio ) . Phosphorylated products were treated for 3 min at 85°C , ligated to oligo ARC140 overnight at room temperature with 10 U of T4 RNA ligase , 25% PEG 8000 and 1 mM CoCl3 ( NH3 ) 6 , and purified with HighPrep PCR beads ( MagBio ) . Ligated products were treated for 3 min at 85°C . The ARC76–ARC77 adaptor was annealed to the second strand for 5 min at room temperature . The second strand was synthesized with 4 U of T7 DNA polymerase ( New England BioLabs ) and purified with HighPrep PCR beads ( MagBio ) . Libraries were purified , quantified with a Qubit fluorometric instrument ( ThermoFisher Scientific ) and 50-base paired-end sequenced on an Illumina NextSeq500 instrument , to identify the location of the free 5´-ends . All reads were trimmed for quality and adaptor sequence with cutadapt 1 . 2 . 1 ( -m 15 -q 10–match-read-wildcards ) . Pairs with one or both reads shorter than 15 nt were discarded . Mate 1 of the remaining pairs was aligned to an index containing the sequence of all oligos used in the preparation of these libraries with bowtie 0 . 12 . 8 ( -m1 -v2 ) , and all pairs with successful alignments were discarded . Pairs passing this filter were subsequently aligned to the hg38 H . sapiens reference genome ( -m1 -v2 -X10000–best ) . Single-end alignments were then performed with mate 1 of all unaligned pairs ( -m1 -v2 ) . Using the–m1 setting causes Bowtie to discard all reads which align to multiple places in the genome , including nuclear mitochondrial DNA segments ( NUMTs ) . To calculate the base identity of ribonucleotides in mtDNA , the count of 5′-ends of all paired-end and single-end alignments were determined for all samples and shifted one base upstream to the location of the free 5′-end or hydrolyzed ribonucleotide . For visualizing reads aligning to the entire mitochondrial genome in Fig 1 , the pipeline described above was used with the difference that the–m1 flag was not used . The counts were normalized to reads per million ( RPM ) and visualized using Circos software . To estimate the number of ribonucleotides per mitochondrial molecule the mtDNA cut with HincII was used . For each of the H- and L strands , the reads at each position were first normalized to reads per million . Subsequently , the mean number of reads at the eleven HincII sites , including reads five positions upstream and downstream thereof , was calculated . The reads from these double-stranded breaks give a relative quantification of all of the mitochondrial molecules . Dividing the total reads in the region between 300 and 16 , 200 and excluding the OriL region from 5 , 747–5 , 847 for each strand , not including reads at the eleven HincII sites , with the mean number of reads gives the number of ribonucleotides per single strand break , i . e . the number of ribonucleotides per mitochondrial molecule . To calculate ribonucleotide incorporation frequency , we made use of HydEn–seq libraries . For each of the H- and L- strands , the number of reads in the region between 300 and 16 , 200 and excluding the OriL region from 5 , 747–5 , 847 for each of the four ribonucleotides was normalized to the mean number of reads in HincII sites . This gives the number of each individual ribonucleotide per mitochondrial molecule . This number was converted to incorporation frequency by dividing by the total number of the complementary nucleotides in mtDNA ( in the region between 300 and 16 , 200 and excluding the OriL region from 5 , 747–5 , 847 ) and scaled by a factor of 1 , 000 . For each strand and library the relative incorporation percentage of each ribonucleotide was calculated . In samples treated with HincII the reads mapping to those recognition sites were excluded prior to calculating percentages . The percentage data was then re-scaled on a per strand and ribonucleotide basis to a z-score with a mean of 0 and a standard deviation of 1 . Hierarchical clustering was performed on the re-scaled data using the “heatmap” function from the “stats” package in R . HeLa cells were trypsinized followed by wash with PBS . Cells were resuspended in lysis buffer ( 10 mM Tris-HCl pH 8 . 0 , 0 . 1 M NaCl , 25mM EDTA pH 8 . 0 and 0 . 5% SDS ) and incubated at 55°C for 2 hours followed by phenol extraction and ethanol precipitation to isolate DNA . Samples were resuspended in TE buffer ( 10 mM Tris-HCl pH 8 . 0 , 1 mM EDTA ) overnight . Total DNA ( 24 μg ) was digested with BamH1-HF and thereafter supplied with 0 . 3 M NaCl and 300 ng RNaseA to remove single-stranded RNA . The DNA was precipitated and resuspended in TE buffer and aliquoted into two tubes . The samples were treated with either 0 . 3 M KCl or 0 . 3 M KOH , for 2 h at 55°C in a hybridization oven and then aliquoted into three and run on a 0 . 8% alkaline agarose gel . The samples were transferred to a nylon Hybond-N+ membrane ( GE Healthcare ) and UV-crosslinked . Strand specific probes ( L-strand or H-strand ) against the human mtDNA molecule were hybridized to the DNA as indicated in the figure legends . Probe sequences are available from the authors upon request . Human recombinant POLγA and POLγB ( WT and EXO- versions ) , were expressed and purified as described previously [46] . A 70-nt oligonucleotide ( 5´-ATG ACC ATG ATT ACG AAT TCC AGC TCG GTA CCG GGT TGA CCT TTG GAG TCG ACC TGC AGA AAT TCA CTG G-3´ ) was annealed to a 40-nt DNA oligonucleotide ( 5´-CCA GTG AAT TTC TGC AGG TCG ACT CCA AAG GTC AAC CCG G-3´ ) labeled in the 5´-end with [γ-32P] ATP to produce a primed-template that can be used as a substrate for DNA polymerization [57] . The reaction mixture ( 20 μl ) contained 600 fmol of the DNA template , 25 mM Tris-HCl , pH 7 . 8 , 1 mM DTT , 10 mM MgCl2 , 100 μg/ml BSA , 4 μM dNTP , 600 fmol of WT or EXO- POLγA , 1200 fmol POLγB and each NTP to final concentrations of; 0 μM , 40 μM , 100 μM , 400 μM , 1 mM , or 4mM rATP , rUTP , rCTP and rGTP respectively . The reaction was incubated at 37°C for 30 min and stopped by the addition of 20 μl formamid loading buffer ( 95% formamid , 25 mM EDTA , 10 mg/ml bromophenol blue , 10 mg/ml xylene cyanol ) . The samples were loaded on a 7 M urea , 8% polyacrylamide sequencing gel in 1 × TBE buffer . Full-length products were excised from the gel and precipitated with ethanol precipitation and resuspended in TE buffer . The products were counted in a scintillation counter and equal amount of counts from the samples were mixed with either KOH or KCl to a final concentration of 0 . 3 M . The samples were incubated at 55°C for 2 hours or treated with RNase H2 at 37°C for 1 hour . The reactions were stopped by addition of formamide loading buffer and analyzed by electrophoresis on a 7 M urea , 8% polyacrylamide sequencing gel in 1 × TBE buffer and signals were visualized by autoradiography . Quantification was performed in MultiGauge and the results are the average from three independent experiments . | Human mitochondria contain a small double-stranded DNA genome ( mtDNA ) of only 16 , 569 base pairs ( bp ) that encodes 13 essential subunits of the oxidative phosphorylation system . Depletion of mtDNA and different types of mtDNA mutations cause mitochondrial disease , and are also implicated in biological ageing . For almost half a century it has been known that mtDNA contains ribonucleotides , but their identity and precise location are not known . The source of these ribonucleotides and their relevance for mitochondrial genome stability in healthy individuals and in patients with mitochondrial defects has not been addressed . We have used a combination of next-generation sequencing , and in vivo and in vitro biochemistry to address some of these questions . Our findings demonstrate that DNA polymerase γ-dependent incorporation is the main source of ribonucleotides in mtDNA and argues against the existence of ribonucleotide excision repair pathways in human mitochondria . Our data also reveal that when dNTP pools are limiting , ribonucleotides serves as a second line of building blocks for DNA synthesis . We also demonstrate increased levels of embedded ribonucleotides in patient cells with disturbed nucleotide pools , which may constitute a new pathogenic mechanism that affects mtDNA stability and impairs later rounds of mtDNA replication . | [
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] | 2017 | Nucleotide pools dictate the identity and frequency of ribonucleotide incorporation in mitochondrial DNA |
Accurate estimation of neuronal receptive fields is essential for understanding sensory processing in the early visual system . Yet a full characterization of receptive fields is still incomplete , especially with regard to natural visual stimuli and in complete populations of cortical neurons . While previous work has incorporated known structural properties of the early visual system , such as lateral connectivity , or imposing simple-cell-like receptive field structure , no study has exploited the fact that nearby V1 neurons share common feed-forward input from thalamus and other upstream cortical neurons . We introduce a new method for estimating receptive fields simultaneously for a population of V1 neurons , using a model-based analysis incorporating knowledge of the feed-forward visual hierarchy . We assume that a population of V1 neurons shares a common pool of thalamic inputs , and consists of two layers of simple and complex-like V1 neurons . When fit to recordings of a local population of mouse layer 2/3 V1 neurons , our model offers an accurate description of their response to natural images and significant improvement of prediction power over the current state-of-the-art methods . We show that the responses of a large local population of V1 neurons with locally diverse receptive fields can be described with surprisingly limited number of thalamic inputs , consistent with recent experimental findings . Our structural model not only offers an improved functional characterization of V1 neurons , but also provides a framework for studying the relationship between connectivity and function in visual cortical areas .
The fundamental assumption underlying early sensory processing is that different external stimuli elicit distinct activity patterns that encode the content of the stimuli . Patterns of neuronal activity in early sensory areas of cortex are themselves a product of the network in which the neurons are embedded [1 , 2] . Understanding the relationship between stimuli and responses in a given neural population , and how these responses are created by the underlying neural circuits , is thus essential for explaining the role of these neurons in sensory processing [3] . A common approach for identifying stimulus-response functions is to present a large set of stimuli while recording the responses of individual neurons , and subsequently fit each neuron with a model . The accuracy of the model can be determined by comparing the predicted and actual activities in responses to a novel stimulus set . This data-driven approach to describing stimulus response functions ( e . g . spatio-temporal response functions , STRFs ) of neurons in the visual system has been refined over the last four decades . Initially , the filter functions of functionally linear neurons in the retina , lateral geniculate nucleus ( LGN ) , or simple cells in primary visual cortex ( V1 ) were obtained using artificial sets of stimuli , such as sparse noise or M-sequences [4–6] . More recently , studies advanced to describing the response functions of less linear neurons ( complex cells in primary visual cortex , and neurons in V2 ) [7 , 8] while using stimuli more representative of the natural environment , such as sequences or movies of natural scenes [9–12] . However , even in V1 , the modest response prediction accuracy from these models indicates that our current ability to characterize the stimulus response functions is incomplete [8 , 10 , 13] . Several major advances in the estimation of response functions have been introduced in recent years . Spike-triggered covariance ( STC ) [7] and multi-layer neural networks [13–15] made it possible to estimate the non-linear receptive fields ( RFs ) of complex cells . Most previous methods for estimating RFs in the early visual system have dealt with data from single cells independently [4–6 , 16 , 17] . The introduction of generalized linear models ( GLMs ) showed that incorporating information about the activity of nearby neurons a few milliseconds in the past can significantly improve predictive power [2] , but this technique is restricted to a linear representation of the receptive field . More recently , usage of pre-defined banks of linear and non-linear filters to pre-process the visual input , and then using linear regression in this transformed input space to fit the model , has improved prediction accuracy [8 , 18] . However , no RF estimation method has taken advantage of the fact the RFs of a local population of neurons are constructed from a limited number of shared LGN inputs [19] , which have stereotypical center-surround RF structure . The advent of two-photon calcium imaging makes it possible to record the activity from complete local populations of neurons [20 , 21] , and thus allows estimation of a model containing these constraints . Here we propose a new method for estimating RFs in V1—the Hierarchical Structural Model ( HSM ) —which assumes that a local neuronal population shares a limited number of afferent inputs from the LGN . The model explicitly incorporates hierarchical sub-cortical and cortical processing , whereby center-surround thalamo-cortical inputs are summed in the first layer of neurons , consisting of putative simple cells , followed by a second layer of neurons that sum inputs from simple cells to form both simple and complex-cell like RFs . The model takes advantage of the RF redundancies among nearby V1 neurons , by simultaneously fitting the entire local population of recorded neurons , and outperforms current state-of-the-art approaches to RF estimation when predicting neuronal activity measured with two-photon calcium imaging .
Our model-based approach to RF estimation is inspired by the anatomical and functional organization of mammalian V1 ( see Fig 2 and Materials and Methods ) . It is based on the following basic assumptions: LGN units can be well described as difference-of-Gaussian functions [24]; the local population of V1 neurons shares input from limited number of such LGN units [19 , 25]; simple cells can be constructed by summing several RFs of LGN neurons [26 , 27]; complex cells can be constructed by summing inputs from the local population of simple cells that are selective to the same orientation but different RF phases [27] . The HSM consists of 3 layers of units: the first layer consists of linear kernels of LGN units that are modeled as 2D difference-of-Gaussians functions ( see Fig 2 ) . Units in the second layer sum the responses of LGN-like linear units , and pass on the resulting potential via a logistic-loss non-linearity . In this way units in the second layer construct oriented RFs through feed-forward summation of thalamocortical inputs [27] . Linear summation coupled with logistic-loss non-linearity is repeated again in the third layer , which enables construction of RFs that are tuned to orientation but can be insensitive to spatial phase ( i . e . units resembling complex cells ) . Moreover , this approach also allows the generation of RFs that do not conform to the standard idealized models of either simple or complex cells , including , for example , models of cells that are selective to two orthogonal orientations . The HSM therefore leverages the assumed local connectivity in V1 , thus potentially improving fits on limited data , while fitting RFs that do not conform to the idealized models of V1 neurons , a requirement that may be important for capturing the full response variability of V1 neurons . We estimated the RFs for each neuronal population in V1 by applying a gradient ascent method to optimize corresponding log-likelihood functions with respect to the model parameters to reproduce responses to the set of training visual stimuli ( see Materials and Methods ) . To demonstrate the results of fitting the HSM to a local population of mouse V1 neurons , we plotted the linearized RFs in cortical space with each RF centered on the location of the corresponding neuron’s cell body in the imaged region ( Fig 3 ) . The color of the frame around each neuron’s RF represents the value of the non-linearity index ( NLI; see Materials and Methods ) for the given neuron , indicating the portion of the predicted responses which is due to non-linear as opposed to linear aspects of the HSM , and can be considered as an estimate of a neuron’s non-linearity . We observed a diversity of RF shapes in local regions of mouse V1 , with a full range of linear and non-linear characteristics ( Fig 3 ) . To estimate the performance of the HSM , we measured the correlation between responses predicted by the fitted model and evoked responses to a novel set of validation images ( 50 images , responses averaged over 8–12 trials ) that were not included in the training set ( Fig 4 ) . The top neuron depicted in Fig 4A was the best fit neuron ( R = 0 . 9; p<0 . 001 ) . The model predicted its response with high accuracy , apart from small response deviations at lower response amplitudes . The neuron in Fig 4B exhibited the median correlation between predicted and recorded responses ( R = 0 . 53; p<0 . 001 ) , where the predicted response captured a considerable part of the neural response , but significant deviations from the measured activity were still observed . The correlation coefficients of neurons from all three imaged regions in V1 were broadly distributed across a range of positive values , with few neurons showing weak anti-correlation ( median values of 0 . 53 , 0 . 45 and 0 . 47 respectively; p<0 . 001 for all three regions; Fig 4C ) . We found a strong negative relationship between the normalized noise power [28] in recordings of individual neurons and the performance of the model for those neurons ( Fig 4D ) . This is because response reliability of neurons has a significant impact on the ability of the model to fit individual neurons , as less reliable responses carry less information about the stimulus , and the mean response from the validation image set is likely to deviate more from the true average response for less reliable neurons . This predicts that collecting a larger training set and increasing the number of repetitions in the validation set would further improve the prediction power of the model . In the previous section we showed that the HSM can predict well the responses of many neurons to novel natural stimuli . How does the prediction performance of our model compare to other models , including a regularized variant of the linear-nonlinear model ( rLN ) [9] , and the Berkeley wavelet transform ( BWT ) model [29] ? Due to its simplicity and interpretability , the linear-nonlinear model is the standard approach for RF estimation and has been used in a wide range of studies [30] . On the other hand , the recently proposed BWT model ( together with the closely related Gabor pyramid variant [18] ) represents the state-of-the-art in neural response prediction to naturalistic stimuli , as it can capture linear and non-linear components of the neuronal responses and outperforms most RF identification methods [31] . Fig 5A compares the performance for the three models ( measured as the correlation coefficients of measured and predicted responses to a novel set of natural images ) averaged across all neurons in the three recorded cortical regions , while the line-graphs show the performance for the three cortical regions separately . The rLN and the BWT methods achieved averaged performances of R = 0 . 29 and R = 0 . 39 respectively , while the HSMs outperformed both consistently across all three regions ( P<0 . 001; Wilcoxon signed ranked test; data pooled across the three regions ) showing an average correlation of R = 0 . 47 ( a 20% improvement over the BWT model ) . Furthermore , if we fit the HSM to each neuron individually , we see that the prediction performance drops to the levels shown by rLN ( the HSM ( SN ) condition , R = 0 . 30 ) . This indicates that the predictive advantage of HSM largely arises from our ability to constrain the fitting problem by the assumption of limited common feed-forward input into a local population of neurons in V1 . To quantify the proportion of the neuronal response captured by our model , we computed the fraction of explained variance ( FEV ) [28] . Since high quality multi-trial data is required to reliably calculate FEV , for this analysis we excluded neurons with normalized noise power greater than 70% ( sparing 70 of the 260 imaged neurons ) . The average fraction of variance explained by the rLN and the BWT models was 0 . 16 and 0 . 30 respectively , while the HSMs outperformed both ( P<0 . 001; Wilcoxon signed ranked test; data pooled across the three regions ) , achieving an average of 0 . 43 , representing a 43% improvement over the BWT model ( Fig 5B ) . Finally , the improvement in the average prediction ( fraction of explained variance ) of the HSM was not restricted to subset of neurons , but spread almost across the entire measured population ( Fig 5C and 5D ) . The HSM contains two free meta-parameters that are not optimized during the fitting process: the number of LGN units and the number of neurons in the hidden layer ( which we express as the fraction γ of recorded neurons in an imaged region ) . To assess the influence of these parameters on the performance of the HSM , we performed a parameter search . Due to the high computational requirements of the fitting process and the large number of neurons in our dataset we were not able to explore the full space of these two parameters . Instead we performed a partial one-dimensional search through the parameter space , varying one parameter and fixing the other to a value we found empirically to give good performance ( 9 for the number of LGN units and 20% for the number of hidden units expressed as fraction of imaged neurons ) . We limited the number of fitting restarts with different initial seeds for each explored value of the meta-parameters to 20 . The performance of the model on the training set initially increased with the number of LGN inputs ( Fig 6A , full lines ) , however , beyond ~9 LGN units the performance saturated . We observed a similar pattern in the relationship between hidden layer size and performance of the model on the training set ( see Fig 6B , full lines ) . Initially , the performance increased with increasing hidden layer fraction , however , it quickly saturated at a value of ~0 . 2 . A similar relationship between the performance of the model and the meta-parameter values exists when measured against the validation set ( Fig 6 , dashed lines ) . Overall , surprisingly few LGN inputs and hidden units are required to capture the responses of large local populations of neurons ( >100 neurons ) . This observation is consistent with the recent evidence showing limited variability in the location of RF subunits in local populations of mouse V1 neurons [19] . It should however be emphasized that the values of these two parameters are very likely an underestimation of the true number of LGN cells innervating the imaged cortical region , and the number of linear ( simple ) cortical cells from which the imaged neurons receive inputs . These numbers are a reflection of the number of parameters we can resolve given our limited training sets . We expect that larger amounts of data would lead to a slight increase in the number of subunits , and a more accurate correspondence between the fitted model parameters and the underlying neural substrate . It is important to emphasize that the estimated HSM parameters are unlikely to reflect a direct one-to-one relationship with the underlying biological substrate , but rather offer a functional description of the system . To gain a insight into how the HSM captures the responses of the fitted neurons , we show the linear RFs of all the units in the LGN and intermediate layer of the fitted HSM ( Fig 7A and 7B ) . Note that such linear visualization is not possible for the output layer units , due to the non-linearity of the hidden layer unit transfer functions . Unsurprisingly , as a direct consequence of their definition in the HSM , the LGN kernels have isotropic center-surround structure , but some have very weak surround components . Some intermediate units express RFs that can be well described by Gabor functions , yet others have more unusual shapes , while we also observe multiple cells with similar RFs . This is not surprising given that the number of hidden units used in the HSM models was much smaller ( < = 20 ) than the number of linear ( simple ) cells that can be expected to reside in the corresponding region of V1 . Consequently , it is unlikely that the fitted HSM hidden units correspond to RFs of individual neurons in the imaged area , but rather to a low-dimensional subspace in which neural responses are generated . Advanced model regularization and selection methods could in future improve the link between the HSM units and biological neurons , but these will require collection of substantially more data than available in our experiments to be effective . How then are the hidden layer RFs combined to generate the fitted RFs of the measured neurons in the HSM ? Fig 7C shows the weight matrices between the hidden and output layer for all three regions . Each weight matrix is relatively dense . Based on theories about complex cell construction including the energy model [32] or the STC analysis , which suggest a relatively limited number of linear filters in complex cells [7 , 10] , one would expect these matrices to be sparse . This does not appear to be the case in the HSM , as is suggested by the negative kurtosis values of the weight distributions Fig 7D . One possibility is that these dense weight matrices are the consequence of over-fitting . Another option is that the model hidden units correspond to linear presynaptic cells ( or linear combinations of a set of such cells ) and the dense weight matrices between the hidden and output layer reflect the fact that single cortical neurons receive a large number of connections from other local cortical neurons [33] . Finally , the small number of hidden HSM units means that a combination of larger numbers of them might be needed to obtain the RFs of the individual recorded neurons , leading to a lack of sparseness in the weight matrices . In the future , simultaneous imaging of layer 4 and 2/3 will be required to resolve this question experimentally . Finally , we examined the evidence for spatial organization of two model measures in the local cortical network ( Fig 9 ) . We found no evidence for a relationship between cortical distance and non-linearity index ( Fig 9A and 9C; R < 0 . 05 , P>0 . 1 ) or model prediction power ( Fig 9B and 9D; R < 0 . 05 , P>0 . 1 ) . These results are consistent with the findings of several previous studies that—with the exception of retinotopic position—failed to find a spatially organized arrangement of neurons of similar RF properties in local cortical networks of rodent V1 [21 , 34] .
In this study we introduce a novel model-based method for estimating RFs simultaneously in large populations of V1 neurons , under the assumption that the population shares input from a limited number of thalamo-cortical afferents . We applied the novel model ( HSM ) to recordings of local populations of neurons in L2/3 of mouse V1 in response to natural images . Our model can explain significant proportions of signal variance of reliably responding neurons in V1 and improves upon existing rLN [11] and BWT models [29] when applied to the same data . The improved performance of HSM in comparison to the rLN is due to its greater expressive power . The two layers of non-linearity allow the HSM to account for responses of non-linear cells , such as the complex cells in V1 . Furthermore , we have also tested an advanced linear method , the automatic locality determination ( ALD ) [35] , but when applied to our data this method showed very similar performance to the rLN model , yielding correlations in the validation set of 0 . 31 , 0 . 24 and 0 . 299 in the three recorded regions . When compared to the BWT model , HSM still has the advantage in expressiveness for it allows constructions of RFs that deviate from the stereotypical Gabor-like RFs imposed into BWT structure . Moreover , HSM better constrains the optimization problem by assuming shared afferent input into a cortical column . Thus , unlike rLN or BWT , HSM fits all recorded neurons simultaneously . This makes the HSM parameters constrained by the responses of all recorded neurons , effectively increasing the amount of data available for fitting . Finally , it should be noted that we have also attempted to apply the STC method to the presented data . However , due to the dependence of STC on very large datasets , when applied to our limited data , STC failed to identify any significant eigenvectors for the majority of neurons [10] . This further highlights the advantages of the HSM in estimation of non-linear RFs from limited data . Recent experimental studies have indicated that local populations of V1 neurons in cats and mice share a limited number of inputs from LGN [19] [25] . Here we offer further support for this hypothesis , by showing that a model assuming feed-forward convergence of thalamic afferents , shared among a population of neighboring cortical neurons , resulted in RF estimates with better predictive power than previous models ( see Fig 5 ) . Additionally , removing the assumption of pooled hierarchical input resulted in dramatic drop in quality of estimated RFs ( see Fig 5 ) . We show that as few as 9 LGN-like units are sufficient to explain a significant proportion of the stimulus dependent responses in a local population of L2/3 neurons within a ~300x300μm field of view . This is particularly remarkable given the diversity of RFs observed in local populations of mouse V1 neurons ( Fig 3 ) [34] . It is important to emphasize that the estimated structure of the HSM cannot be interpreted as direct evidence of the underlying connectivity . This is obvious in the case of the hidden model units which are orders of magnitude fewer in the HSM models used in this study than the expected number of layer 4 neurons within the corresponding region of mouse V1 . It is also very likely that we underestimate the actual number of thalamic neurons innervating the imaged regions of mouse V1 . This might be because multiple LGN neurons with similar RFs will be approximated with a single LGN DoG model , consistent with a recent study that directly mapped the RFs of LGN axons in V1 [36] . Further advances in recording techniques that will allow collection of more data ( both in terms of image presentation and sampling ratio of the local neural population ) should impose more constraints on the fitting of HSM parameters and thus offer a closer picture of the underlying neural substrate . Furthermore , additional prior knowledge ( if available ) , such as the cortical depth/layer membership of the neurons , or neural type , could be incorporated into the HSM model to further constrain its parameter estimation . Unlike most previous approaches to RF estimation , the optimization problem HSM poses is not convex , and thus finding the global optimum is not guaranteed . But HSM still outperforms other methods because the non-convexity of the optimization is favorably compensated by its better expressive power . Importantly , the quality of the RF estimation is determined by the optimization algorithm used to fit the HSM parameters . We found the truncated Newton conjugate method worked well for the present form of the model , but adding further nonlinearities into the model dramatically decreased its performance . This is likely because adding nonlinearities transformed HSM into a so-called “deep learning” problem which is known to be difficult to optimize . However , the recent advances in optimization techniques applicable to deep-learning problems [37] could improve the fitting of HSM and allow inclusion of additional nonlinear mechanisms . The limited number of natural images that we could present during each imaging experiment and the relatively slow time-course of spike-related calcium signals constrained this study in three important ways . First , we did not consider the temporal properties of RFs , because the slow sampling ( 7 . 6Hz ) and kinetics ( 100s milliseconds ) of the calcium signals did not lend this dataset to the analysis of fine-scale temporal response properties of RFs . Second , we did not utilize ‘early-stopping’ criteria to prevent over-fitting , as they require an extra dataset to be separated out of—in our case very limited—training set . We found that such reduction of the training set outweighed the gains due to early stopping . Third , estimation of couplings between neurons using the GLM method has previously been shown to greatly improve prediction power . However , we did not include coupling filters in HSM , as their estimation relies on fine-scale temporal sampling of the recorded neural activity [2] . Improvements in functional imaging , including higher sampling rates , improved signal-to-noise ratio , faster calcium indicator kinetics , voltage-based indicators , and better spike estimation techniques , will allow for more accurate reconstructions of underlying spike-trains in large populations of imaged neurons . Moreover , recent applications of genetically encoded calcium indicators and chronic preparations [38 , 39] could greatly increase the number of possible stimulus presentations in anaesthetized and awake mice . Overall , such advances would make it possible to overcome all the limitations discussed above , and thus provide a framework for further improvements in understanding the relationship between connectivity and functional properties of V1 and higher visual cortical areas .
All experimental procedures were carried out in accordance with institutional animal welfare guidelines and licensed by the UK Home Office . Experiments were performed on C57Bl/6 mice between postnatal day 30 and 40 . Mice were anesthetized with a mixture of fentanyl ( 0 . 05 mg/kg ) , midazolam ( 5 . 0 mg/kg ) , and medetomidine ( 0 . 5 mg/kg ) . During Ca2+-imaging experiments , light anesthesia was maintained by Isoflurane ( 0 . 3–0 . 5% ) in a 60:40% mixture of O2:N2O delivered via a small nose cone . Surgically , a small craniotomy ( 1–2 mm ) was carried out over primary visual cortex and sealed after dye injection with 1 . 6% agarose in HEPES-buffered artificial cerebrospinal fluid ( ACSF ) and a cover slip . For bulk loading of cortical neurons the calcium-sensitive dye Oregon Green Bapta-1 AM ( OGB-1 AM; Molecular Probes ) was first dissolved in 4 μl DMSO containing 20% Pluronic , and further diluted ( 1/11 ) in dye buffer ( 150 mM NaCl , 2 . 5 mM KCl and 10 mM HEPES ( pH 7 . 4 ) ) to yield a final concentration of 0 . 9 mM . Sulforhodamine-101 ( 50 μM , Molecular Probes ) was added to the solution for experiments in C57Bl/6 mice to distinguish neurons and astrocytes [40] . The dye was slowly pressure injected into the right visual cortex at a depth of 150–200 μm with a micropipette ( 3–5 MΩ , 3–10 psi , 2–4 min ) under visual control by two-photon imaging ( 10x water immersion objective , Olympus ) . Activity of cortical neurons was monitored by imaging fluorescence changes with a custom-built microscope and a mode-locked Ti:sapphire laser ( Mai Tai , Spectra-Physics ) at 830 nm through a 40x water immersion objective ( 0 . 8 NA , Olympus ) . Scanning and image acquisition were implemented in custom software ( Labview , NI ) . The average laser power delivered to the brain was <50 mW . Imaging frames of 256x256 pixels were acquired at 7 . 6 Hz . After each recording the focal plane and imaging position was checked and realigned with the initial image if necessary . Image sequences were aligned for tangential drift and analyzed with custom programs written in ImageJ ( NIH ) , Matlab ( Mathworks ) and Labview ( NI ) . Recordings with significant brain movements , vertical drift , or both were excluded from further analysis . Cell outlines were detected using a semi-automated algorithm based on morphological measurements of cell intensity , size , and shape , and subsequently confirmed by visual inspection . After erosion of the cell-based regions of interest , to minimize influence of the neuropil signal around the cell bodies , all pixels within each region of interest were averaged to give a single time course ( ΔF/F ) , which was additionally high-pass filtered at a cut-off frequency of 0 . 02 Hz to remove slow fluctuations in the signal . Unresponsive neurons during spontaneous and evoked conditions were excluded from further analysis , by testing whether , for each cell , the distribution of all fluorescence values was not significantly different ( i . e . positively long-tailed ) from a random , normal distribution ( Kolmogorov-Smirnov goodness-of-fit test ) . Astrocytes labeled with Sulforhodamine 101 ( red fluorescence ) were excluded from the analysis . Spike trains were inferred from calcium signals using a fast non-negative de-convolution method which approximates the most likely spike train for each neuron , given the observed fluorescence [22] . The deconvolved traces represent estimates proportional to the number of action potentials emitted during the corresponding period . These proportional estimates were calibrated based on simultaneous cell attached recordings and calcium imaging in individual neurons [23] to represent the estimated number of emitted spikes . The inferred spike trains were further processed by computing the sliding average with a window of three frames . This was done to offset the biases introduced by the temporal quantization due to the relatively slow data acquisition rate . Stimuli were presented on 60 Hz LCD monitors , at a resolution of 1024×768 pixels . A retinotopic mapping protocol was used to ensure that the monitor covered the RF of recorded neurons: a patch of moving gratings was presented at 12 different locations on the screen , for 1 . 4 s in each location with a gap of 1 . 5 s between locations . The monitor was repositioned such that the preferred retinotopic position of most imaged neurons was roughly in the middle of the monitor . The stimulus set was composed of static scenes from David Attenborough’s BBC documentary Life of Mammals , depicting natural scenes such as landscapes , animals or humans . Images were scaled to have 256 equally spaced luminance steps , and were composed of 384×208 pixels , and expanded to fill the screen . Each image appeared in the stimulus set four times , in the original form , flipped horizontally and flipped vertically , and with reversed contrast . The onset of image presentation was aligned with the frame rate of the scanning . To account for the noise and the dynamics of somatic calcium signals , we applied a relatively slow visual stimulation protocol ( total time = 1974 ms per image presentation , i . e . 15 imaging frames at 7 . 6 Hz ) to obtain reliable responses of V1 neuronal populations to the naturalistic stimuli . Images were presented for 500 ms , and interleaved with blank grey screens presented for 1474 ms ( see Fig 1A ) . Averaging of the resulting calcium traces across all stimulus presentations and neurons revealed the typical onset and offset dynamics of the neural responses . We define the response of a neuron to single image presentation as the average number of spikes inferred by the spike extraction algorithm across imaging frames 3–7 , which we identified to hold the bulk of the onset signal , but likely also an early component of the offset response . This way , for each imaged region , we obtained two datasets of values . The first is an n×m matrix corresponding to the responses of each of the m recorded neurons to n single trial image presentations , which we refer to as the training set ( see Fig 1B ) . Additionally , in each region we recorded responses to 8–12 presentations of another 50 images forming the second dataset , a 50×m×r matrix , we will refer to as the validation set . Three regions in two animals were recorded , containing 103 , 55 and 102 neurons , while presenting sequences of 1800 , 1260 and 1800 single trial images , respectively . The images were presented in partially interleaved manner . The training images were divided into 10 blocks . Additional blocks were formed by the 50 validation images , in each of these blocks the 50 images were presented multiple times . During the experiment the resulting stimulation blocks were presented in random order . For each region , we ran a rLN fitting protocol with full-field stimuli to determine the rough position and size of all the neurons' RFs . Consistent with retinotopic map in mouse V1 , in all three recorded regions all recovered RFs were located in a restricted region of visual space . This allowed us to determine a region of interest in the visual space , centered on the set of initially recovered RFs and spanning roughly two times the area they covered . The images were constrained to this region of interest and then down-sampled to 31×31 pixels to form the input stimuli set , which was used in all the subsequent analysis . The HSM is a feed-forward network consisting of three fully connected layers ( see Fig 2 ) . The first layer corresponds to units found in LGN , represented as difference-of-Gaussian kernels . The output of the i-th unit in the LGN layer to an image I is computed as follows: ψi1 = ∑k , lIkl ( αiσi2 e− ( k−μix ) 2+ ( l−μiy ) 22σi2−βiρi2 e− ( k−μix ) 2+ ( l−μiy ) 22ρi2 ) ( 1 ) where ψi1 is the output of the i-th unit in the first layer containing LGN units , Ikl is the image intensity at coordinates k and l , σi and ρi are the widths of the center and surround Gaussians of the i-th LGN unit , μix and μiy are the x and y center coordinates of the i-th LGN unit , and αi and βi are the weights of the center and surround respectively . The two ‘cortical’ layers consist of simple linear integrators with a logistic-loss output non-linearity: ψil= f ( ∑jwijψj ( l−1 ) ) ( 2 ) where ψil , l ∈ {2 , 3}is the output of the i-th unit in layer l , wij is the weight from unit j to unit i and f is a logistic-loss transfer function: f ( x ) = log ( 1+exp ( x−ti ) ) ( 3 ) where ti is the threshold of unit i . Thus , the free parameters of the model are the 6 parameters per LGN unit ( αi , βi , μiy , μiy , σi , ρi ) , one parameter per each cortical unit corresponding to its threshold ti and the weights between the layers , totaling 6s1 + s2 + s3 +s1s2 + s2s3 parameters , where Si is the size of the layer i . Assuming Poisson spiking , we fit the HSM via a maximum-likelihood method by performing gradient descent on the corresponding log-likelihood function [41]: log p ( y|x , ϕ ) = ∑iyilogM ( ϕ , xi ) −∑iM ( ϕ , xi ) ( 4 ) where y are the measured neural responses , x are the input patterns , ϕ are the free parameters of the model and M ( ϕ , xi ) =ψ→3 is the output of the model to image xi . To implement the model and search for optima of its log-likelihood function we use the theano package [42 , 43] in combination with the constrained truncated Newton conjugate method ( implemented by the Python scipy . fmin . tnc function ) . This optimization method allows constraining the parameters to lie within intervals , allowing us to enforce the centers of LGN units to lie within the image , and the width of the center and surround Gaussians of LGN units to be positive and smaller than the width of the image . The model has two free meta-parameters that are not set by the fitting process: the number of LGN units ( s1 ) and the number of units in the hidden layer ( s2 = γs3 ) , where γ expresses the number of hidden units as a fraction of the number of output units . We performed a systematic search for these two parameters with respect to the performance of the model on the training set . We found that the performance of the model on the training set quickly saturates when increasing the number of LGN inputs and the fraction of hidden units , at values of about s1 = 9 and γ = 0 . 2 respectively . Therefore , in order to prevent over-fitting and facilitate simple comparison , we decided to use these low values of the two free meta-parameters for fitting of all the three regions , in all the other analysis . The optimization problem posed by the HSM log-likelihood function is not convex . Therefore we are not guaranteed to find a global optimum and thus the solution found , and consequently its performance , will be dependent on the initial parameter values . To lessen this dependence on initial parameters , each fitting was run multiple times with different randomly seeded initial parameter values , and followed by selecting the model with the best performance on the training set . The initial model parameterizations were obtained by specifying ranges for each HSM parameter , and then randomly selecting values uniformly from within these ranges . For example for the position parameters of the LGN DoG kernels we set the ranges to correspond to the extent of the input images . For the parameter search experiments we performed 20 restarts of the fitting algorithm for each meta-parameter combination , while for the rest of the analysis with the selected meta-parameters we performed 50 restarts . To show that the initial parameter restarts are an effective method , we have fitted the HSM model using 100 different initial parameterizations ( S1 Fig ) . The performance of the fitted HSM model on the training set was correlated with the performance on the validation set across the set of initial seeds ( panel A in S1 Fig ) . Even though the different initial conditions lead to solutions with similar responses and prediction power ( S1 Fig ) , these solutions correspond to considerably different estimates of the HSM parameters ( even if we account for some basic ambiguities: see S1 Fig caption for details ) . We observe analogous results if we fix the initial conditions , but instead fit HSM on 100 sub-samples of the training set ( S2 Fig ) . Overall , the HSM method is successful in consistently finding good functional descriptions of a neuron’s stimulus-response function , but these solutions are not unique , and many different HSM parameterizations can lead to the same input-output relationships . Such many-to-one mappings between HSM parameterizations and input-output functions are consistent with previous observations in machine learning and computational neuroscience [44–47] . It remains to be seen if incorporating additional constraints by increasing the amount of data for training , or obtaining more complete samples of local neuronal populations , or obtaining other experimental observables ( e . g . layer membership or cell type of the recorded neurons ) or further extending the HSM model ( eg . with coupling filters ) could lead to HSM parameterization that is less sensitive to initial conditions and more closely reflects the underlying biological substrate . The exact time the model training took varied based on the free model parameters , the number of output neurons , and the size of the training set . For the first region ( 103 neurons , 1800 training examples ) and the values of the free parameters used in the model comparison ( see Fig 5 ) the fitting ( a single initial condition ) took approximately 3 hours on a modern 2 . 4MHz CPU with 4GB of memory . To allow for visualization and comparison of the fitted model and to facilitate further analysis we also performed linearization of the model . We did this by fitting the rLN model to the HSM responses to the training set of images . Furthermore , in order to quantify the extent to which the accounted-for portion of the neuronal response predicted by the HSM is linear or non-linear , we define a non-linearity index ( NLI ) as: NLI =LC − max ( LLC , 0 ) LC ( 5 ) where LC corresponds for the correlation between the activities predicted by the HSM and measured activities in response to the validation set of images , and LLC is the corresponding value but for the linearized HSM . For 53 of the 259 neurons for which LC < LLC we set NLI to zero . For a given neuron , NLI will be zero if the correlation between measured activities and predicted activities by HSM is entirely accounted for by the linearized model alone , while the value is 1 if the linear model does not account for any of the correlation—and thus the correlations are due to non-linear aspects of the HSM . In order to assess the performance of the HSM against other RF decoding methods we fitted the same data with a regularized least-squares variant of the LN model [11] , as well as with a recent method using nonlinear Berkeley wavelet transform ( BWT ) decomposition of the stimuli [8 , 29] . | A key goal in sensory neuroscience is to understand the relationship between sensory stimuli and patterns of activity they elicit in networks of sensory neurons . Many models have been proposed in the past; however , these models have largely ignored the known architecture of primary visual cortex revealed in experimental studies , thus limiting their ability to accurately describe neural responses to sensory stimuli . Here we propose a model of primary visual cortex that takes into account the known architecture of visual cortex , specifically the fact that only a limited number of thalamic inputs with stereotypical receptive fields are shared within a local area of visual cortex , and the hierarchical progression from neurons with linear receptive fields ( simple cells ) to neurons with non-linear receptive fields ( complex cells ) . We show that the proposed model outperforms state-of-the-art methods for receptive field estimation when fitted to two-photon calcium recordings of local populations of mouse V1 neurons responding to natural image stimuli . The model demonstrates how the diverse set of receptive fields in the local population of neurons can be constructed from a limited number ( < 20 ) thalamic inputs . | [
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] | 2016 | Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes |
Conservation over three mammalian genera—the mouse , rat , and human—has been found for a subset of the transcripts whose level differs between the adenoma and normal epithelium of the colon . Pde4b is one of the triply conserved transcripts whose level is enhanced both in the colonic adenoma and in the normal colonic epithelium , especially adjacent to adenomas . It encodes the phosphodiesterase PDE4B , specific for cAMP . Loss of PDE4B function in the ApcMin/+ mouse leads to a significant increase in the number of colonic adenomas . Similarly , Pde4b-deficient ApcMin/+ mice are hypersensitive to treatment by the inflammatory agent DSS , becoming moribund soon after treatment . These observations imply that the PDE4B function protects against ApcMin-induced adenomagenesis and inflammatory lethality . The paradoxical enhancement of the Pde4b transcript in the adenoma versus this inferred protective function of PDE4B can be rationalized by a feedback model in which PDE4B is first activated by early oncogenic stress involving cAMP and then , as reported for frank human colon cancer , inactivated by epigenetic silencing .
The use of animal models is furthering our understanding and management of colon cancer as are genomic analyses of human colonic cancers . Animal models provide an under-appreciated advantage not found in the deep-sequencing studies of humans , though . By examining adenomas in mice and rats genetically predisposed to develop colon cancer , we can gain insights from the evolutionary divergence of these species that extend to human cancers . This study explores the strategy of using a pair of animal models for early human colon cancer as sources of information evolutionarily divergent from the human . We assess the power of the transcriptome in each model to predict the transcriptome of the corresponding neoplastic change in the human . We explore the possibility that the power of this test is maximized by using two models , the mouse and the rat , from distinct genera , Mus and Rattus , respectively . The ApcMin/+ mouse [1] , is heterozygous for a nonsense mutation in codon 850 of the Apc “gatekeeper” gene [2] . On the C57BL/6J genetic background , the small intestine of the ApcMin/+ mouse develops 92 ± 33 adenomas in males and 103 ± 32 adenomas in females . By contrast , in the colon males develop only 2 . 9 ± 1 . 8 adenomas and females 1 . 6 ± 1 . 2 adenomas [3] . The ApcPirc/+ rat is heterozygous for a nonsense mutation in codon 1137 of Apc [3] . On the F344/Tac genetic background , the small intestine of the ApcPirc/+ rat develops only 17 ± 7 adenomas in males and 1 . 9 ± 1 . 6 adenomas in females . By contrast , in the colon it develops 20 ± 8 . 9 adenomas in males and 9 . 2 ± 6 . 0 adenomas in females [4] . Since the colon is the predominant site of intestinal neoplasia in the human , the transcriptomes of each genus were analyzed only from colonic adenomas . This study aimed first to seek molecular signals of early colonic neoplasia that are conserved across genera , and then to ascertain which signals are also expressed in the apparently normal colonic epithelium , especially that adjacent to emergent tumors . Early studies of epithelial cancers by clinicians indicated changes occurred in normal epithelial tissue prior to tumorigenesis . The term Field Cancerization refers to “the constellation of locoregional changes triggered by long-term exposure of a field of tissue to a carcinogen , which is not necessarily recognized histologically; the remaining ‘field , ’ despite adequate resection , is grossly normal but more susceptible to future insult” [5 , 6] . Observations interpreted as reflecting field cancerization have been made in most epithelial cancers , including colon cancer [7–11] . Pre-neoplastic mutations in the underlying colonic mucosa could serve as a tumor-enhancing field effect [12] . Tissue inflammation can be considered as one possible source for field cancerization [13] . Inflammatory bowel diseases ( IBD ) , such as Crohn’s disease and ulcerative colitis , strongly increase a person’s risk of developing colorectal cancer . The lifetime risk of developing colorectal in the United States is 5% , with the average age of diagnosis 70 years of age [14] . With an age of onset of IBD between 10 and 30 years of age , an affected person’s risk of developing colorectal cancer increases to 8% 20 years later and 18% 30 years later [15] . Thus , by affecting the entire colon , perhaps by inducing the formation of mutagenic reactive oxygen species , inflammation may cause field cancerization , strongly influenced by environmental exposure . Thus , on one hand , the investigation of signals expressed in tumors that are also expressed in adjacent normal tissues can identify pro-tumorigenic causes of field cancerization . On the other hand , such signals can provide evidence for regionally expressed protective functions . Here too , an animal model whose genome can be manipulated provides the power to study in vivo the consequences of a particular genetic loss-of-function . This study investigates by mutational analysis in the mouse whether PDE4B , the product of one of the triply conserved molecular transcriptome signals detected in ApcMin/+ adenomas and in the adjacent normal colonic epithelium , acts positively or negatively on adenomagenesis . PDE4B is specifically relevant to the issue of a sensitizing versus protective field effect in human colon cancer because it has been reported to be expressed in “non-neoplastic appearing colonic mucosa from patients with colorectal neoplasia” [16] . For the human , the emergent resources of molecular data for colorectal cancer in patients [17–19] enables an assessment of the mutational and expression status of the PDE4B gene in frank human colon cancer .
As described in Materials and Methods , RNA was isolated from colonic tumors and adjacent normal colonic epithelium of four ApcMin/+ mice and five ApcPirc/+ rats . RNA populations were then analyzed by hybridization to Agilent microarrays . Human data were analyzed from published data comparing normal and adenoma tissue from 32 patients with spontaneous , non-familial colorectal cancer . For comparison purposes , transcripts were linked among genera using orthology information . Using analysis-of-variance methods for paired tumor-normal data we identified transcripts whose levels differ substantially between the colonic adenoma and its corresponding normal epithelium ( Fig 1 ) . In the colonic adenoma of the ApcMin/+ mouse , 3054 gene transcripts were enhanced and 2041diminished by at least a factor of 2 , with a 5% false discovery rate . For the colonic adenoma of the ApcPirc/+ rat , 1460 distinct transcripts were enhanced and 416 diminished by the same criteria . The difference between the mouse and rat in the numbers of transcripts with differences in level may be owing either to the noise characteristics of the expression data or to the incomplete annotation of the rat genome ( rn5 Mar . 2012 RGSC Rnor_5 . 0 ) compared to the mouse genome ( mm10 Dec . 2011 Genome Reference Consortium GRCm38 ) . Though we did not pre-filter array data by removing low-expressing genes , the adenoma-associated transcripts showed substantially higher expression level than typical genes ( S1 and S2 Figs ) Finally , in the spontaneous human colonic adenoma 1604 annotated transcripts were enhanced and 3044 diminished ( hg18 Mar . 2006 NCBI Build 36 . 1 ) . This three-genus study provides a unique window into conserved oncogenic processes . As we illustrate in this initial study , these conserved aspects of human adenoma formation can subsequently be analyzed further with targeted functional genetic studies in mice and/or rat models . To assess gene-level conservation , we determined the pairwise overlap between genera of individual genes whose transcripts show differences in level ( Fig 1A ) . For example , on average over pairings of genera , the enhanced transcripts of one genus overlap with 19% of the enhanced transcripts of a second genus . For the transcripts that are diminished in adenomas , this mean overlap fraction is 13% . When considering the genome size for each of the three genera , this agreement in directionality of expression is significantly higher than would be expected in randomly generated lists of the same sizes ( by Fisher’s Exact Test; p < 10-10 in mouse by human and mouse by rat; but p = 0 . 06 in rat by human comparisons ) . More directly , Fig 2A reports on a permutation test , as described in Materials and Methods , of the mean-overlap statistic , confirming that the 3-way agreement , while low , is significantly higher than expected by chance ( p = 0 . 001 , for enhanced in adenoma; p = 0 . 003 , for diminished in adenoma ) . It is well known that sources of variation at the gene level can erode measures of agreement between genome-wide profiles of bona fide linked biological processes . To assess the conservation of adenoma-associated processes between mouse , rat , and human , we then determined the pairwise sharing between genera at the level of Gene Ontology ( GO ) categories , using the approach pioneered by Hao [20] . Thus , we identified genus-specific lists of GO categories enriched for adenoma-associated genes , and made pairwise comparisons of these gene-category lists . In contrast to the gene-level analysis , the mean pairwise overlap calculated directly from the GO analysis ( Fig 2B ) far exceeded that calculated at the gene-level: 43% for the enhanced and 33% for the diminished categories . Transcripts represented in the union list of adenoma-association in at least one genus populate extended sets of GO categories ( Figs 3 and 4 ) . Speaking broadly , Fig 3 indicates that transcripts associated with angiogenesis , nuclear and cell division , RNA processing , extracellular matrix , and inflammation are enhanced in the adenoma . By contrast , Fig 4 indicates that transcripts diminished in the adenoma are associated with processes of differentiation such as monocarboxylic acid metabolism , the organization of the actin cytoskeleton and the plasma membrane . We went on to focus on genes and their GO categories with differential levels in the adenoma shared across all three genera . The across-genus heat-map of Fig 5 summarizes the array data from the 89 genes that showed differences in level in a consistent direction in the colonic adenoma transcriptomes of all three genera– 75 enhanced and 14 diminished . Despite the genus differences , a clear across-genus adenoma-associated expression signature emerges . For the size of the 3-way intersection , the observed overlaps are significant at both the gene-level and the GO level . Here , the GO-level accentuates the conserved signal , focusing on shared fundamental biological processes in colonic adenomagenesis . We investigated whether the overlap statistics are sensitive to pre-filtering according to expression levels of the transcripts ( S3 Fig ) GO categories enriched in the three-way overlap are tabulated in S2 and S3 Tables , and summarized in Fig 6 . For the triply conserved enhanced transcripts , we infer functions commonly assigned to the tumor microenvironment , including leukocyte migration and fibroblast proliferation . By contrast , the triply conserved GO categories for the diminished transcripts include functions involved in cellular response such as transmembrane transporter activity . Finding a gene involved in colonic adenomagenesis in three distinct genera raises the hypothesis that the gene has an important function in this process . If so , by mutational analysis one can gain information relevant to function in vivo . Such molecular genetic tests of function by targeted mutagenesis are increasingly available in the mouse ( through the Knockout Mouse Project: www . komp . org ) and rat [21] . While it is not practical to analyze functionally all of the individual genes belonging to these significant triply shared GO categories , we have focused on Pde4b , one of the genes whose transcript is differentially enhanced in colonic adenomatous tissue of all three genera: 3 . 3-fold in the mouse , 2 . 7-fold in the rat , and 5 . 1-fold in the human . We have examined Pde4b in a mouse strain carrying its targeted knockout allele [22] . Phosphodiesterase 4b ( PDE4B , encoded in the mouse by Pde4b ) is a member of an eleven-member family of the cyclic nucleotide phosphodiesterases that enzymatically regulate the degradation of cAMP and cGMP . The Pde4 gene family , specific for cAMP , is composed of four genes ( A-D ) transcribed into a number of splice variants [23 , 24] . Little is known about PDE4B function in the intestine beyond basic expression profiles [23 , 25] . The enhancement of Pde4b transcripts in colonic adenomas is consistent with , but does not prove , the hypothesis that the PDE4B function plays a pro-tumorigenic role in the colon . We asked: Does loss of PDE4B function reduce adenomagenesis in the ApcMin/+ mouse ? To address this question , mice heterozygous for a global knockout allele of Pde4b [24] were crossed with ApcMin/+ animals . ApcMin/+ and Apc+/+ animals of Pde4b+/+ , Pde4b+/- , and Pde4b-/- genotypes were generated and the numbers of tumors in the small intestine and colon were scored . A significant effect of the Pde4b genotype was observed on the number of adenomas in the colon of the ApcMin/+ mouse ( Table 1 and S4 Fig ) . The average numbers ( ± standard deviation ) of colonic adenomas are 2 . 4 ± 2 . 2 for the Pde4b+/+ , 3 . 3 ± 2 . 9 for Pde4b -/+ , and 4 . 3 ± 4 . 6 for the Pde4b-/- genotype . By two-sided Wilcoxon rank sum test , the adenoma numbers in the heterozygote and homozygous mutant are significantly higher than in the wildtype ( p = 0 . 004 and p = 0 . 0002 , respectively ) , but do not differ significantly from each other ( p = 0 . 06 ) . Adenoma counts in the small intestine varied extensively for each Pde4b genotype; these differences were not significantly correlated with differences in the Pde4b genotype ( S1 Table ) . In summary , this mutational analysis indicates that the Pde4b genotype significantly affects adenomagenesis in the colon of ApcMin/+ mice , even in heterozygotes for the knockout allele . The observed mutational enhancement of colonic adenomagenesis by a loss-of-function mutant allele is not consistent with a simple pro-tumorigenic role of wildtype PDE4B function . Instead , these observations imply that the wildtype Pde4b allele encodes a protective function . Because the average colonic tumor number in ApcMin/+ mice is small , a random subset of ApcMin/+ mice are free of colonic tumors , at least in the distal half of the colon . As described in Materials and Methods , they can be identified by endoscopy prior to necropsy . Accordingly , we compared the level of the Pde4b transcript in the normal colonic epithelium from tumor-free colons with that from tumor-bearing colons . By array analysis , we observed that the Pde4b transcript level is enhanced in the normal colonic epithelium adjacent to colonic tumors in ApcMin/+ mice , compared to that of the normal colonic epithelium of tumor-free ApcMin/+ mice ( Fig 7 ) . The levels of the Pde4b transcript were quantified by real time PCR analysis , as described in Materials and Methods . Tumors express the Pde4b transcript at a level higher than in any other tissue examined ( Fig 8 , p = 0 . 00008 ) . Compared to its level in the normal colonic epithelium of tumor-free colons of ApcMin/+ mice , this transcript is found at a 3 . 3-fold higher level in the adenoma and at a 2 . 1-fold higher level in the normal colonic epithelium of tumor-bearing colons ( p = 0 . 048 ) . The Pde4b transcript level in tumor-free ApcMin/+ mice shows no significant difference from that in the normal colonic epithelium of mice wildtype for Apc ( p = 0 . 97 ) . The functional importance of the triply conserved PDE4B function may extend beyond adenomagenesis . Inflammation is known to increase tumor multiplicity and tumor stage in colorectal cancer [26 , 27] . Immune and inflammatory cells express high levels of PDE4B , thus reducing cAMP levels [28 , 29] . Inhibitors specific for cAMP-specific phosphodiesterases have been used to treat other inflammatory conditions including chronic obstructive pulmonary disease ( COPD ) and asthma . This effect demonstrates the importance of cAMP-specific phosphodiesterases in enhancing the inflammatory process , providing a potential therapeutic target [30] . Further , intestinal tumorigenesis has a known inflammatory component , perhaps involving PDE4B function [24 , 27 , 31] On these bases , inactivating the PDE4B function in a colorectal cancer model of inflammation would be expected to attenuate or eliminate the enhancement of colonic adenomagenesis by inflammation . To test this prediction , we generated ApcMin/+ and Apc+/+ animals bearing Pde4b+/+ , Pde4b+/- , and Pde4b-/- genotypes . Sets of mice with each Pde4b and Apc genotype were then divided into two groups , one treated with Dextran Sodium Sulfate ( DSS ) , a model of inflammatory bowel disease ( IBD ) , and the other left untreated . Colonic adenoma numbers and survival were then assessed . As shown in the left panel of Fig 9 , ApcMin/+ mice that are wildtype for Pde4b , have significantly elevated colonic tumor counts after treatment with DSS . This enhancement is more extreme when ApcMin/+ mice are also heterozygous or homozygous for the knockout allele of Pde4b ( Fig 9 , central and right panels , respectively ) . This observation is inconsistent with the hypothesis that PDE4B function enhances the inflammatory pathway to colonic neoplasia . Instead , the wildtype phosphodiesterase acts to protect against DSS-associated colonic adenomagenesis—directly or indirectly . The involvement of the ApcMin/+ mutation in the lethal phenotype implies that Pde4b is a modifier of Apc in this protective function . Beyond its effect on the number of DSS-induced colonic adenomas , we observed that the loss of PDE4B function has a severe effect on the survival of DSS-treated ApcMin/+ mutant mice . The imposition of the inflammatory pathway by treatment with 4% DSS , but not 2% DSS , compromises the survival of ApcMin/+ mice that are also mutated in the Pde4b gene ( Table 2 ) . This effect is specific to ApcMin/+ mice , and is most severe in homozygotes for mutated Pde4b . These effects are displayed graphically in the Kaplan-Meier plot of Fig 10 . Once again , these observations support the hypothesis that , in Apc-mutant conditions , wildtype levels of PDE4B function are protective .
Including Pde4b , we have found 89 triply conserved genes with differential levels of transcripts– 75 enhanced and 14 diminished in adenomas and have identified common functional categories associated with this conservation . Each of these differentially expressed , conserved genes can be analyzed as we have analyzed Pde4b . The differences in their transcript levels can result from either or both of two distinct causes: differences in transcript level per cell; and/or differences in the proportion of expressing cells in the adenoma versus the normal tissue [32] . Because the RNA samples were isolated from unfractionated tumor and epithelial tissues , changes in level can arise from the tumor lineage and/or the stromal microenvironment . Thus , we recognize the importance of assessing transcript levels in unfractionated adenoma tissue , to be able to identify cases of differential transcript level in either the tumor lineage or its stroma–“drivers” , “landscapers” [33] , or other classes of “modifiers” [34 , 35] . Conservation of transcriptome signals between evolutionarily divergent platforms may increase the likelihood of discovering functionally important signals . For example , the Pde4b gene has essential functions that protect against colonic adenomagenesis ( Table 1 ) and DSS-associated adenomagenesis ( Fig 9 ) and lethality ( Fig 10 and Table 2 ) . Our findings lead us to this hypothesis: molecular signals conserved at a particular neoplastic stage in multiple distinct animal models as well as in humans are more likely to identify fundamental functions that are involved in that specific stage in colonic neoplasia . Changes in the levels or activity of PDEs have been implicated in diverse roles in multiple diseases , including cancer [36 , 37] . cAMP is a critical mediator of multiple signaling events related to cell growth , differentiation , metabolism , and adhesion , among others . Its role in cancer is context dependent , as the cellular effect of a cAMP signal depends on the cell type and subcellular localization of the signal [36 , 38] . In the select cases in which cAMP has been shown to be oncogenic [39] , it may act directly on protein kinase A ( PKA ) or indirectly on its downstream effectors , cAMP-responsive element binding protein and Rap Guanine Nucleotide Exchange Factor 3 ( RAPGEF3 ) [40] . The quantitative analysis of Pde4b transcripts by realtime PCR ( Fig 8 ) showed levels in the ApcMin/+ adenoma enhanced by 3 . 3-fold over those in the normal colonic epithelium of wildtype and tumor-free ApcMin/+ mice . Contrary to the hypothesis that PDE4B function is pro-tumorigenic , we observed that ApcMin/+ animals carrying an inactivating mutation in Pde4b developed 1 . 4-fold more colonic adenomas in the mutant heterozygote and 1 . 8-fold more in the homozygote ( Table 1 ) . PDE4B functions directly or indirectly to inhibit colonic adenomagenesis in the ApcMin/+ mouse . This apparent paradox can be explained by a pro-tumorigenic effect mediated by cAMP , for example through activation of PKA as outlined above . In this scenario , loss of function of PDE4B would result in increased levels of cAMP , which in turn would activate PKA . Interestingly , PKA function has been shown to enhance the transcriptional activity of β-catenin by phosphorylating β-catenin on Ser675 [40 , 41] . Therefore , reduction of both APC and PDE4B functions would jointly activate β-catenin and increase adenomagenesis in the ApcMin/+ mouse . This hypothesized negative feedback circuit is diagrammed in Fig 11 . We have found that Pde4b transcript levels are not only enhanced 3 . 2-fold in colonic adenomas , but also 2 . 1-fold in the normal colonic epithelium adjacent to tumors ( Fig 8 and S5 Fig ) . Consistent with these observations , Mahmood and colleagues have observed by immunohistochemistry that PDE4B protein levels are enhanced in “non-neoplastic appearing colonic mucosa from patients with colonic neoplasia” [16] . These authors suggested that PDE4B is overexpressed as a malfunctioning protein in normal tissue . Instead , as suggested above , wildtype PDE4B functions as a negative regulator of colonic adenomagenesis . Its regional action formally fits one feature of the model of Meinhardt [42] that posits long-range negative coupled with short-range positive action in pattern formation . One is struck by the formal analogy with the long-range negative action of the metabolically stable angiostatin on tumor angiogenesis [43] . The compromised survival of DSS-treated ApcMin/+ mice carrying a knockout mutation in Pde4b , either as heterozygotes or more severely , as homozygous mutants ( Fig 10 and Table 2 ) is striking . This effect on survival also requires the presence of the heterozygous Min nonsense mutation in the Apc gene , an example of “synthetic lethality” [44] . What model might explain this DSS-induced lethality ? We reason that a function affecting survival at an early adult stage does not involve stochastic tumorigenesis , but instead is constitutional . The Apc gene is known to be expressed in most mammalian tissues . The interaction of DSS treatment with the ApcMin/+ heterozygous and Pde4b mutant conditions would rely on normal APC and PDE4B functions in survival of DSS treatment . Because the Apc gene is expressed broadly , any explanation of this DSS-dependent lethality would require comprehensive , timed necropsies , an analysis beyond this study . The test of the function of PED4B in adenomagenesis showed strong enhancement in both the heterozygous and homozygous mutant Pde4b genotypes ( Table 1 ) , consistent with a protective role for PDE4B function . Under this interpretation , it seems paradoxical that the heterozygote , carrying one copy of the protective allele , is not significantly resistant . The sensitivity of the Pde4b+/- heterozygote implies that PDE4B must function at a level higher than the 50% level expected for the heterozygote–“haploinsufficiency . ” Indeed , the Apc gene also demonstrates haploinsufficiency for intestinal adenomagenesis [45] and other processes in mice [46–48] . To incorporate haploinsufficiency , the canonical Tumor Suppressor Model has been amended to a “one-hit model” by Berger , Knudson and Pandolfi [49] . We have deduced that PDE4B function inhibits adenomagenesis , directly or indirectly . It then seems paradoxical that the levels of the Pde4b transcript are enhanced in colonic adenomas [50] . A possible resolution to this paradox comes from the observation that Pde4b is a target gene of β-catenin [51] . Thus , it is plausible that the increase of Pde4b transcript in the ApcMin/+ adenomas and surrounding mucosa is secondary to active WNT signaling in the setting of loss of APC . Then , through the destruction of cyclic AMP , PDE4B would act as a negative regulator of PKA-dependent colonic adenomagenesis ( Fig 11 ) . We note that the normal , non-mutated form of another negative regulator , TP53 , is also enhanced in early tumors [52 , 53] . In frank cancers , then , the p53 gene is frequently mutated . These observations gave support to the classical tumor suppressor model . At what stage in tumorigenesis does the negative regulation by the normal TP53 function act ? For intestinal neoplasia in the ApcMin/+ mouse , it has been reported that p53-deficiency enhances adenomagenesis only 1 . 5-fold , leading to a small proportion of more advanced adenomas [54] . Thus , the protective function of TP53 may come into play at a stage later than that invoked in these studies of the PDE4B function . Does the overexpression in early tumors of the non-mutated normal forms of TP53 and PDE4B reflect a feedback response by normal tissues to uncontrolled growth–“oncogenic stress” [55 , 56] ? Two reports by Tomlinson and colleagues are relevant–TP53 protein levels are elevated only late in the progression of colon cancer [57] and the mutations in p53 that are found in the adenomas of familial adenomatous polyposis patients are a special subset of those found in frank colon cancer [58] . Will frank human cancers show evidence for mutations in the PDE4B gene ? In interrogation of the Dana Farber Cancer Institute ( DFCI ) dataset , PDE4B mutations are reported in only 9 of 619 colorectal cancers ( 1 . 5% ) [17 , 18 , 59] . Of these mutant alleles , only one creates a known truncation , and none has been classified as a putative “driver” [cf . 60] . Many of these mutant alleles of PDE4B occur in colonic cancers that also carry truncations of the canonical gatekeeper gene APC [59] . A significant minority also carry putative driver mutations in TP53 [59] . Interestingly , 40% of those cancers with mutant PDE4B also possess mutations in the BRAF gene , in contrast to an incidence of only 8–10% BRAF-mutant carriers in the entire colorectal cancer population . Although rarely mutated in human colorectal cancers , the level of PDE4B protein is reduced in frank colon cancer—possibly through an epigenetic mechanism . Specifically , the Human Protein Atlas reports that 8 out of 12 human colorectal cancers demonstrate reduced intensity of immunostaining for PDE4B antigen in the epithelial component of these cancers , compared to samples from the normal colon and rectum [19] . These mutational and silencing observations in patients are consistent with PDE4B serving a protective function in the major APC-dependent pathway to frank colon cancer . In contrast to TP53 , however , the function of PDE4B is commonly lost in colorectal cancer not by inactivating mutations but by epigenetic silencing . Consistent with this hypothesis , Bottomly and colleagues have reported that β-catenin strongly binds to the promoter region of the histone deacetylase HDAC4 , a component of repressive chromatin [51] . An emergent challenge is to discern whether the inferred silencing process is stochastic or instead is developmentally programmed within the somatic lineage that leads to frank colorectal cancer [61] . A silencing process can be monoallelic or biallelic [61] . The observation that PDE4B function is haploinsufficient indicates that , though silencing may only be partial , it can be effective for PDE4B . Fig 11 summarizes the interactions suggested in this Discussion . Here , the proposed negative regulatory role of HDAC4 would invoke several intriguing unknowns for further investigation: the developmental pathway or stochastic process that would uncover the silencing event and the specificity factors that would direct the repressive HDAC4 to the Pde4b gene The observation that Pde4b affects the colonic adenoma phenotype of ApcMin/+ animals leads to its formal designation as a Modifier of Min ( Mom ) gene–one whose mutant phenotype depends also on the ApcMin/+ genotype . Most studies that define Mom loci involve measurements of tumor numbers in both the small intestine and colon . This study and that of the gender effect [3] show that these two regions of the intestinal tract differ in their response to genetic modifiers . How the understanding of the complexities of colon cancer being unraveled by mutational [62] and phenotypic [63] analysis can be conceptually simplified into metaphors including driver , landscaper and growth rate regulator [33 , 64] is unclear . It seems possible that an indefinitely large number of functions can impact colonic neoplasia; such an “Infinite Tree” can be explored by the expansion of modifier genetics [62] and conservation of molecular signals across multiple genera . “Personalized Medicine” would benefit from such an expansion of functional targets . In summary , we have combined mutational analysis in the ApcMin/+ mouse with published studies of frank colon cancer in patients to deduce that Pde4b has two strong biological functions . It negatively regulates colonic adenomagenesis in ApcMin/+ mice . In patients , PDE4B is most commonly inactivated by an epigenetic process . PDE4B protects against the lethality caused by treatment of young ApcMin/+ mice with the inflammatory agent DSS . Finding evidence for important functions of PDE4B in tumorigenesis and survival supports applying the strategy of combining molecular analyses of human tumors with molecular and functional analyses of a pair of animal models from distinct genera to discover conserved functions that are important to understand and manage human cancer .
Mice and rats were maintained under a protocol ( M02049-0-11-11 and M00268-0-07-13 ) approved by the Animal Care and Use Committee of the University of Wisconsin School of Medicine and Public Health , in a facility in the McArdle Laboratory approved by the American Association of Laboratory Animal Care . All experiments were carried out in accordance to the Guide for the Care and Use of Laboratory Animals from the National Research Council of the National Academies . Animals were housed in standard caging with free access to food ( 5020 chow , Purina , St . Louis , MO ) and acidified water . A 12:12 hour light:dark cycle was maintained throughout the experiments . C57BL/6J ApcMin/+ mice ( developed in the laboratory of WFD and commercially available through the Jackson Laboratory , Bar Harbor , ME ) were maintained as a closed colony , C57BL/6JD [62] by breeding Apc+/+ females to ApcMin/+ males and monitoring the canonical high tumor number [1] ( University of Missouri Stock 043849-MU ) . F1 generation ( C57BL/6JD x BTBR ) F1-Min mice were generated by breeding female C57BL/6JD ApcMin/+ to male BTBR mice . F1 generation ( ACIxF344 ) -Pirc rats were generated by breeding female ACI Apc+/+ rats ( Harlan , Indianapolis , IN ) to male F344/Tac coisogenic ApcPirc/+ rats ( developed in the laboratory of WFD and commercially available through Taconic , Hudson , NY ) . The Min and Pirc alleles were genotyped as previously described [65 , 66] . C57BL/6 Pde4b-/- mice were generously contributed by the laboratory of Marco Conti ( University of California San Francisco ) . Pde4B knockout mice were generated as described [67] . F1 mice were generated by breeding C57BL/6JD ApcMin/+ Pde4b+/+ females to C57BL/6 Apc+/+ Pde4B-/- males to generate C57BL/6JD ApcMin/+ Pde4b+/- and C57BL/6 Apc+/+ Pde4b+/- mice . F2 mice were generated by intercrossing F1 C57BL/6JD ApcMin/+ Pde4b+/- and C57BL/6 Apc+/+ Pde4b+/- mice . N2-N5 mice were generated by backcrossing C57BL/6 Apc+/+ Pde4b+/- mice with C57BL/6JD ApcMin/+ Pde4b+/+ mice . Comparisons were made between mice of contrasting genotype from the same intercross generation . At 35 days of age , sets of male and female F2 C57BL/6 ApcMin/+ animals of Pde4b+/+ , Pde4b+/- , and Pde4b-/- genotypes were divided into litter-matched or age-matched groups , with four animals in each cage . Dextran sodium sulfate ( 500kDa ) was purchased from Fisher Scientific ( Pittsburgh , PA ) and mixed with standard acidified drinking water to 2% or 4% ( wt/vol ) . The DSS-supplemented drinking water was administered to the treatment group between 35 and 39 days of age for four days , followed by a 17-day recovery period , after which they were given DSS for another four days between 56 and 60 days of age . Mice were sacrificed at 100 days of age or when moribund . Beginning at 35–40 days of age , endoscopy was performed weekly until sacrifice , except during DSS treatment . This experimental approach allowed the selection of the subsets of tumor-positive and tumor-negative ApcMin/+ mice . For endoscopy , animals were anesthetized with 3% isoflurane and placed on a sterile surgical field , ventral side down . The colon was flushed with 1% saline to remove fecal material and provide lubrication . A Hopkins Optik 0° 10cm endoscope ( 1232AA , Karl Storz , Tuttlingen , Germany ) contained within a sheath ( 61029D , Karl Storz ) was inserted into the colon , allowing the distal half of the colon to be visualized . Still and video images were captured at each visit using a Xenon Nova 175 light source ( 20131520 , Karl Storz ) with an Image 1 hub ( 2220020 , Karl Storz ) and viewed using AidaVet software ( 69204020 , Karl Storz ) . The final endoscopic visit was completed at least 24 hours prior to sacrifice . At sacrifice , the small intestine ( divided into four equal sections ) and colon were removed , opened longitudinally , laid flat , and washed with PBS . The four sections of small intestine and the colon were then fixed with 10% formalin for 48 hours and transferred to 70% ethanol for long-term storage . Following fixation , each section of intestine was viewed under 10X magnification on a dissecting microscope . Tumor counts were obtained for each of the four sections of small intestine and for the entire colon . All analyses for differences in tumor multiplicities were done using Mstat software ( https://mcardle . wisc . edu/mstat/index . html ) . Since the tumor distribution is known to be non-normal , non-parametric tests were used . A Kruskal-Wallis test was used for differences among tumor multiplicities over more than two sample groups . A two-sided Wilcoxon rank sum test was used to test for differences between two sample groups . For each test , a p-value ≤ 0 . 05 was considered significant . Both the mouse and rat microarray studies utilized only male animals to eliminate potential hormone variation of the estrus cycle in female animals . A 12:12 hour light:dark cycle was maintained throughout the experiments and all tumors were harvested within a four-hour window in the afternoon to minimize any circadian cycle variation in transcript level . Only untreated mice and rats were included in this study; no chemical or biological mutagens were used . Thus the ApcMin/+ and ApcPirc/+ mutations , respectively , were the sole controlled causes of adenoma development . Untreated ApcMin/+ mice at 80 days of age and untreated ApcPirc/+ rats at 97 days of age were sacrificed for RNA collection from adenoma and normal colonic tissue . Tumor samples were collected immediately upon dissection as follows: a cut was made part way down the middle of the tumor perpendicular to the surface of the colon . Then a second cut was made parallel to the surface of the colon , resulting in a segment representing approximately one quarter of the adenoma . Normal colonic epithelial tissue was collected by scraping the colonic lumen with a scalpel blade , at least 3mm from any visible tumor . Each tissue sample was homogenized in a tube containing RLTplus buffer ( Qiagen , Hilden , Germany ) and frozen at -80°C . RNA was isolated from each sample using the Allprep DNA/RNA Mini Kit ( Qiagen ) , following the manufacturer’s protocol . DNA contamination of RNA samples was removed by on-the-column DNase treatment following the manufacturer’s protocol . RNA quality was determined using an Agilent 2100 BioAnalyzer ( Agilent Technologies , Inc . Santa Clara , CA , USA ) . Microarray experiments follow the nomenclature , descriptions , and data sharing protocols recommended by the MIAME Guidelines [68] . For each normal colonic epithelium and adenoma sample , total RNA ( 100 ng ) was labeled with Cy3 dye using a Low Input Quick Amp kit ( Agilent Technologies ) according to the manufacturer’s instructions . Mouse samples were hybridized to Agilent 8x60K Whole Genome microarrays . Rat samples were hybridized to Agilent 4x44K Whole Genome microarrays . Following incubation , arrays were scanned on an Agilent High-resolution Microarray Scanner at 3μm resolution with a 20-bit data format . Files were extracted using Agilent Feature Extraction version 10 . 7 . Human data were analyzed from the published GEO dataset GDS2947 comparing normal and adenoma tissue from 32 patients with spontaneous , non-familial colorectal cancer . Mouse data were acquired as documented in GEO Series accession number GSE107139: paired normal and tumor tissue was harvested from each of 4 Min mice . Rat data were acquired as described in GEO Series accession number GSE54036: paired normal and tumor tissue was harvested from each of 5 rats plus an additional normal sample from 2 of those rats . Mouse and rat data from Agilent extraction files and human data from Affymetrix extraction files were analyzed using Genome Suite software ( Partek ) . Samples from each genus were analyzed separately , using the same mode of analysis for each genus , to generate three lists of transcripts with differential levels . 2-Way ANOVA was used to assess differential levels , with animal ID and sample type ( adenoma or normal colonic epithelium ) as variables . Animal ID was included as a variable to permit a paired analysis of tumor and normal samples . Gene lists were generated from transcript levels that differed by at least a factor of 2 ( up or down ) between normal epithelium and adenoma , with a false discovery rate of equal to or less than 0 . 05 . For comparison of differences of transcript levels of tumors and the normal colonic epithelium of tumor-bearing and tumor-free animals we used the LNNMV model in Bioconductor package EBarrays [69] . Orthology information relating mouse , human , and rat genes was obtained from the Mouse Genome Informatics Web ( http://www . informatics . jax . org ) , retrieved April 2013 [70] . Within-genus differential expression results were aligned according to Entrez gene ID using mouse-rat and mouse-human orthology . To allow an equivalent comparison involving all genera , Entrez IDs orthology selected only the probes that were represented in all three genera . Genus-aligned data were compared pairwise and over three genera by tabulating genes that were consistently up or down ( adenoma versus normal epithelium ) among genera . Pairwise overlap was measured with the mean-overlap fraction , which is the average proportion of one gene list that is contained in another , averaged over the different source lists [20] . Fisher’s exact test was also used as a baseline approach to assess whether the overlap between two lists was higher than expected from randomized gene lists of the same sizes . We also assessed 3-way overlap using permutations that shuffled gene-list content independently in each genus . Gene-set analysis was used for multiple purposes: ( 1 ) to identify functional categories that were enriched in genus-specific gene lists; ( 2 ) to identify functional categories enriched in all-genera gene lists; and ( 3 ) to describe the among-genera agreement at the functional level . In all cases Gene Ontology ( GO ) classes mapping to the linking mouse Entrez-gene IDs were used from Bioconductor package org . Mm . eg . db ( June 2015 ) . Enrichment computations used both allez [71] and model-based multi-set computations [72] . Multi-genera agreement [73] was computed in a gene-permutation analysis using the technique described by Hao et al . [20] . Briefly , enriched GO terms ( via model-based computation ) were identified in each species , and the GO-term lists were compared for their mean-overlap fractions . For a comparison , the mean overlap fraction was also recorded on gene lists . These statistics were calibrated by gene-shuffling in which gene-set content was shuffled but the gene-list sizes and gene-level agreements were fixed among genera . An increase in overlap fraction among genera at the functional GO level , compared to the gene level , would signify pathway or functional agreement among genera in the level of transcripts associated with adenomagenesis . This agreement is masked by gene-level noise . Dominant functional categories ( Figs 3 , 4 and 6 , ) were derived from enriched categories and plotted using the same technique as in Hao et al . [20] and Barger et al . [74] Transcriptome candidates were verified by real time PCR using experiments following the nomenclature and description recommended by the MIQE Guidelines [75] . cDNA was generated from isolated RNA using the Superscript III Reverse Transcriptase Kit ( Thermo Fischer Scientific , Waltham , MA ) . The hydrolysis probe labeled with FAM dye for Pde4b was purchased from Applied Biosystems . A GAPDH probe labeled with VIC dye ( Applied Biosystems , Foster City , CA ) was used as a reference gene . Normal colonic epithelial samples of both tumor-bearing and tumor-free ApcMin/+ animals were analyzed for all array samples . The normal colonic epithelium of Apc+/+ and tumor samples from ApcMin/+ were included for additional quantitative comparisons of transcript levels . Additional RNA was obtained using the protocol described above ( Qiagen ) . Each sample was run in triplicate and technical error between replicates did not exceed 5% . Fold-change expression was determined by calculating 2n for each sample , where n equals the difference in amplification cycle between the GAPDH reference and the test probe . The parameter deltaCT gives the number rounds of amplification needed to reach threshold compared to the number required for the control GAPDH probe . | We have used the extensive genetic variation between genera within the mammalian order–mouse versus rat versus human–to discover genes whose broadly conserved transcripts differ in level in early colonic tumors compared to the normal colonic epithelium . Then , we developed a quantitative functional analysis using a targeted inactivating mutation in a genetically homogeneous strain , the congenic C57BL/6J ApcMin/+ mouse . This physiological genetic analysis of cancer involves parsing gene action into positive versus negative effects on the cancer of interest . Combining our studies with reports in the literature , we deduced that , by catabolizing cyclic AMP , the phosphodiesterase encoded by the gene of interest , Pde4b , protects against the early stages of colon cancer in the mouse . In advanced colon cancer in human patients , this gene is silenced , losing its inferred protective role . Together , this study illustrates the power of combining discovery by conservation among diverse mammalian genera with functional analysis within a single experimental species to understand a novel facet of human cancer . | [
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] | 2018 | The conserved protective cyclic AMP-phosphodiesterase function PDE4B is expressed in the adenoma and adjacent normal colonic epithelium of mammals and silenced in colorectal cancer |
The current outbreak of Zika virus poses a severe threat to human health . While the range of the virus has been cataloged growing slowly over the last 50 years , the recent explosive expansion in the Americas indicates that the full potential distribution of Zika remains uncertain . Moreover , many studies rely on its similarity to dengue fever , a phylogenetically closely related disease of unknown ecological comparability . Here we compile a comprehensive spatially-explicit occurrence dataset from Zika viral surveillance and serological surveys based in its native range , and construct ecological niche models to test basic hypotheses about its spread and potential establishment . The hypothesis that the outbreak of cases in Mexico and North America are anomalous and outside the native ecological niche of the disease , and may be linked to either genetic shifts between strains , or El Nino or similar climatic events , remains plausible at this time . Comparison of the Zika niche against the known distribution of dengue fever suggests that Zika is more constrained by the seasonality of precipitation and diurnal temperature fluctuations , likely confining autochthonous non-sexual transmission to the tropics without significant evolutionary change . Projecting the range of the diseases in conjunction with three major vector species ( Aedes africanus , Ae . aegypti , and Ae . albopictus ) that transmit the pathogens , under climate change , suggests that Zika has potential for northward expansion; but , based on current knowledge , our models indicate Zika is unlikely to fill the full range its vectors occupy , and public fear of a vector-borne Zika epidemic in the mainland United States is potentially informed by biased or limited scientific knowledge . With recent sexual transmission of the virus globally , we caution that our results only apply to the vector-borne transmission route of the pathogen , and while the threat of a mosquito-carried Zika pandemic may be overstated in the media , other transmission modes of the virus may emerge and facilitate naturalization worldwide .
Following a twenty-fold upsurge in microcephalic newborns in Brazil linked to Zika virus ( ZIKV ) , the World Health Organization has declared an international health emergency . [1] Despite being profiled for the first time in 1947 . [2] Zika remained poorly characterized at a global scale until the last six months . Thus , the present pandemic expansion in the Americas poses a threat of currently unknown magnitude . Closely related to dengue fever , Zika conventionally presents as a mild infection , with 80% of cases estimated to be asymptomatic . [3] The cryptic nature of infection has resulted in sporadic documentation of the disease and rarely includes spatially explicit information beyond the regional scale . [1 , 4–6] This greatly limits the confidence with which statistical inferences can be made about the expansion of the virus . With an estimated 440 , 000–1 , 300 , 000 cases in Brazil in 2015 , [3] and continuing emergence of new cases in Central America and , most recently , the United States , assessing the full pandemic potential of the virus is an urgent task with major ramifications for global health policy . Current evidence portrays the global spread of ZIKV as a basic diffusion process facilitated by human and mosquito movement , a hypothesis supported by the frequency of infected traveler case studies in the Zika literature . [7–10] Tracing phylogenetic and epidemiological data has revealed the expansion of ZIKV has occurred in a stepwise process through the South Pacific , moving the disease from Southeast Asia into French Polynesia and the Philippines , and subsequently to Easter Island . [1 , 4–6] Based on phylogenetic reconstruction , ZIKV is assumed to have dispersed into South America as recently as three years ago from the last of those locations , [11] and the virus is presumed to be at a biogeographic disequilibrium in the Americas . With cases in the ongoing outbreak in Colombia , El Salvador , Guatemala , Paraguay , and Venezuela , and by November of last year , as far north as Mexico and Puerto Rico , the full potential distribution of the disease remains unknown . Moreover , several alternative explanations for the disease’s expansion remain overlooked; most notably , the role of climate change in Zika’s expansion has not yet been thoroughly investigated . [12] We present three competing hypotheses that describe the path of expansion that Zika could take , based on evaluations of the ecological niche of the virus within and outside of its vectors . First , if Zika has no additional climatic constraints relative to those of its vectors , future range expansions should match mosquito ranges . Second , if Zika has a transmission niche that is constrained by climatic factors within the ranges of its mosquito vectors , its range may be much more limited—with , as we show below , possible confinement to the tropics—and cases in North America could be driven by human dispersal or extreme episodic weather events . Finally , it is possible that the expansion of Zika into North America may be a steady range expansion beyond the known niche in its native range , facilitated by climatic shifts or by genetic shifts in the virus or vectors . To test these hypotheses , we present a spatially explicit database of Zika occurrences from the literature and an ensemble of ecological niche models [13] using that data to map the potential distribution of the virus .
Occurrence data for Zika virus was compiled from the literature from studies dating as far back as the original discovery of the virus in Zika Forest , Uganda in 1947 . While the asymptomatic nature of the virus limits the total availability of data , lack of evidence for spatial patterns in symptoms in the native range suggest this is an unlikely cause of spatial bias ( and instead , merely limits total dataset size ) . Special attention was paid to correctly attributing cases of travelers to the true source of infection . Locality data was extracted from a combination of clinical cases and seropositivity surveys in humans and mosquitoes , and georeferenced using a combination of Google Maps for hospitals and the Tulane University GEOLocate web platform for the remainder , [14] which allows for the attribution of an uncertainty radius to points only identified to a regional level . Four points were georeferenced in the New World but excluded from niche models because a limited sample as small as four points was likely to significantly bias predictions ( compared to the necessary number of pseudoabsences in the same region ) . Thus , sixty points from the Old World were used in the final models presented in our paper after eliminating data from the current outbreak in the Americas . All points included in our dataset had an outer-bound of at most 65 km of uncertainty , with most substantially less . Constraining datasets based on an uncertainty threshold will become more statistically feasible in future studies once more survey data become available . In the present study , we deemed that the additional information gained from each point outweighed the potential impact of the uncertainty on model performance ( S1 Table ) . We note that for similar reasons , we did not subsample our dataset for spatial thinning in our main models , as software packages like spThin allow , [15] due to information-accuracy tradeoffs; and the strong final performance of models ( and the correspondence of our predictions for dengue and Aedes species to published “gold standard” niche models ) speaks to the appropriateness of the underlying data and variables . Sensitivity analyses in the literature unequivocally suggest that accuracy of the modeling methods we employ plateaus at or near 50 points , justifying the use of a dataset of this size . [16–18] Occurrence data for the other species included in our study were compiled from the literature . For Aedes africanus , we used a dataset of 99 points downloaded from the Global Biodiversity Informatics Facility ( www . gbif . org ) . GBIF’s coverage of Aedes aegypti and Aedes albopictus was deemed to be lacking , so occurrences for those species were taken from the previously published work of Kraemer et al . [19–20] Finally , Messina et al . ’s database was used for dengue , [21] as it has been previously published and used with great success to generate a global distribution model . [22] Both of these datasets were reduced down to point-only data ( i . e . , polygons of occurrence were excluded ) , leaving 5 , 216 points for dengue and 13 , 992 and 17 , 280 points for Ae . aegypti and Ae . albopictus respectively . A number of other Zika vectors are known from previous reports , including at least a dozen Aedes species , as well as Anopheles coustani , Culex perfuscus , and Mansonia uniformis . [23–24] While we do not include these vectors in this study in order to keep focus on the most likely globally-cosmopolitan Aedes vectors , we note these species could be important in regional patterns of establishment . These species lack the globally comprehensive datasets that dominant arbovirus-vectoring Aedes species have , and require future attention by similarly-dedicated researchers . Due to the potentially transient nature of the New World distribution of Zika virus , our model uses presence and 1000 randomly selected pseudo-absence points from the Eurasian , African , and Australian regions where the virus is established . We used the WorldClim data set BIOCLIM at 2 . 5 arcminute resolution , an aggregated dataset across values from 1950 to 2000 , to provide all but one of our climate variables . [25] The BIOCLIM features 19 variables ( BIO1-BIO19 ) that summarize trends and extremes in temperature and precipitation at a global scale . Given the relevance of the normalized difference vegetation index ( NDVI ) in previous studies of dengue and as a predictor of vector mosquito distributions , [26] we downloaded monthly average NDVI layers for each month in 2014 from the NASA Earth Observations TERRA/MODIS data portal , [27] at a resolution of 0 . 25 degrees to maintain compatibility with the BIOCLIM layers ( 0 . 25 degrees is equivalent to 15 arcminutes ) . The twelve monthly layers were averaged to provide a single mean NDVI layer . Due to the absence of NDVI data at the necessary resolution associated with many of the historical records ( especially prior to 1992 ) , the use of a recent mean NDVI layer was deemed the most pragmatic method of including vegetation in our models . We also make the simplifying assumption that areas of prior presence correspond to areas of current presence , an assumption that allows the use of current NDVI and is relatively standard for the niche modeling literature . Species distribution models were executed using the BIOMOD2 package in R 3 . 1 . 1 , which produces ensemble species distribution models using ten different methods: general linear models ( GLM ) , general boosted models or boosted regression trees ( GBM ) , general additive models ( GAM ) , classification tree analysis ( CTA ) , artificial neural networks ( ANN ) , surface range envelope ( SRE ) , flexible discriminant analysis ( FDA ) , multiple adaptive regression splines ( MARS ) , random forests ( RF ) , and maximum entropy ( MAXENT ) . [28] The BIOMOD algorithm runs a series of distribution models using training data , each of which is subsequently weighted and stacked across methods based on relative predictive performance with test data . As Thuiller et al . note , if a single modeling method is consistently most accurate , use of that method should be favored over ensemble approaches , [28] but in our study model performance varied , making ensemble approaches informed by degree-of-belief in a given model the most powerful option available . With recent publication of two Zika niche modeling papers using MAXENT and boosted regression trees , respectively , [29–30] differences between these two modeling methods may be responsible for differences in predictions–an issue that makes ensemble models particularly robust to idiosyncrasies of any individual methods . Models were run individually for Zika ( ZIKV ) , dengue ( DENV ) , Ae . aegypti , Ae . albopictus , and Ae . africanus . For Zika , models trained on Old World environmental data ( from Europe , Africa , Asia and Australia ) were used to establish the potential distribution of the virus in the Americas under climatic conditions captured by WorldClim data , which are an aggregate of data between 1950 and 2000 ( appropriately matching the date range of historical Zika occurrence data ) , and represent an expected range of variability that does not incorporate anomalous events like 2015 El Niño Southern Oscillation . Extrapolation between continents is a procedure with the potential for error: if novel environments exist in the New World with incomparable covariance structure between climate variables , predictive accuracy is likely to decline . While using only Old World data could potentially bias our models towards a subset of the niche , this can be readily tested for , by comparing models that include or exclude South American occurrence data . To address colinearity in the environmental variable set , we produced a correlation matrix for our 20 variables , and identified each pair with a correlation coefficient > 0 . 8 . For each species , we ran a single ensemble model with all ten methods and averaged the variable importance for our 20 predictors across the methods ( S2–S6 Tables ) . In each pair we identified the variable with the greater contribution , and we produced species-specific reduced variable sets used in the final published models by eliminating any covariates that universally performed more poorly than their pair-mate . Based on this criterion , we excluded the following variables for each species to reduce colinearity: The AUC of every model run with reduced variable sets is presented in S7 Table . We found no significant correlation between NDVI and any individual BIOCLIM variable , so NDVI was included in every model of current distributions . We ran five iterations of each reduced variable set model and eliminated any prediction methods from the ensemble with an AUC of lower than 0 . 95 , so that the final model had only included the best predicting models . This greatly limited the models available for ZIKV and DENV , so a cutoff of 0 . 9 was applied in those cases , to keep the ensemble approach constant across datasets . The final models were run with the following methods with ten iterations using an 80/20 training-test split in the final presentation: The importance of variables of the reduced model set for each are presented in S8–S13 Tables , and the final ensemble models are projected from the BIOMOD output in S1–S5 Figs . To assess the transferability of our Zika model across environmental space , we conducted a geographic cross validation ( GCV ) between African and Asian datasets ( an analysis we did not repeat for Aedes species or dengue , given the far greater sample size and geographic coverage of those species , and the publication of more intensive niche modeling efforts by experts for those systems ) . While under normal circumstances , a model would be trained on New World data and projected onto the Old World to cross-validate results , the lack of data prior to the current outbreak makes such a direct comparison infeasible . However , given the evidence for separate Asian and African strains , a cross-validation between the two was supported , and models trained on those two continents were projected globally to test the performance of the model across geographic regions , and evaluate how sensitive our projections in the Americas are to the environmental covariates sampled . The clustering of points in western India narrows the environmental range sampled by presences , potentially limiting the apparent transferability of the Asian sub-model . In contrast , the African sub-model performs well in new regions , and corresponds well to the global model . The potential contribution of climate change to Zika’s current expansion , and the outer bounds of transmission under future expansion , are largely unaddressed . While these have not been the subject of any concerted speculation , Shapshak et al . [31] point out that the majority of arboviruses are potentially implicated in the climate change-driven expansion of global disease burden , with a shared set of drivers that quite probably extends to Zika as well . Consequently this analysis serves two purposes; to address the potential expansion and thereby assist public health planning , and to test whether even a liberal post-climate-change interpretation of range margins matches the predictions of Messina et al . [29] and Samy et al . [30] that we consider limited in specificity and potentially over-predictive . To project the distribution of the species under a worst-case scenario for climate change , we reran each model with the previously chosen method and variable sets but excluded NDVI , as future values could not be simulated effectively . BIOCLIM forecasts were taken from WorldClim using the Hadley Centre Global Environmental Model v . 2 Earth System climate forecast ( HadGEM2-ES ) predictions for representative climate pathway 8 . 5 ( RCP85 ) , which , within that model , represents a worst-case scenario for carbon emissions and climate warming . [32] All five species’ models were retrained on current climate data and projected onto forecasts for the year 2050 . While we could have also included milder climate change forecasts and scenarios in our analysis , public concern over the future spread of Zika make the worst case scenario the most relevant question of interest for public health research ( and intermediate scenarios would fall between current ranges and the worst case scenario we project ) . To compare the niche of dengue and Zika and thereby address whether dengue models can be appropriately used to forecast the Zika pandemic , we used the R package ecospat , which uses principal component analysis to define the position of species’ ecological niche relative to background environmental variation . [33–34] The ecospat analysis was run using the full 64 point database and the full extent of global environmental data , because , while the niche of Zika in the Americas is uncertain , dengue is well established , and the analysis was most appropriately done with global coverage . Niche similarity tests were run with 500 iterations and using the entire set of 20 environmental variables ( BIOCLIM + NDVI ) . Our study is centered on the assumption that incorrect predictions at the country level can have drastic consequences for the misinterpretation of science . As a final precautionary analysis , we supplemented the data published in the Messina et al . study [30] to our own for a final re-analysis . Broennimann & Guisan [35] recommend the pooling of data from native and invasive ranges for ecological niche modeling during the course of a biological invasion , an approach we adopt in this final analysis . The Messina data is heavily clustered in Brazil , with a high degree of aggregation , and especially compared against our less-aggregated , smaller dataset this made the combination of datasets potentially inaccurate . To address this problem , the 390 pooled points were reduced down to 242 points using the package spThin , [15] with a 40km buffer between points ( the width of an average grid cell for our environmental data ) . Models were rerun using the same variable and model set as for the primary Zika model and the results of the analysis are included in the supplementary information as S6 Fig and , with a threshold applied based on the true skill statistic , S7 Fig . The final model performs poorer than our main ensemble ( weighted model: AUC = 0 . 970 ) , and while it more appropriately predicts presences in southern Brazil , it does a far poorer job in the rest of the world , once again most likely due to the relative balance of points even after thinning the dataset .
Our final Zika model combines seven methods with a variable set chosen from bioclimatic variables and a vegetation index to minimize predictor covariance . The ensemble model performs very well ( AUC = 0 . 993; Fig 1 ) , to a degree that resembles overfitting but is in fact driven by the strength of the ensemble modeling approach ( which preferentially weights the best models across iterations , minimizing the error associated with any given high-performing iteration ) . The model strongly matches most occurrences including the hotspots of Brazilian microcephaly . It also predicts additional regions where Zika is so far unrecorded , but where further inquiry may be desired ( in particular , Southern Sudan and the northern coast of Australia ) . Our model indicates that certain occurrences , like the 1954 report from Egypt and almost all North American cases , are likely outside the stable transmission niche ( i . e . , persistent over time ) of the virus ( sensu [36] ) . Moreover , we note that visual presentation of cases–or , of ecological niche models–at the country level may make the range of the virus appear far larger than our models suggest ( see Fig 1 ) . Given the public health crisis posed by Zika , and the potential costs associated with underpredicting the extent of the current outbreak , we pay special attention to evaluating the sensitivity of our models to variations in our preliminary dataset . Historical geographical data on cases in the Americas are lacking , given the recent introduction of the virus , and the routes and drivers of transmission involved in that outbreak are uncertain , preventing meaningful cross-validation of models of the current outbreak with our Old World model . However , it is worth noting that recent phylogenetic work suggests a deep phylogenetic division between African and Asian strains , the latter of which as a monophyletic group include the entire radiation through French Polynesia into current outbreak areas; [11 , 37] to address the potential evidence that African and Asian strains of the virus may be ecologically distinct , we present models trained on each continent and projected globally as a basic sensitivity analysis ( Fig 2 ) . The two models cross-validate weakly compared to the performance of the global model; driven by both the 50% reduction in sample size and the higher degree of aggregation of Asian occurrences , the two projected distributions are dramatically different . Despite the over-prediction of the Asian model in Africa and the possible overfitting of the African model , we emphasize that neither extreme scenario predicts any substantially greater range in North America than our main ensemble model . Moreover , our Asian model underpredicts but does predict two major hotspots of occurrence in Brazil , the Ceara/Rio Grande do Norte region and Roraima , both of which spatially correspond to hotspots of Zika according to the recent Faria et al . publication in Science , [11] adding further support to the model . Finally , despite low transferability between continents , both sub-models are well matched by our aggregated model in their native range , further supporting the accuracy and predictive power of our global projection . Recently published work by Bogoch et al . [38] uses an ecological niche model for dengue as a proxy for the potential full distribution of ZIKV in the Americas , presenting findings in terms of potential seasonal vs . full-year transmission zones . While that approach has been effectively validated for dengue transmission in mosquitoes , using a model of one disease to represent the potential distribution of another emerging pathogen is only a placeholder , and is particularly concerning given the lack of evidence in our models that ZIKV and dengue have a similar niche breadth . [39] Comparing our niche models for dengue and ZIKV reveals that the two niches are significantly different ( Schoener’s D = 0 . 176; p < 0 . 01; Fig 3 ) . While the two occupy a similar region of global climate space , Zika is more strictly tropical than dengue , occupying regions with higher diurnal temperature fluctuations and seasonality of precipitation ( Fig 3A ) . Projecting niche models to the year 2050 suggests that expansion of Zika’s niche outside the tropics is an unlikely scenario , independent of vector availability ( Fig 4 ) . However , significant westward expansion in South America and eastward expansion in Africa implies that Zika may continue to emerge in the tropics . Moreover , our future projections for dengue ( which strongly agree with previously published ones [40] ) show an expansion out of the tropics that is not shared with Zika ( Fig 4 ) . These results call into question the applicability of dengue niche models used to project a significant future range for Zika in North America . [38] Finally , we add a last layer of validation in the form of an analysis aggregating our and Messina et al . ’s data , and include the results of an updated ensemble model in Fig 5 ( as well as S6 and S7 ) . Even with spatial thinning , that updated model is still heavily biased in favor of the South American occurrence data , which it predicts excellently , compared to a weaker fit in Africa and Asia . That accompanying loss of specificity is partly responsible for a lower AUC than our main model ( AUC = 0 . 970 ) and the low TSS-based threshold ( 271 , from 0 to 1000 ) that produces the substantially-greater predicted range shown in S7 Fig . The model does predict the current outbreak more effectively than ours , in particular better encompassing the southern half of Brazil where a surprising number of cases are clustered . But those southward expansions are accompanied by far less expansion above the equator in the Americas , and once again with the exception of the southernmost tip of Florida , there is no substantial predicted range in the United States , even along the Gulf Coast . If model discrepancies are attributed to evolutionary change and not to differences in model methods and specificity , those evolutionary changes seem to have done little to expand the North American niche of the virus ( S8 Fig ) .
Ecological niche modeling has become one of the most generalized and useful parts of the streamlined response process for emerging infections . Recently published ecological niche models for Zika using MAXENT [30] and boosted regression trees [29] have resulted in somewhat conflicting results . Samy et al . , using data exclusively from the range of the current outbreak , project autochthonous transmission in the southeastern United States , and potentially throughout the U . S . following regional outbreaks introduced by travelers . Their analysis incorporates socioeconomic factors into prediction , a valuable extra dimension we did not incorporate into our analysis; but the prediction of regions throughout the United States and most of the European continent as suitable based on only these criteria ( i . e . despite lacking available vectors ) seems uninformative except for the prediction of sexual outbreaks . Samy et al . , however , conclude: “In Western Europe , ZIKV transmission risk is enhanced by travel times and connectivity to known transmission areas; as such , isolated autochthonous cases may occur at least seasonally when competent vector species are present . ” [30] Messina et al . have a similar finding , based on a primarily ecological approach applied to 323 occurrences mostly from the New World; they map out most countries in the world as highly suitable , including the United States , with the conclusion that 2 . 17 billion people live in countries within Zika’s potential expanse . [29] These studies , being contemporaneous , do not refer to each other , and their conflicting results could render Zika forecasts unclear to the media and policymakers . Interpreting conflicts between these models and those published here requires acknowledging three fundamental problems . First , differences in virulence between American and Asian strains of the virus may have changed the range limits . The niche of the vector-borne disease is manifest in its transmission and prevalence in mosquitoes ( as well as humans and reservoirs ) , and increases in virulence could change the threshold of habitat suitability manifest in range limits . Without comparative work using updated data in Samy et al . and Messina et al . ’s papers , equal support exists for our differences being attributable to methodological discrepancies or to a difference between Asian and American strains . But in the preliminary analysis we present in the supplementary information , incorporating data from the New World does not substantially expand projections in the United States ( though a greater region of Brazil is predicted ) ; and we believe a combination of evolutionary shifts and methodological differences is likely the most parsimonious explanation for differing results . Second , we acknowledge the untested possibility that Zika has been expanding in its range since discovery in the 1940s ( though , the virus was soon recorded in Borneo and Vietnam in the 1950s [23] ) , which would also decrease both the accuracy of our models in that region , and their power in the New World compared to the models published in the other two studies . Testing that possibility using our data broken down by time periods would be strongly statistically biased by the non-random element of viral discovery in different tropical countries , a factor for which it would be nearly impossible to control . Phylogenetic evidence has placed the introduction in the Americas within the last decade [11] , but the age of divergence between Zika and closely related viruses like Japanese and St . Louis Encephalitis Viruses is less certain . Improving phylogenetic evidence based on updated Old World genomes in the coming years is a far more appropriate methodology for testing different biogeographic theories within that region . Third and finally , we acknowledge the possibility that dispersal limitations have changed between the Old and New World , in such a way that the present expansion of Zika is not the emergence of novel niche space but the manifestation of hidden plasticity . This possibility is troubling from a public health perspective: if Zika’s niche is simply more expansive than current data/models capture , its geographic expansion could progress much further than we predict . This problem is fundamental to all predictive models applied to biological invasions , but Broenniman & Guisan [35] suggest that combining data from the native and invasive range maximizes the utility of ENMs in these scenarios . In our combined model we find evidence for subtle differences , especially in South America , but our findings remain sound with respect to the boundaries of transmission in North America . In any niche modeling study , there is always the possibility for error by omission; but we find no evidence that this has occurred in our study . The dynamics of arboviruses at the range margins of their vectors are complex . In the case of dengue , the distribution of the virus in the United States ( and elsewhere in temperate regions ) remains more constrained than the range of its vectors . Our paper tests and rejects the hypothesis that predictions of Zika will occupy the entire niche of Aedes populations in North America , disagreeing with the two recently published niche model studies . Our models imply a similar constraint on Zika transmission to that of dengue if not a more pronounced one , and owing to the complexities surrounding transmission dynamics at the edges of suitable ranges , [41] the potential existence of Zika in even the southernmost parts of Florida [42] may not sustain autochthonous Zika transmission indefinitely . Making more specific predictions within Florida can be done through ecological niche models , but is likely more appropriately achieved through conventional epidemiological models that explicitly model vector abundance , biting rates and phenology . Our models find an ecological nonequivalence of Zika and dengue , and suggest that the niche of the virus in both Africa and Asia is far narrower than what other models project based on current outbreak data or based on knowledge of dengue’s spread . We reject our first hypothesis , but based on the occurrence of Zika cases outside our predicted suitable range for the virus , we cannot eliminate our second hypothesis that the 2016 Zika outbreak may be in ephemeral , rather than stable , parts of the Zika transmission niche due to episodic climatic conditions . Specifically , El Niño Southern Oscillation ( ENSO ) events drive outbreaks of dengue in the Americas and in Southeast Asia , [43] and Paz et al . [12] have conjectured that the 2015 ENSO event could have contributed to the severity of the ZIKV outbreak in North and Central America ( in response to Bogoch et al . [38] ) . While wind-dispersed mosquitoes carrying infections can be responsible for the introduction of diseases to new regions , [44] reported cases in the United States have all been contracted sexually or while traveling abroad to regions with endemic outbreaks , further supporting the tropical constraint hypothesis . However , in the second hypothesis scenario , the rapid expansion during the current outbreak beyond the boundaries of the stable transmission niche is unlikely to be followed by naturalization of the pathogen in the United States in the future , except perhaps in the southernmost tip of Florida . While ecological niche models relate occurrence to climate , drivers of disease may operate at the temporal scale of weather , and we suggest further analyses of a different methodology are necessary to confirm or reject the potential contribution of El Nino or anomalous storms to Zika’s expansion . In the case of our third hypothesis , if alternative modeling efforts based on data from the Americas are evidence that the niche of the American strain of the virus has broadened , it is possible that mutations allowing increased virulence or changing transmission dynamics have occurred ( and that weather events have not driven the severity of the current outbreak ) . From the results of our supplementary analysis using aggregated global data , we continue to treat the third hypothesis as a hypothesis for which there may be weak evidence . But we suggest it cannot be rejected or accepted confidently unless alternative hypotheses are eliminated and more evidence is collected–in particular , empirical data demonstrating or failing to find differences in transmission dynamics or virulence between the native Asian virus and its invasive descendant ( rather than global comparisons and cross-validations of different ecological niche models ) . Our models nevertheless suggest it could be premature to expect Zika naturalization as a widespread eventuality in North America , as other models have forecasted . Without more definitive information on the basic biology of Zika , however , the confidence with which niche models can forecast pandemics is limited . In particular , we also draw attention to recent evidence suggesting Zika persistence may depend on wildlife reservoirs in addition to human hosts and mosquitoes . Primates have been suggested as the primary candidate clade because the Zika flavivirus was first isolated in a rhesus macaque in the Zika Forest in Uganda . But as rhesus macaques do not occur on the African continent , and were captive there for inoculation experiments , the primate reservoir hypothesis remains unsupported . A 2015 case of an Australian presumed to have contracted Zika from a monkey bite while traveling in Indonesia , however , indicates that primates may transmit the virus directly . [9] Additionally , antibodies against Zika have been observed in several rodent and livestock species in Pakistan , [45] as well as several large mammal species , including orangutans , zebras , and elephants . [46] The potential for any North American wildlife species to play host to Zika is , at the present time , entirely unknown , and the emergence of novel amplification hosts ( which may allow the virus to proliferate above the host density threshold in vectors in regions otherwise unsuitable for sustained transmission ) could potentially expand the suitable range margins of Zika infection on a global scale . From the results of our model we find strong evidence for the hypothesis that the global threat of a specifically vector-borne Zika pandemic , though devastating , may be most acute in the tropics; and we find that the evidence of future North American transmission in the literature is not unequivocal . However , we concur with the scientific majority that sexual transmission of Zika infections may still facilitate a significant outbreak in the United States and other previously unsuitable regions , particularly under evolutionary processes that select for the most directly transmissible strains of pathogens . [47] A case of sexual transmission in Texas has been suspected in the 2016 outbreak , and two previous reports of likely sexual transmission of ZIKV occurred in 2011 and 2015 . [5 , 48] Even if the Zika cases in the United States represent a rare spillover outside of the mosquito-borne viral niche , sexual transmission could create a new , unbounded niche in which the virus could spread . We draw attention to the potential parallels with simian and human immunodeficiency virus ( SIV/HIV ) , for which a sexually transmitted pandemic has overshadowed the zoonotic origin of the disease . [49] With Zika’s asymptomatic presentation and the overall confusion surrounding its basic biology and transmission modes , we caution that its potential for severe sexually-transmitted outbreaks cannot be overlooked in the coming months . To address the broader community of modelers and ecologists involved in the Zika intervention , we conclude with a final cautionary note . The consequences of under-predicting an outbreak’s potential distribution are obvious and our results are phrased cautiously as a result . But there are also economic and social consequences to over-predicting the potential distribution , especially in the United States . The response to Zika is necessarily political and consequently involves the division of resources between domestic preparedness and international relief; while new tools are being developed to help allocate funds efficiently based on epidemiological principles ( we particularly highlight the work of Alfaro-Murillo et al . [50] ) , global overestimation of the virus’s trajectory could vastly reduce the power of those methods . Models like those of Messina et al . and Samy et al . that predict substantial Zika expansion in the United States , and in the case of the former suggest Zika could threaten up to 2 . 17 billion people , contribute ( independent of accuracy ) to fear of an American pandemic . This prediction necessarily diverts funding away from relief efforts in Brazil and other affected countries in Latin America , increasing the probability of traveler infections feeding sexual outbreaks in the U . S . ; and further reduces the credibility and impact of the American foreign response to Zika by mobilizing potentially-unnecessary domestic responses . At the time of writing , the Zika Vector Control Act passed by the U . S . House of Representatives weakens permit requirements for spraying pesticides near bodies of water without reallocating any funding for Zika interventions; and preventative efforts in New York City alone will cost $21 million to trap mosquitoes and hire epidemiological experts , with other cities outside our predicted range investing in preparation and vector control to similar degrees . Voices of scientific authority contributing to fear in the United States can substantially impact the political response to Zika , and it serves future modeling efforts to be as accurate , cautious , and objective as possible in the information and statistics that underpin media and policy conversations . But even more importantly , scientific teams with different approaches and data must work collaboratively to interpret the discrepancies between their results and to build an unbiased scientific consensus that is accessible to the public . | A combination of media attention and the declaration of a World Health Organization state of emergency have made the pandemic expansion of Zika virus a topic of great public concern . Understanding the threat North America faces from the still-expanding viral range requires an understanding of the historical range and ecology of the disease , a topic currently difficult to study due to incomplete occurrence data . We compile the most comprehensive geospatial dataset of Zika occurrences in its native range , beginning with its discovery in 1947 , and build bioclimatic models that set an outer bound on where the virus is likely to persist . Our results suggest Zika is likely far more constrained than the closely-related dengue fever , on which many projections have been based . While Zika poses a serious threat in current outbreak regions and is clearly a high-priority neglected tropical disease , our models suggest that even under an extreme climate change scenario for 2050 , the disease is unlikely to become cosmopolitan in most temperate regions as a vector-borne disease , a discrepant finding from the results of non-ensemble modeling methods . Despite that , sexual transmission remains a serious public health concern , and a route by which Zika could become a severe public health emergency in temperate zones , including in the United States . | [
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] | 2016 | An Ecological Assessment of the Pandemic Threat of Zika Virus |
Sexual reproduction is a universal mechanism for generating genetic diversity in eukaryotes . Fungi exhibit diverse strategies for sexual reproduction both in nature and in the laboratory . In this study , we report the discovery of same-sex ( homothallic ) mating in the human fungal pathogen Candida tropicalis . We show that same-sex mating occurs between two cells carrying the same mating type ( MTLa/a or α/α ) and requires the presence of pheromone from the opposite mating type as well as the receptor for this pheromone . In ménage à trois mating mixes ( i . e . , “a x a + α helper” or “α x α + a helper” mixes ) , pheromone secreted by helper strains promotes diploid C . tropicalis cells to undergo same-sex mating and form tetraploid products . Surprisingly , however , the tetraploid mating products can then efficiently mate with cells of the opposite mating type to generate hexaploid products . The unstable hexaploid progeny generated from this coupled process of same- and opposite-sex mating undergo rapid chromosome loss and generate extensive genetic variation . Phenotypic analysis demonstrated that the mating progeny-derived strains exhibit diverse morphologies and phenotypes , including differences in secreted aspartic proteinase ( Sap ) activity and susceptibility to the antifungal drugs . Thus , the coupling of same- and opposite-sex mating represents a novel mode to generate polyploidy and genetic diversity , which may facilitate the evolution of new traits in C . tropicalis and adaptation to changing environments .
Sexual reproduction drives the evolution of new traits and adaptation to new environments in eukaryotic organisms . Fungi adopt different strategies for sexual reproduction [1 , 2] . Many fungal species exhibit opposite- and same-sex mating ( or heterothallic and homothallic reproduction , respectively ) , both in nature and under experimental conditions [3–5] . For example , the human fungal pathogens Candida albicans and Cryptococcus neoformans undergo both opposite- and same-sex mating in the laboratory [3 , 4] . The vast majority of natural isolates of C . neoformans are of the α mating type and same-sex mating can occur between both isogenic and non-isogenic α strains in a pheromone-dependent manner [3] . In C . neoformans and C . albicans , sexual mating and subsequent meiosis or chromosome loss often results in aneuploid forms and the generation of genetic and phenotypic diversity de novo [6 , 7] . C . albicans is a diploid organism that undergoes a parasexual reproductive cycle [8] . Over 90% of natural isolates of C . albicans are heterozygous at the mating type-like ( MTL ) locus and hence carry both MTLa and MTLα idiomorphs [9] . To mate , diploid MTLa/α cells must first undergo homozygosis at the MTL locus to become an MTLa/a or α/α ( a or α ) strain . In addition , a or α cells must switch from the white to the opaque phenotype to become mating-competent [10] . White and opaque cells are two heritable cell types and can maintain their phenotypic states for many generations . White cells are relatively small and round , whereas opaque cells are larger and elongated [11] . The two cell types also differ in global gene expression profiles and mating competency , with only opaque cells being capable of mating efficiently [10 , 12 , 13] . Opaque a cells secrete a-pheromone whereas opaque α cells secrete α-pheromone , and these induce the development of mating projections in cells of the opposite mating type [8] . Inactivation of the Bar1 protease , which is required for degradation of α-pheromone in a cells of C . albicans , allows same-sex mating between opaque “a x a” cells [4] . The presence of white or opaque helper cells with an opposite MTL type also promotes same-sex mating between “a x a” or “α x α” cells of C . albicans [4 , 14] . C . albicans therefore undergoes same-sex mating when cells are mixed in a “ménage à trois” mode . In C . neoformans , same-sex mating ( or fruiting ) between isogenic cells has been proposed to be an important mode of sexual production in nature [3 , 15] . Candida tropicalis is also an important human fungal pathogen that is a close relative of C . albicans [16] . While C . albicans primarily lives as a human commensal , C . tropicalis is commonly isolated from humans as well as from certain environmental niches such as sea water , soil , and plants , especially in tropical or subtropical areas [16–18] . Although C . tropicalis is largely susceptible to antifungals such as fluconazole and amphotericin B , development of resistance has been reported , especially in AIDS patients , intensive care units and leukemia patients [19] . Recently , white-opaque switching and a parasexual cycle have been documented in C . tropicalis [20–22] . Similar to C . albicans , C . tropicalis also has a third heritable cell type , namely the “gray” or “hybrid” type , which is distinguishable from the white and opaque cell types . The gray or hybrid cell type exhibits an intermediate-to-high level of mating competency between that of white and opaque cell types [23 , 24] . In this study , we report the discovery of same-sex mating and a coupled process of same- and opposite-sex mating in C . tropicalis . In the presence of pheromone from the opposite mating type , C . tropicalis cells can undergo same-sex mating and form MTL homozygous tetraploid progeny under laboratory culture conditions . To our surprise , the MTL homozygous tetraploid products can further mate with diploid cells of the opposite MTL type in ménage à trois mating mixes . The latter mating mixes contained two “a” strains and one “α” strain ( a x a +α ) or two “α” strains and one “a” strain ( α x α + a ) . This coupling of homothallic and heterothallic mating processes generated high-ploidy ( >4N ) strains , which exhibited a high degree of genomic instability and subsequently gave rise to numerous genetically and phenotypically diverse offspring . The coupling of same- and opposite-sex mating may represent a novel mode of sexual reproduction in fungi , and could facilitate the rapid evolution of new traits thereby driving adaptation to changing environments .
The frequency of white-to-opaque switching is extremely low under regular culture conditions in some natural isolates of C . tropicalis , and the opaque phenotype is also unstable in some strain backgrounds [20 , 21] . We previously demonstrated that both GlcNAc and pH are critical regulators of white-opaque switching and sexual mating in C . albicans and C . tropicalis [25 , 26] . Here we performed opposite-mating assays between two C . tropicalis strains ( a x α ) on four different culture media ( Lee’s glucose , pH 6 . 8 and pH 8 . 5 , and Lee’s GlcNAc , pH 6 . 8 and pH 8 . 5 ) at 25°C . As shown in S1 Fig , both GlcNAc and alkaline pH conditions increased the mating efficiency of C . tropicalis cells . Lee’s GlcNAc with pH 8 . 5 was found to be the optimal culture medium for mating and , unless specified otherwise , this medium was used for most mating experiments in this study . The presence of pheromone from the opposite mating type can induce the expression of both a- and α-pheromone-encoding genes , MFA1 and MFα , respectively , and promote same-sex mating in C . albicans [4 , 14 , 27] . Given the genetic and morphological similarities between C . tropicalis and C . albicans , we predicted that C . tropicalis cells would similarly be capable of same-sex mating . We therefore tested this possibility using two “a” strains of C . tropicalis , that are histidine or arginine auxotrophs ( his1/his1 or arg4/arg4 ) , respectively . As shown in Fig 1A , a mixture of two “a” strains was spotted onto Lee’s GlcNAc ( pH 8 . 5 ) medium and treated with or without synthetic α-pheromone . After seven days of growth at 25°C , cells were replated onto histidine and arginine dropout plates to select for same-sex mating products . Progeny colonies grew on the selective plates when a cells had been co-incubated with synthetic α-pheromone , whereas no colonies were observed in the mock-treated control ( Fig 1A ) . PCR assays verified that all progeny from “a x a” mating crosses were MTLa cells like the parental strains ( Fig 1B ) . Flow cytometry ( FACS ) analysis confirmed that mating progeny were tetraploid ( Fig 1C ) . These results indicate that synthetic α-pheromone induces same-sex mating between two a strains of C . tropicalis . A BLAST search revealed that C . tropicalis has a single ortholog of C . albicans MFα and has more than 10 copies of the MFA1 ortholog . We hypothesized that “a” cells would secrete a-pheromone and thus promote same-sex mating in “α” cells , and vice versa . To prove this , we performed “a x a” or “α x α” same-sex mating assays in a sandwich-culture method ( Fig 2 ) . Mating progeny were observed arising in the sandwich cultures of both “a x a” mixtures with α cell side patches and in “α x α” mixtures with “a” cell side patches , although mating efficiencies were relatively low ( 5 . 9 x 10−8 and 4 . 8 x 10−7 for “a x a” and “α x α” experiments , respectively ) . However , no progeny were observed in control cultures when using side patches that contained cells of the same MTL cell type as in the mating test mixture . These results establish that “a” and “α” cells of C . tropicalis secrete pheromones that promote same-sex mating in cells of the opposite mating type . We next performed quantitative real-time PCR ( q-RT-PCR ) assays to examine the relative expression levels of MFA1 , MFα , STE2 ( encoding the receptor for α-pheromone ) , and STE3 ( encoding the receptor for a-pheromone ) in a cells responding to synthetic α-pheromone . As shown in Fig 3A , the relative expression levels of the four genes were significantly increased in a cells of C . tropicalis when treated with synthetic α-pheromone . These results indicate that synthetic α-pheromone induces a cells to become potential bi-maters that exhibit features of both a and α cells in terms of cell identity . Ste2 is required for α-pheromone-induced responses in a cells and is essential for opposite-sex mating in C . albicans [28] . To confirm the importance of pheromone signaling , we examined the role of Ste2 in same-sex mating in C . tropicalis using ménage à trois mating assays ( “a x a + α helper” ) . As shown in Fig 3B , both the WTa x ste2a and ste2a x ste2a mating cultures in the presence of WT α cells failed to produce progeny . However , the mating efficiencies of the WTa x WTa control , WTa x STE2Ra , and STE2Ra x STE2Ra were comparable ( approximately 1 x 10−3; STE2R is a ste2/ste2 deletion strain in which STE2 has been reconstituted ) . These results indicate that the Ste2 receptor is essential for “a x a” same-sex mating in C . tropicalis . As demonstrated earlier , the efficiency of same-sex mating induced by synthetic pheromone or opposite mating type cells in a sandwich culture was relatively low ( Figs 1 and 2 ) . We speculated that the pheromone added to the medium or secreted by opposite mating type cells in the sandwich culture method is rapidly degraded by cells in the mating mixture and not enough to support efficient same-sex mating . We therefore performed traditional ménage à trois mating assays in which cells of different mating types are mixed with one another , as described in Fig 3B and previous publications [4 , 14] . We grew the three-way mating mixtures ( “a x a + α helper” or “α x α + a helper” ) on four different media ( Lee’s glucose , pH 6 . 8 and pH 8 . 5 , and Lee’s GlcNAc , pH 6 . 8 and pH 8 . 5 ) at 25°C . The parental mating strains contained a complementary his1/his1 or arg4/arg4 nutritional markers , whereas the helper strains were auxotrophic for both histidine and arginine ( his1/his1 arg4/arg4 strains ) . Consistent with our results of opposite-sex mating , the efficiencies of the three-way matings on Lee’s GlcNAc medium , pH 8 . 5 , were higher than those on the other three media ( Fig 4A ) . Furthermore , the efficiencies in the ménage à trois mating assays were much higher than those of “a x a” and “α x α” same-sex matings induced by synthetic pheromone or by pheromone secreted by neighboring cells in sandwich experiments ( Figs 1 and 2 ) . We performed PCR assays to verify the MTL types after mating in the ménage à trois cultures ( Fig 4B ) . To our surprise , while there were a small number of MTLa or α homozygotes , we identified many mating progeny with a heterozygous MTLa/α cell type . Upon further examination , we found that approximately 5% of mating progeny from the “a x a + α helper” experiment were MTLa homozygotes , whereas 95% were MTLa/α heterozygotes . Similarly , 27 . 5% of progeny selected from the “α x α + a helper” mating experiment were MTLα homozygotes , whereas 72 . 5% were MTLa/α heterozygotes ( Fig 4C ) . We next analyzed the genomic DNA content of mating products using FACS assays . As shown in Fig 4D , the genomic DNA of mating progeny varied from 2N to 6N and was enriched in cells that were 3N to 5N . Approximately 40% of the progeny were euploid ( 3N , 4N , 5N , or 6N ) with 60% being aneuploid . The occurrence of high ploidy ( >4N ) and MTLa/α heterozygotes implies that the MTL homozygous products of same-sex mating between “a x a” or “α x α” cells subsequently underwent an efficient opposite-sex mating with the MTL helper strain in the ménage à trois cultures . For example , in the “a x a + α helper” experiment , the helper strain secreted sufficient pheromone to promote “a x a” same-sex mating , which generated tetraploid a/a/a/a ( HIS+LEU+ ) progeny . The tetraploid MTLa cells then mated with α helper cells and generated higher ploidy progeny ( 6N , S2A Fig ) . An analogous process for “α x α + a helper” experiments is assumed to have occurred . Based on the high frequency of isolated progeny with a lower ploidy ( <6N ) , we reasoned that the genome of high-ploidy progeny could be extremely unstable . To confirm whether tetraploid ( a/a/a/a or α/α/α/α ) strains could mate with diploid cells of the opposite mating type , we performed mating assays on Lee’s GlcNAc medium by crossing a tetraploid strain ( either a/a/a/a or α/α/α/α ) with a diploid α/α or a/a strain . As demonstrated in S3 Fig , tetraploid cells mated efficiently with opposite sex diploid cells and generated high-ploidy a/α heterozygous cells . In the ménage à trois cultures , an alternative coupled opposite-sex/opposite-sex mating process could also generate hexaploid progeny . As shown in S2B Fig , diploid “a/a” and “α/α” cells could first mate and generate tetraploid progeny ( a/a/α/α ) . Tetraploid “a/a/α/α” cells could subsequently undergo homozygosis at the MTL locus and become “a/a/a/a” or “α/α/α/α” cells . The “a/a/a/a” or “α/α/α/α” cells could then mate with diploid “α/α” or “a/a” cells to generate hexaploid progeny . However , the genome of tetraploid progeny was relatively stable under our culture conditions . We estimated that tetraploid cells rarely underwent homozygosis at the MTL locus and lost both “a” or both “α” chromosomes in our ménage-a-trois cultures . To establish this , we performed quantitative mating assays between a tetraploid “a/a/α/α” strain ( mating products of CAY3741 x CAY2061 ) and a diploid “a/a ( CAY2060 ) ” or “α/α ( CAY2063 ) ” strain . We found that the mating efficiencies of both “a/a/α/α x a/a” and “a/a/α/α x α/α” crosses were extremely low ( ~1 x 10−6 to 1 x 10−5 ) . Therefore , the occurrence of this alternative mating process was very rare in our mating assays ( S2B Fig ) . However , this coupled opposite-sex mating process could occur in nature or under certain specific conditions that affect the stability of the chromosome carrying the MTL locus . Wor1 is the master regulator of white-opaque switching in C . albicans and C . tropicalis [29–31] . Since only opaque cells can mate efficiently in “a x α” opposite-mating assays , we next examined whether deletion of WOR1 affected same-sex mating in C . tropicalis . As shown in Fig 4A , deletion of WOR1 in α cells blocked same-sex mating between “wor1/wor1 α x WTα” in the presence of “a helper”; however , deletion of WOR1 in a cells did not block same-sex mating between “wor1/wor1 a x WTa” in the presence of “α helper” . We do note , however , that the mating efficiencies of the “wor1/wor1 a x WTa” cross were only ~10% of those of the “WTa x WTa” control cross . These results suggest that white-to-opaque switching is essential for “α x α” same-sex mating but not for “a x a” same-sex mating in C . tropicalis . The mechanism of phenotypic switching-independent mating needs to be further investigated . The ploidy of progeny from “a x a + α helper” and “α x α + a helper” mating assays varied from 2N to 6N ( Fig 4D ) . Genomic changes often lead to phenotypic variation , and morphological change is necessary for virulence in pathogenic Candida species [32] . We selected a portion of mating progeny strains with different ploidy levels and performed a morphological analysis on Lee’s glucose medium at 25°C . This culture condition normally does not favor filamentous growth in natural strains of C . tropicalis . As demonstrated in Fig 5 , colonies of the three parental strains ( CAY2060 , GH1374h , and CAY4149 ) were smooth and contained only yeast cells . However , mating progeny with different genomic DNA content exhibited 2–7 colony types under the same culture condition ( Fig 5 ) . These colony types included smooth , wrinkled , star-like , and irregular colonies . Some colonies also varied in size , suggesting that growth rates may be different . Microscopy assays demonstrated that different types of colonies contained different cell types including regular yeast-form , opaque and gray-like cells , as well as filamentous cells . For example , progeny No . 22 exhibited at least four types of cellular phenotypes including gray-like ( 1 ) , white ( 2 ) , opaque-like ( 3 ) , and filamentous ( 4 ) cells ( Fig 5 ) . The red dye phloxine B was added to the medium and revealed extensive differences in colony coloration between progeny , which might reflect differences in the structure or integrity of the cell wall ( e . g . , see #22 , 64 , and 77 ) . Genomic variations ( especially the occurrence of aneuploidy ) often drive the evolution of antifungal resistance in fungi [6 , 7 , 33] . We next tested the susceptibility to amphotericin B ( Amp ) and caspofungin ( Casp ) , two potent and widely used antifungal drugs , in 77 representative mating progeny-derived strains selected according to their colony morphologies ( as exhibited in Fig 5 ) . As shown in Fig 6 , the minimum inhibitory concentrations ( MIC ) of Amp for the parental strains ( CAY2060 , GH1374h , and CAY4149 ) were about 1 . 3 μg/mL , 1 . 8 μg/mL , and 0 . 7 μg/mL , respectively . Although values varied considerably , all progeny-derived strains tested exhibited a MIC value between 0 . 4 to 1 . 8 μg/mL , with no strains showing a MIC value higher than 1 . 8 μg/mL ( that of GH1374h ) . Similar results were observed in the Casp treatments ( Fig 6 ) as progeny-derived strains exhibited a MIC value of Casp between 0 . 4 to 1 . 0 μg/mL . Saps represent a major virulence factor of C . tropicalis , as they are critical for invasive growth in the host and for nutritional acquisition [34] . We examined Sap activity using YCB-BSA halo-ring formation assays [35] in 20 strains derived from progeny of the coupled mating process . As shown in S4 Fig , the 20 representative strains exhibited highly variable sizes of the BSA precipitation rings , which reflect the levels of Sap activity , after three days of growth on YCB-BSA medium . To further quantify the Sap activity of the mating progeny , we grew cells for six days and examined the width of BSA precipitation rings . As shown in Fig 7 , the ring width values of the three parental controls showed limited variation , whereas those of the mating progeny-derived strains varied across a much wider range . Several progeny exhibited a lower level of Sap activity than the parental controls , whereas most showed an increased level of Sap activity compared to controls . This result suggests that the Saps are often expressed at a higher level in the high-ploidy progeny-derived strains than in their diploid parental strains . It is also possible that the selected progeny-derived strains for the Sap activity assays were in a different morphological state to control strains ( e . g . , wrinkled , filamentous , irregular , or opaque-like ) and that these changes caused altered Sap activity . The genome of high-ploidy cells is often unstable in yeasts [36] . The phenotypic diversity of mating progeny-derived strains could therefore be a result of genomic instability associated with their high-ploidy content . We examined the DNA content of several colonies with distinct morphologies ( see Fig 5 ) and found that the DNA content of different colonies arising from the same mating product were varied and generally contained less DNA than the mating product ( S5 Fig ) . These results indicate that high-ploidy progeny cells often underwent chromosome loss when grown on regular culture medium such as Lee’s glucose medium . To further elucidate the biological significance of the generation of high-ploidy progeny from coupled same- and opposite-sex mating , we directly tested the genomic stability of mating progeny with 3N , 4N , and 5N genome on SCD medium . These experimental progeny strains were engineered to contain a single copy of ARG4 and HIS1 at the endogenous locus . The detailed methods for the generation of these strains are presented in Materials and methods . C . tropicalis ARG4 and HIS1 are on different chromosomes ( Butler G . , personal communication ) . Therefore , the frequency of loss of ARG4 and/or HIS1 markers could be used as an indicator of genomic instability . As demonstrated in Fig 8 , the frequencies of marker loss exhibited a gradual increase from 3N progeny to 5N progeny . For example , the frequencies of ARG4 loss were 0 . 20±0 . 05% , 0 . 55±0 . 24% , and 1 . 56±0 . 06% in the 3N , 4N , and 5N progeny strains , respectively . Considering that , of the 3 , 4 , or 5 homologous chromosomes in 3N , 4N , and 5N progeny , only one chromosome carried a nutrient marker , the difference between chromosome loss frequencies in cells with different ploidies could be much higher than that presented . These results suggest that genomic instability correlates with increased ploidy in mating progeny . To further verify whether there would be a tendency to return to lower ploidy levels in high-ploidy progeny , two progeny strains containing 5N or 6N genomic DNA and eight derivatives with a distinct colony morphology were selected for passaging assays . As shown in S6A Fig , the ten strains were inoculated into laboratory medium ( liquid Lee’s glucose , pH6 . 8 ) for 24 hours of growth and then re-inoculated into fresh medium . In total , 20 passages were performed ( approximately 130 generations ) . Original cells and cells of the 5th , 15th , and 20th passages were subject to genomic DNA analysis . As shown in S6B and S6C Fig , there was a general tendency for most strains to adopt a lower ploidy . Thus , after 20 passages , most strains stabilized at a lower ploidy state that appeared to be euploid or close to euploid .
In this study , we report the discovery of same-sex mating as well as a coupled process of same- and opposite-sex mating in the fungal pathogen C . tropicalis . The coupled mating process represents a novel route for the generation of highly polyploid forms of a species . Genetic instability of polyploid cells provides an efficient mechanism for generating genetic and phenotypic diversity , and thereby promotes adaptation to diverse ecological niches and may ensure survival under harsh environments . Fungi exhibit multiple strategies for sexual reproduction [1] . Similar to its closely related species C . albicans , C . tropicalis must first undergo a switch from the default white state to the mating-competent opaque state to mate efficiently [20 , 21] . Opposite-sex mating involves two cell types with different MTL configurations ( namely a and α ) . Opaque a cells secrete a-pheromone to induce the formation of mating projections in opaque α cells , and vice versa . Two diploid cells of opposite sexes then undergo cell fusion and generate tetraploid cells [8] . In the current study , we observed that the presence of cells with an opposite MTL type ( or synthetic pheromone ) can induce same-sex mating between “a x a” or “α x α” cells in C . tropicalis ( Figs 1 and 2 ) . This is similar to what has been reported for same-sex mating of C . albicans cells [4 , 14] . Interestingly , although white cells of C . albicans are mating-incompetent , they can be induced to secrete pheromone and thereby help both same- and opposite-sex mating in opaque cells in a ménage à trois mating mixes [14] . In C . tropicalis , the tetraploid products of same-sex mating retain their opaque cell identity after mating [37] and these progeny ( a/a/a/a or α/α/α/α ) can therefore potentially mate with an opposite-sex strain . We now show that mating of a homozygous 4N cell and an opposite-sex 2N cell occurs very efficiently under ménage à trois mating conditions ( 95% efficiency for “a x a + α helper” mixes and 72 . 5% efficiency for “α x α + a helper” mixes , Fig 4C ) . This is because the efficiency of opposite-sex mating is much higher than that of same-sex mating , as described for another fungal pathogen , C . neoformans [3] . In this coupled two-step mating system , the initial same-sex mating generates tetraploid progeny of C . tropicalis , which then efficiently mate with the ‘helper’ strain that has a complementary MTL type to produce hexaploid progeny . In agreement with this , we observed efficient mating in 4N x 2N opposite-sex mating assays ( S3 Fig ) . FACS analysis revealed that hexaploid progeny were rare in the combined same- and opposite-sex mating experiments in C . tropicalis ( Fig 4D ) , implying that hexaploid progeny were unstable . Even under regular laboratory culture conditions ( in Lee’s glucose or SCD medium ) , polyploid cells underwent rapid chromosome loss and produced extensive genetic variation ( Fig 8B and S6 Fig ) . Although there was a general tendency towards cells adopting lower ploidy states , some cells with a high genomic content were relatively stable ( Fig 4D and S6 Fig ) . Overall , the loss of chromosomes resulted in a number of aneuploid cell states , although a relatively large portion ( ~40% ) of mating progeny were 3N to 5N cells with euploid , or close to euploid , genomes ( Fig 4D ) . These results reveal that polyploid cells often undergo concerted chromosome loss to stable cell states with a balanced complement of chromosomes . It has been demonstrated that specific aneuploid forms of C . albicans can provide a selective advantage under antifungal stresses [33] . ( Para ) sex generates high ploidy and promotes genetic diversity and even de novo genomic changes in both C . albicans and C . neoformans populations [6 , 7] . It has been proposed that same-sex mating contributed to the hypervirulent isolates of Cryptococcus gattii responsible for the Vancouver Island outbreak that started in 1999 [38] . These studies suggest that sexual reproduction confers novel traits to pathogenic species by producing recombinant progeny that include important genomic changes . These changes can enable fungi to better adapt to new ecological niches . In C . tropicalis , a higher ploidy ( >4N ) state generated in the coupled process of same- and opposite-sex mating might have an even more profound impact on generating genetic and phenotypic diversity . To evaluate phenotypic properties , we examined a number of diverse mating progeny for drug resistance , cell morphologies and Sap activity . As shown in Fig 6 , the MIC values of the antifungal amphotericin B varied from 0 . 4 to 1 . 8 μg/mL in different progeny-derived strains . However , none of these strains exhibited a higher resistance to amphotericin B than the most resistant parental strain ( GH1374h ) . Polyploid progeny produced multiple morphological types including opaque-like , gray-like , and filamentous phenotypes even under regular culture conditions ( Lee’s glucose medium at 25°C , Fig 5 ) . We also examined Sap activity given that this is a major virulence factor in pathogenic Candida species [34] . YCB-BSA assays demonstrated that Sap activity varied dramatically among different mating progeny ( Fig 7 and S4 Fig ) , and a large portion of strains exhibited higher Sap activity than the parental strains . This could be linked to the morphologies of these strains , as morphological switching and Sap activity are tightly linked with pathogenesis in both C . albicans and C . tropicalis [32 , 34] . These diversified properties may contribute to the evolution of virulence factors in C . tropicalis and rapid adaptation to changing environments . Polyploidy is prevalent in across the tree of life and has been well investigated in model organisms such as the yeast Saccharomyces cerevisiae and the plant Arabidopsis thaliana [39–41] . The genome of polyploids is also generally unstable in these species . It has been reported that polyploidy can drive rapid adaptation to stressful environments in S . cerevisiae [41] . In addition , clones evolved from polyploidy exhibited a high frequency of de novo mutations [40] , which may provide additional selective advantages . However , there could also be some disadvantages to being polyploid . For example , polyploid cells have an increased cell size , changed cellular architecture , and increased inaccuracy of chromosome segregation [39] . In C . albicans , tetraploids are less virulent and exhibit decreased fitness in the mammalian host [42] . Overall , there is a tendency to return to lower ploidy in each of these species , and a similar phenomenon is observed in C . tropicalis in our study ( S6 Fig ) . Of note , some aneuploid states were relatively stable perhaps due to their increased ability to adapt to certain growth conditions . Moreover , it has been shown that some aneuploid strains were drug resistant and when passaged they lost resistance but only lost some of the aneuploid chromosomes [7] . Chr5 was often stable for example . So , there is a bias for certain chromosomes being more stable . In summary , together with the previously reported opposite-sex mating , the discovery of same-sex mating in C . tropicalis indicates that sexual reproduction in pathogenic Candida species is conserved in terms of regulatory mechanisms and condition requirements . The coupled process of same- and opposite-sex mating in C . tropicalis represents a novel route of generating polyploidy , which may accelerate evolution and promote rapid adaptation to changing environments . Here , we therefore provide a new and valuable model with which to investigate the effect of polyploidy and aneuploidy . Our study also sheds new light on the diversified mating modes in fungi and adaptive mechanisms of pathogenic Candida species to environmental stresses .
C . tropicalis strains used in this study are listed in the supplementary S1 Table . Cells were routinely grown in YPD ( 20 g/L glucose , 20 g/L peptone , 10 g/L yeast extract; 20 g/L agar added for solid medium ) or synthetic defined ( SD ) medium at 30°C . Lee’s glucose ( pH 6 . 8 and pH 8 . 5 ) and Lee’s GlcNAc ( pH 6 . 8 and pH 8 . 5 ) were used for mating assays . Lee’s glucose ( pH 6 . 8 ) with the red dye phloxine B ( 5 μg/mL ) was used for morphological assays . For morphological analysis assays , cells were grown on Lee’s glucose ( pH 6 . 8 ) at 25°C . Colonies and cells were imaged after three days of growth . SD with dextrose as carbon source ( SCD ) was used for the evaluation of genomic stability . HIS1 and ARG4 nutritional markers were used to evaluate the genomic stability of mating progeny . Strains for genomic stability assays were generated as described below . The genome content of all strains used in genomic stability assays was examined by FACS analysis . Opaque cells were used for mating assays . For some strains , if the opaque phenotype was not stable , cells were initially grown on Lee’s GlcNAc ( pH 8 . 5 ) medium at 25°C for four days . This culture condition is conducive for formation of the opaque phenotype . Synthetic α-pheromone ( KFKFRLTRYGWFSPN ) used for induction of same-sex mating in a cells of C . tropicalis was synthesized by the company Scilight-peptide Inc . ( Beijing , China ) . To test α-pheromone-induced same-sex mating in C . tropicalis , 5 x 106 cells of CAY2060 ( MTLa/a arg4/arg4 ) and 5 x 106 cells of GH1374h ( MTLa/a his1/his1 ) were mixed , spotted on Lee’s GlcNAc ( pH 8 . 5 ) plates , and grown at 25°C for seven days . In the first three days , 40 μL synthetic α-factor ( 5mM ) was added to the medium surrounding the mating spots every 24 hours . The mating mixture with no pheromone treatment served as the mock control . After seven days of growth , cells were then replated onto synthetic media ( -Arg , -His , or lacking both ) for selectable growth of parental and mating progeny cells . Mating efficiencies were calculated according to the colony numbers obtained from SCD media . To perform the sandwich mating assays , a-strains ( CAY2060 and GH1374h ) or α strains ( CAY2061 and CAY2063 ) were initially grown on Lee’s GlcNAc medium ( pH 8 . 5 ) for four days at 25°C . Cells from the two a-strains ( or α-strains ) were taken from single colonies , mixed together and patched onto the same medium . Cells with an opposite MTL type were then patched close to each side of the same-sex mating mixture in a sandwich mode ( as shown in Fig 2 ) . The plates were cultured at 25°C for seven days . Cells of the “a x a” or “α x α” mating mixture were then replated onto selectable plates for the growth of parental ( -His or -Arg ) or mating progeny cells ( -His -Arg ) . To perform ménage à trois mating assays , cells of the parental strains were initially cultured on Lee’s GlcNAc plates ( pH 8 . 5 ) for four days at 25°C . 5 x 106 of cells of each mating partner and the helper strain ( “a x a + α helper” or “α x α + a helper” ) were mixed and spotted on Lee’s glucose or Lee’s GlcNAc for seven days of growth at 25°C . The mating mixture was then replated on SCD media ( -Arg , -His , or -Arg-His ) for selectable growth of parental and mating progeny cells . Mating efficiencies = the greater of ( number of progeny/number parent–progeny ) . Cells were incubated with shaking in liquid SCD medium . Cultures were harvested , washed , and resuspended in 1 x TE buffer ( 10 mM Tris , 1 mM EDTA , pH 8 . 0 ) and then fixed with 70% ethanol for two hours at room temperature . Cells were then washed with 1 x TE buffer and treated with RNase A ( 1 mg/mL ) for 24 hours and subsequently with proteinase K ( 5 mg/mL ) for two hours at room temperature . Cells were collected , washed with TE buffer , and stained with propidium iodide ( PI , 25 μg/mL ) . Stained cells were washed and resuspended in 1 x TE buffer for DNA content analysis . A total of ~10 , 000 cells of each strain were run on a FACS Caliber ( a multi-Gaussian cell cycle model , BD ) and the data was analyzed using the software FlowJo 7 . 6 . 1 . RT-qPCR assays were performed as described in our previous publication [26] . Briefly , after treatment with ɑ-pheromone ( final concentration 100 μM ) in liquid Lee’s GlcNAc ( pH 8 . 5 ) at 25°C for 6 hours , cells were harvested and washed with ice-cold ddH2O . Cells pellet was then subjected to total RNA extraction by GeneJET RNA Purification Kit ( Thermo Fisher ) following the instruction of manufacture . Total RNA ( 0 . 8 μg ) was used to synthesize cDNA with RevertAid H Minus Reverse Transcriptase ( Thermo Scientific , Inc . ) and subject to qPCR assays using SYBR green Mix ( TOYOBO , Inc . ) . The relative expression levels were determined using a Bio-Rad CFX96 real-time PCR detection system and normalized to that of C . tropicalis ACT1 . Cells of the control and mating progeny with different ploidy ( 2N to 5N ) were taken from a single colony initially patched on Arg/His dropout plates ( SCD-His-Arg ) at 25°C for growth overnight . Cells were then collected and washed with ddH2O . Approximately 200 cells were spotted on SCD medium containing His and Arg for seven days of growth at 37°C . To examine the loss of HIS1 or ARG4 , as an indicator of genomic instability , we tested the frequency of formation of auxotrophic cells ( His- , Arg- , or His-Arg- ) . Briefly , cells from the spot cultures were replated onto SCD medium containing Arg and His and grown at 30°C for 36 hours . The plates were then cultured on Arg-His- dropout SCD plates . The auxotrophic type was verified by patching assays using dropout SCD plates ( His- , Arg- , or His-Arg- ) . Percentage of his1- or arg4- cells = ( number of his1- or arg4- colonies/total number of colonies ) x 100% . Sap activity was tested using the YCB-BSA method as described previously [35 , 43] . Cells of C . tropicalis were initially grown on Lee’s GlcNAc ( pH 8 . 5 ) at 25°C for seven days . 5×106 cells of each strain in 5μL ddH2O were spotted onto YCB-BSA plates and cultured at 25°C for six days . The width of BSA precipitation rings ( halos ) , which reflect the activity of Saps , was examined at the third and sixth day . MIC assays were performed according to the NCCLS document M27-A2 and previous publications [7 , 44] . Three biological replicates were performed . C . tropicalis cells of each strain were initially patched on SCD solid medium for 24 hours at 25°C . Approximately , 500 cells were then inoculated into 200 μL RPMI-1640 medium ( w/v , 1 . 04% RPMI-1640 , 3 . 45% MOPs , NaOH used for pH adjustment to 7 . 0 ) in a 96-well plate for MIC testing . A series of amphotericin B or caspofungin concentrations ( from 0 . 3 to 2 . 4 μg/mL ) were used . Cells were incubated at 37°C in air for four days . The growth states of cells at different amphotericin B and caspofungin concentrations were recorded . | The fungal pathogen Candida tropicalis not only lives as a commensal in humans but is also widely distributed in diverse environments . Until recently , C . tropicalis was thought to be an asexual diploid organism . In this study , we report the discovery of same-sex mating and reveal an unusual process in which same- and opposite-sex mating are coupled in this fungus . The coupling process represents a novel mode of mating which produces unstable polyploid products and results in a high level of genetic and phenotypic diversity . This biological process may benefit the adaptation of C . tropicalis to a variety of ecological niches and promotes survival under stressful conditions . Our study expands the repertoire of mating strategies in fungi and sheds new lights on the generation of polyploidy and genomic flexibility . | [
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] | 2018 | A coupled process of same- and opposite-sex mating generates polyploidy and genetic diversity in Candida tropicalis |
Brain computation relies on effective interactions between ensembles of neurons . In neuroimaging , measures of functional connectivity ( FC ) aim at statistically quantifying such interactions , often to study normal or pathological cognition . Their capacity to reflect a meaningful variety of patterns as expected from neural computation in relation to cognitive processes remains debated . The relative weights of time-varying local neurophysiological dynamics versus static structural connectivity ( SC ) in the generation of FC as measured remains unsettled . Empirical evidence features mixed results: from little to significant FC variability and correlation with cognitive functions , within and between participants . We used a unified approach combining multivariate analysis , bootstrap and computational modeling to characterize the potential variety of patterns of FC and SC both qualitatively and quantitatively . Empirical data and simulations from generative models with different dynamical behaviors demonstrated , largely irrespective of FC metrics , that a linear subspace with dimension one or two could explain much of the variability across patterns of FC . On the contrary , the variability across BOLD time-courses could not be reduced to such a small subspace . FC appeared to strongly reflect SC and to be partly governed by a Gaussian process . The main differences between simulated and empirical data related to limitations of DWI-based SC estimation ( and SC itself could then be estimated from FC ) . Above and beyond the limited dynamical range of the BOLD signal itself , measures of FC may offer a degenerate representation of brain interactions , with limited access to the underlying complexity . They feature an invariant common core , reflecting the channel capacity of the network as conditioned by SC , with a limited , though perhaps meaningful residual variability .
The processing and routing of information in the brain are implemented through the interplay of two general constraints: 1 ) the specifics of physiological dynamics , controlling how connected neurons respond to each other locally , and 2 ) the entire wiring diagram of anatomical connections , channeling the possible exchange of information between neuronal ensembles according to the properties of its cables [1–6] . While large-scale anatomical connections are expected to remain relatively stable over time , neuronal processes may be expected to produce complex transient patterns related to different processing phases , either in a stationary or non-stationary manner [7] . Since the same anatomical network may be used to implement variable physiological dynamics at different times [5 , 8 , 9] , many degrees of freedom could be expected in the relationship between structure and function , that is , between the wiring diagram and the resulting patterns of activity [5 , 10–12] . In the context of neuroimaging , the wiring diagram of connections , or ‘structural connectivity’ ( SC ) , has generally been estimated using DWI-based tractography [13–15] . In blood oxygen level dependent ( BOLD ) resting-state functional magnetic resonance imaging ( rs-fMRI , where subjects passively lie in the magnet [16 , 17] ) , measures of ‘functional connectivity’ ( FC ) have emerged as a convenient proxy to quantify functional patterns of interaction between neural ensembles [16 , 18–21] . In this scientific field , standard Pearson correlation has become a gold standard [22–24]; more elaborate measures , though rarely applied so far , have also been proposed , such as mutual information as well as estimators of higher-order relationships [25–30] . If FC were sensitive to features of dynamics related to ongoing cognition , we could expect a relatively large variability and complexity of this measure , in particular as compared to SC . In the resting state , for instance , cognition is allowed to roam free range , and FC could thus be expected to be quite variable across arbitrary acquisition times , both within and between subjects . Beyond theoretical rationale , empirical evidence displays mixed results making FC hard to assess as a measure . Several studies support the hypothesis that FC can express a variety of patterns and complex dynamical behaviors , which can demonstrate empirical correlations with normal and pathological cognitive processes [31] . As a matter of fact , it has already been shown that FC could be sensitive to brain maturity [32] , age [33–38] , the global level of awareness [8 , 39] , personality traits [40 , 41] , the current mental state [31 , 42 , 43] , recent experience [44] , and varies over time for a given subject [8 , 39] . It has even been suggested that the between-subject differences could serve as individual fingerprints [45 , 46] . Likewise , the variability of FC patterns across time , as observed with sliding time windows shorter than a standard acquisition run ( ‘dynamic FC’ ) has been recently featured with relation to cognition [47] . Yet , the origins of such differences and variations , whether structural , neuronal , hemodynamic/metabolic , or methodological , and their relative weights in the generation of FC—and thus the potential of FC as a measure—remain to be clarified [38 , 48–51] . Besides , many other studies have suggested that FC reflects SC [22–24 , 52–54] and patterns of FC extracted from rs-fMRI have been repeatedly found to be reproducible both within and across subjects [55–57] . Even though robust FC patterns can be related to general task-based networks from activation studies [58–64] , overall these results suggested that the sensitivity of both the BOLD signal and the different measures of FC to the variability and complexity of the underlying dynamics might be quite limited [65] . The fact that FC is related to SC ( which is relatively reproducible across subjects and stable over time—at least as seen by DWI tractography [66] ) , suggests a FC that would itself reflect the wiring diagram expressed in SC . Evidence for FC patterns that are both reproducible across subjects and sensitive to various subject-specific factors suggests that FC as usually measured might be composed of an invariant core that accounts for a large part of the variance , and a residual variable part . FC might thus have existing but limited sensitivity to important features of brain dynamics , while being strongly dependent on more invariant factors of interaction such as SC , its communication channel capacity and global steady routing schemes . FC would therefore appear as a degenerate measure of the expected complexity of brain interactions , all the more so that it would reflect SC , which , as a measure of static anatomical wiring diagram , implies the highest degeneracy with respect to brain dynamics [11] . Likewise , it remains unclear how this could depend on the kind of measure used to quantify FC or the time window considered . Addressing these questions is important as they concern the potential of FC for capturing relevant aspects of the complexity of brain dynamics and our ability to infer meaningful neurocomputational properties from such measures . In the present study , we characterized and compared qualitatively and quantitatively the relative variability of patterns of SC and FC , across different measures of FC ( correlation , mutual information , 3-way connectivity , dynamic FC ) . We assessed: 1 ) the relative sensitivity of FC to variable states and dynamics as putatively underlying different phases and modes of brain activity , and 2 ) the extent to which FC reflects SC . We used DWI-based estimates of SC and rs-fMRI based estimates of FC from 21 normal participants over a partition of the brain into 160 regions [54] . In addition to the analysis of empirical data , in order to systematically assess the robustness of FC to different types of dynamics , we performed simulations of FC using seven different mainstream computational models of neuronal activity and BOLD response , with a wide range of complexity [54 , 67 , 68] . We used a unified analysis framework based on singular value decomposition ( SVD ) of matrices of connectivity and bootstrap statistics in order to compare patterns of connectivity between and within subjects and assess their variability in a multivariate framework . For each connectivity pattern under investigation , this method extracted a reproducible linear subspace with limited dimension that had the key feature of explaining the most variance or , equivalently , best explaining the data in a least square sense for that dimension ( see Fig 1 and Methods ) .
We then adopted an approach based on computational modeling as a heuristic tool to study the sensitivity of FC to the specificity of the dynamical behaviors controlling interactions between connected regions . We generated simulated FC based on an array of standard computational models , all taking SC as input but with broadly different dynamical behaviors and equations . We wondered whether the patterns of FC generated by the different models would be able to fit empirical FC in the same manner or would retain irreducible components proper to the specific properties of the models ( see Methods ) . We then assessed whether pooled empirical and simulated FC across subjects would project onto the same small number of dimensions by performing a global bootstrap SVD analysis . We found a reproducible linear subspace with dimension 2 ( 63 . 7% ± 1 . 6% of variance explained for cFC and 56 . 0% ± 0 . 5% for mFC ) ( Fig 4a ) . In both cases , the first dimension was tightly related to intrahemispheric connections , while the second dimension largely reflected homotopic and , more largely , interhemispheric connections ( Fig 4b and 4c ) . For both cFC and mFC , the first dimension appeared again tightly related to SC , but not the second one . Finally , it was mostly empirical FC ( which , contrary to simulations , demonstrated clear patterns of interhemispheric connections ) that loaded onto the second dimension ( Fig 4d ) . We reasoned that customary errors of DWI-based SC estimation , concerning more particularly homotopic and interhemispheric connections , could account for the appearance of a second linear dimension when adding empirical FC to simulated FC . Homotopic and interhemispheric connections are known to exist but to be difficult to measure , remaining largely undetected by standard DWI acquisition and processing pipelines [54 , 69 , 70] . Such connections were mostly absent of the SC dataset that we used as an input to the generative models . First , we investigated residual cFC by computing the difference between empirical and simulated FC for each generative model and subject . A bootstrap SVD analysis yielded a reproducible linear subspace with dimension 1 ( 47 . 9% ± 1 . 7% of explained variance ) . This dimension saliently featured interhemispheric and , more specifically , homotopic connections ( Fig 5a ) . Thus a substantial amount of the differences between empirical and simulated FC could be reduced to errors pertaining to homotopic connections . Second , we performed an SVD analysis in which simulated and empirical cFC were pooled together but on intrahemispheric connections only , as a way of excluding putative errors of prediction directly and explicitly related to interhemispheric connections . This analysis yielded a reproducible linear subspace with dimension 2 . Nonetheless , compared to bootstrap SVD over all ( intra- and interhemispheric ) connections , the part of variance accounted for by the first dimension ( corresponding to the dimension embedding the variability of generative models ) increased from 51 . 5% ± 1 . 2% to 63 . 1% ± 1 . 0% , while the part of variance accounted for by the second dimension ( which corresponds to the dimension hypothesized to bear the functional consequences of SC estimation errors ) decreased from 12 . 2% ± 0 . 6% to 7 . 0% ± 0 . 4% . Even though we excluded interhemispheric data from the SVD analysis , this approach was not fully satisfactory , since simulated FC was generated to begin with by computational models taking interhemispheric information from SC into account . Thus we might expect that intrahemispheric simulated FC could also suffer from SC estimation errors concerning interhemispheric and homotopic connections . In order to further tackle this issue in a heuristic manner , we performed a new set of simulations and analyses restricted to SC and cFC averaged across subjects , as we could not afford an analysis on individual data at this stage given the heavy computational burden of the simulations [68] . This precluded further bootstrap analysis and thus made the results only descriptive , but we had a directed hypothesis and were only looking for confirming the impact of failing to detect homotopic connections on the observed discrepancies between empirical and simulated FC . We manipulated the average SC by artificially setting homotopic connections to an arbitrary value ( 0 . 5 ) . Two new batches of simulations across all seven computational models were run in order to generate simulated matrices of FC as predicted from the original average SC matrix on the one hand and , on the other hand , from the modified average SC matrix . We hypothesized that adding homotopic connections would increase the variance explained by the first linear dimension and decrease that of the second one in a standard SVD analysis . We observed that adding homotopic connections increased the part of variance explained by the first dimension from 67 . 2% to 78 . 4% , while it decreased the part of variance explained by the second dimension from 13 . 3% to 7 . 7% ( Fig 5b ) . Altogether this series of results supports the hypothesis that , independently from unmodeled differences that would be related to the complexity of the dynamics being captured , an incorrect estimation of SC , which is itself related to current limitation in DWI , plays an important role in the observed discrepancies between FC predicted by the different generative models and empirical FC . They also further support the hypothesis of a strong relationships between FC as measured and SC . In all the analyses so far , we computed FC over a whole rs-fMRI session , that is , over time series of about 11 minutes . We hypothesized that in resting state data acquired at arbitrary time points across different subjects , brain computation and dynamics would be maximally independent and that , consequently , such sampling strategy would foster the expression of putative differences in patterns related to this free state , and minimize the impact of more invariant constraints . However , it is possible that computing FC over time series of several minutes tends to bias the sensitivity of the analysis toward the most stationary aspects of the processes , thus missing the relevant variability because of the wrong choice of time scale . We thus conducted a bootstrap SVD analysis , concentrating on empirical data and using cFC , but this time on concatenated matrices of cFC independently computed over shorter time windows . Bootstrap SVD extracted a reproducible linear subspace with dimension 1 for all window sizes ( from 32 . 9 s to 5 min 29 s ) but the shortest ( 26 . 32 s ) , where no reproducible linear subspace could be extracted . The part of variance explained by the first dimension decreased however from 52 . 6% ± 1 . 6% for 2 windows of size 5 min 29 s to 21 . 5% ± 2 . 0% for 20 windows of size 32 . 9 s and 19 . 8% ± 2 . 4% for 25 windows of size 26 . 32 s ( Fig 6a ) . This is to be compared to the 59 . 8% ± 1 . 6% of total variance explained by the analysis with cFC computed over the whole session . Dynamic FC featured a reproducible linear dimension that was similar to that of cFC , with a residual variance that increased as the window size decreased ( Fig 6b and 6c ) . One of the findings of these analyses is that FC from rs-fMRI , as measured with correlation , mutual information , or 3-way connectivity , contained a degenerate projection of the complexity and variability of the dynamics underlying brain computation , which reflects a robust and stable common core . This core of FC also appeared to be clearly related to SC , both considering empirical data and simulations from generative models in which the relation is explicit . We reasoned that the empirical relationship between SC and FC ( as measures ) suggests that their common features reflect the same underlying anatomical network . Given the customary issues of false negatives and false positives in standard DWI pipelines , measurements of FC from rs-fMRI might thus appear as a fair additional ground for ( re ) estimating SC in a more encompassing way . While developing and testing a valid and full-fledged procedure for SC estimation through inversion of FC is beyond the scope of this article , we here give a proof of concept in favor of the possibility of such a procedure . The procedure relies on two main assumptions: ( i ) There is a simple , one-to-one relationship between SC and FC which is relatively insensitive to dynamical regimes ( and that loads on the first component of the SVD ) ; and ( ii ) deviation from this relationship ( at the level of this first component ) is mostly a consequence of incorrect estimation of SC . According to ( i ) , one could quantify the relationship between SC and FC from simulations and then apply its inverse to empirical FC . More specifically , from the analyses , we were led to the conclusion that SVD on the pooled data extracted a first component that is a reflection of the underlying steady functional-anatomical organization , and a second one that is related to errors in SC estimation . We reasoned that , should DWI provide correct SC , the second component of SVD on the pooled data would be associated to vanishing variance , while the fraction of variance explained by the first component would increase to 1 , and the relationship between SC and FC would appear on this component . As a consequence , the empirical data would mostly project on the first component . We then also postulated the relative stability of the relationship between SC and FC regardless of the quality of estimation of SC by DWI . In other words , a better estimation of SC would reduce the fraction of variance explained by the second component and increase the fraction of variance explained by the first component but not drastically change the relationship already observed between SC and FC on the first component . Practically , we first quantified the relationship observed between the first reproducible linear dimension of empirical SC and that of the pooled data ( Fig 7a ) , then applied the inverse of this transform to empirical FC ( see Methods ) . Such an approach produced an estimated matrix of SC that was similar to , but richer than , the original DWI-based SC matrix , featuring more interhemispheric connections , especially homotopic ones ( Fig 7b and 7c ) . Beyond this preliminary example , a Bayesian iterative scheme could be developed in the future , embedding a similar approach , and aiming at converging towards optimal estimates of SC .
From a theoretical standpoint , the existence of a low-dimensional approximation of FC from SVD implies that strong linear relationships control FC variability , whereas approximations of significantly higher dimension ( or no approximation at all ) would imply more complex relationships . Our findings suggest that , to the least , FC as measured by these different metrics from rs-fMRI is a strongly degenerate representation of the potential complexity of underlying interactions . This relative degeneracy of FC was not simply attributable to the BOLD signal , whose SVD did not feature any main linear directions , but was a direct effect of measuring FC . Previous studies emphasized that a broad range of computational models generating time courses with quite different spectra and oscillatory properties featured limited yet similar overall predictive powers of simulated FC [54 , 68]; such results also suggest that FC is degenerate with respect to dynamical regimes . It remains that part of the variance was not reducible to this main linear dimension to an extent that depended on the metrics used to compute FC: according to SVD analyses , mutual information tended to explain less variance along the first linear dimension than correlation , and could also feature a second reproducible linear dimension . Likewise , even though the main linear dimension was still reproducibly present when reducing the time window of analysis for the computation of FC down to about 30 seconds , it explained less and less variance . It is difficult to assess how much this additional unexplained variance reflect increasingly worse performance in estimating FC or meaningful changes that reflect brain computation [31 , 71] . Degrees of freedom between SC and FC could be expected , as adaptive brain computation implies dynamic interactions for the routing and integration of information . However , at the level of our measurement space , such degrees of freedom appeared reduced to a main linear dimension relating all observations . Subjects’ cognition may be free ranging in the resting state , but this did not manifest as irreducibly complex differences between independently generated patterns of FC . BOLD-related FC , in particular along 5–10 minute runs , appeared fundamentally dominated by SC , which itself appears dominated by one main linear dimension , explaining up to 86% of the variance across subjects . Altogether this emphasizes that anatomical connections of brain structures themselves must have a strong backbone that is invariant across subjects and that drives a relatively invariant net pattern of bidirectional information transfer . This pattern is reflected in FC and implies an overall bandwidth across communication channels that is a function of the quantity of fibers ( empirically estimated by DWI-SC ) . By manipulating SC , [54] demonstrated the importance of the integrity of SC and of the accuracy of its measurement on the predictive power of generative models for FC . Here , beyond predictive power , we further showed that the main differences between the patterns of simulated and empirical FC largely loaded on connections that were known to be poorly estimated in DWI tractography and that were absent in our SC matrices . Therefore the main factor limiting the predictive power of generative models might turn out to largely reduce to problems of estimation of SC ( when we artificially added homotopic connections in our SC data , the variance explained by the first linear dimension increased from 67% to 78% ) . Improving tractography is therefore expected to greatly improve the fit of simulated FC to empirical FC [69 , 70] . Now , in spite of their strong relationships , there are deeper systematic differences between SC and FC that need to be emphasized . FC as measured is the image of ( linear or nonlinear ) correlations among time-courses that are generated by an underlying dynamical process ( either simulated or empirical ) . While being nominally channeled by SC , FC is ultimately induced by an underlying hidden weighting of SC , sometimes called ‘effective connectivity’ ( EC ) [72 , 73] . This weighting of connections operates as multiplicative gains ( which can be positive or negative ) in the local transfer functions between connected regions , which themselves drive the overall dynamical process . Obviously , estimates of the specific weighting of EC cannot be simply inferred from DWI-based measurements of SC . However , based on the relationships demonstrated herein , procedures of inversion of FC , as envisioned and preliminarily explored above , could yield fair estimates of EC itself , which would be of great interest for neurocomputational modeling and inference . Likewise , BOLD-related measures of FC could provide , after adequate inversion , the basis for novel multimodal approaches to tractography , supporting inference on existing fibers in the context of ambiguous diffusion signal based on EC or SC estimates from FC . In the introduction , we referred to two bodies of literature , one showing the relative reproducibility of FC between subjects , and the other one showing that FC could be influenced by a wealth of factors , such as brain maturity , age , the global level of awareness , personality traits , the current mental state , recent experience , and time . In the present study , we found that the information contained in FC was rather degenerate and in a large part reflective of SC . Together with the fact that SC ( as seen by DWI tractography ) is rather reproducible across subjects , this is in agreement with studies showing the reproducibility of FC . As to the relative influence of the two main ( anatomical and dynamical ) factors on FC , our study hints that SC strongly dominated the signal and loaded on the first reproducible SVD component . Dynamical functional patterns ( and noise ) might load on the remaining components , which are not associated with reproducible patterns . The effect of some factors which cannot easily be associated with a modification of structural connections but are most likely related to a change in dynamical regime ( e . g . , the global level of awareness , the current mental state ) , are probably observed on the part that is not accounted for by the first reproducible SVD component . This distinction could be used to classify data accordingly , even though some factors might influence both structure and dynamics . A future study could apply the framework developed in the present manuscript to experimental settings where a variation of FC is observed and quantified concomitantly with a change in state ( e . g . , sleep or loss of consciousness [8 , 74] ) , behavior , or cognition , and investigate how such change would affect the various singular values from the SVD and corresponding reproducible patterns of FC . In any case , an important point is that the range of variability available to FC is quite reduced compared to the same range at the level of neuronal processes or even at the level of the BOLD signal . Among remaining questions for future studies , one may wonder whether the degeneracy of FC would be as strong with input signals offering more sensitivity to more complex and potentially faster dynamics than the slow and indirect BOLD signal , and how much it relates to the spatial scale of observation . While we showed that increasing the parcellation scale from 160 to 461 to 825 regions essentially led to similar conclusions in terms of degeneracy of FC , a more drastic change in scale together with a change in imaging method might provide complementary insight into the origin and function of brain interactions [75] . The same methodological framework could be applied to electrophysiological measurements and computational models directly , though measuring the required empirical data for the analysis at the scale of the matrix of SC could be challenging . We may also wonder if the brain’s ability to re-route information flow after brain damage , a process that might underlie mechanisms of functional recovery and be involved in resilience to neurological disorders , could not be related to a loosening of the relationship between SC and FC [5] .
All datasets previously mentioned were analyzed in a similar fashion using singular value decomposition ( SVD ) . For all but Dataset #9 , we applied bootstrap SVD . In order to infer SC from empirical FC , we first quantified the relationship observed between the average projection of empirical SC onto its first left-singular vector and the average projection of all FC onto its first left-singular vector . Empirical observations suggested to consider only values of SC ≥10−6 and to apply the following transformation: SC ′ = ln SC 0 . 0001 . We then approximated the relation between SC’ and FC through 4-order polynomial interpolation , FC ≈ P4 ( SC′ ) . Estimation of SC through inversion of the relationship between SC and FC was then carried out using the relationship SC ^ = 0 . 001 exp P 4 - 1 ( FC ) . | The human brain is characterized by both the way its neurons are connected ( the anatomy ) and the way they emit signals to interact ( the dynamics ) . At the typical scale of measurements , by analogy with the road network , anatomy ( the ‘roads’ ) is expected to remain relatively stable over time and reproducible from person to person , while neuronal dynamics propagating in the network ( the ‘traffic’ ) can be expected to vary widely , both over time and between people . But , paradoxically , patterns of functional connectivity ( FC ) , a convenient proxy to quantify interactions between pairs of brain regions in magnetic resonance imaging ( MRI ) , have been found to be reproducible both within and between subjects across several studies , while other studies , on the contrary , have featured variable FC patterns , often in relation to cognitive processes . We investigate this unsettled issue quantitatively using multivariate statistics and generative models of brain activity . We show , across a range of estimators , that FC offers a degenerate representation of brain interactions and strongly depends on anatomy . | [
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] | 2016 | Functional Connectivity’s Degenerate View of Brain Computation |
Amebiasis is a protozoal infection caused by Entamoeba histolytica , while the morphologically indistinguishable E . dispar is considered as non-pathogenic . Polymerase chain reaction ( PCR ) assays are necessary to differentiate both species . The most common clinical presentations of E . histolytica disease are amebic colitis and amebic liver abscess , but asymptomatic infection is also possible . We assessed the frequency and pattern of clinical symptoms and microscopic features in travelers/migrants associated with E . histolytica intestinal infection and compared them to those found in individuals with E . dispar infection . We conducted a retrospective study at the travel clinic of the Institute of Tropical Medicine , Antwerp , Belgium on travelers/migrants found from 2006 to 2016 positive for Entamoeba histolytica/dispar through antigen detection and/or through microscopy confirmed by PCR . All files of individuals with a positive PCR for E . histolytica ( = cases ) and a random selection of an equal number of Entamoeba dispar carriers ( = controls ) were reviewed . We calculated the sensitivity , specificity and likelihood ratios ( LRs ) of clinical symptoms ( blood in stool , mucus in stool , watery diarrhea , abdominal cramps , fever or any of these 5 symptoms ) and of microscopic features ( presence of trophozoites in direct and in sodium acetate-acetic acid-formalin ( SAF ) -fixed stool smears ) to discriminate between E . histolytica and E . dispar infection . Of all stool samples positive for Entamoeba histolytica/dispar for which PCR was performed ( n = 810 ) , 30 ( 3 . 7% ) were true E . histolytica infections , of which 39% were asymptomatic . Sensitivity , specificity and positive LRs were 30% , 100% and 300 ( p 0 . 007 ) for presence of blood in stool; 22% , 100% and 222 ( p 0 . 03 ) for mucus in stool; 44% , 90% and 4 . 7 ( p 0 . 009 ) for cramps and 14% , 97% and 4 . 8 ( p = 0 . 02 ) for trophozoites in direct smears . For watery diarrhea , fever and for trophozoites in SAF fixated smears results were non-significant . E . histolytica infection was demonstrated in a small proportion of travelers/migrants with evidence of Entamoeba histolytica/dispar infection . In this group , history of blood and mucus in stool and cramps had good to strong confirming power ( LR+ ) for actual E . histolytica infection . Trophozoites were also predictive for true E . histolytica infection but in direct smears only .
Amebiasis is a protozoal infection caused by Entamoeba histolytica . The most common clinical presentations of disease are amebic colitis and amebic liver abscess . Before molecular tests allowed distinction between Entamoeba species[1] , [2] , the estimations of the worldwide burden of amoebiasis indicated that approximately 500 million people were infected by E . histolytica , and 10% of these individuals had invasive amoebiasis . Moreover , it was estimated that 100 , 000 patients per year died due to the clinical complications of the disease[3] . The genus Entamoeba contains many species of which Entamoeba histolytica , Entamoeba dispar , Entamoeba coli , Entamoeba hartmanni , and to a much lesser extent Entamoeba moshkovskii and Entamoeba polecki , are found in the human intestinal tract . Cysts of E . histolytica , E . dispar , and E . moshkovskii are morphologically indistinguishable[4] , [5] , [6] but the species are biochemically and genetically different[7] . Towards the end of the 20th century , Polymerase Chain Reaction ( PCR ) -assays that allowed to differentiate between E . histolytica and E . dispar infection led to a re-assessment of the disease burden and indicate that earlier reports had largely overestimated the true number of E . histolytica infections . More recent reports showed in addition varied frequencies of asymptomatic E . histolytica carriage in different populations , ranging from 0–2% in South-Africa and Ivory Coast to 21% in Egypt , with intermediate prevalence of 13 . 8% reported in rural Mexico and 9 . 6% in Vietnam[8] , [9] , [10] , [11] . In studies dating from before PCR could discriminate between E . histolytica and E . dispar infection , a 4% prevalence of asymptomatic E . histolytica/dispar infection was found in travelers returning from the tropics[12] . Notwithstanding , the ratio of symptomatic vs asymptomatic E . histolytica infections remains largely unknown . Though E . dispar is considered non-pathogenic , it has been reported that E . dispar may be the causative agent of intestinal and extra-intestinal symptoms in humans[13] , [14] . The finding of trophozoites ( or vegetative forms ) in fresh stool samples is generally considered predictive of true E . histolytica infection , especially when large trophozoites containing red blood cells are found ( hematophagy ) [15] , [16] , [17] , but it is not known whether the presence of trophozoites found after fixation of stools differs between E . histolytica and E . dispar . In the present work , we aimed to determine the frequency of E . histolytica infection among travelers and migrants presenting with an Entamoeba histolytica/dispar infection diagnosed by microcopy and/or antigen detection at the travel clinic of the Institute of Tropical Medicine of Antwerp , Belgium . In addition , we assessed the predictive value of microscopic features and clinical symptoms for E . histolytica intestinal infection in this study group and correlated the finding of trophozoites in fresh and fixed stool samples with species identification .
The Institute of Tropical Medicine , Antwerp ( ITMA ) is the national reference clinic for tropical medicine in Belgium , with on average about 6500 consultations a year for post-travel care . For this retrospective study , all files of symptomatic and asymptomatic individuals having attended the travel clinic of the ITMA from May 2006 to March 2016 and positive for Entamoeba histolytica/dispar through antigen detection and/or through microscopy ( trophozoites or cysts ) confirmed by PCR , were retrieved . The medical records of all travelers and migrants proven to be infected with E . histolytica during the study period were then reviewed . An equal number of files of patients with confirmed E . dispar intestinal infection were randomly chosen and analyzed for a case control comparison . Relevant clinical and laboratory data were extracted , de-identified and entered in a Microsoft Access 2010 database . Variables included: demographic data including country of origin , month and year of first Entamoeba positive test , most recent travel destination and , for the symptomatic included cases and controls , the following clinical features at presentation: blood in stool , mucus in stool , watery diarrhea , abdominal cramps and fever , as reported in the medical files . All stool samples were analyzed by microscopic examination of direct smears and wet mounts after formalin-ether concentration ( Loughlin and Spitz , 1949[18] ) . A limited number of samples with high suspicion for amebic dysentery was urgently sent to the lab for immediate examination . In case a fresh stool sample could not be produced in ITMA , the patient received a package to collect stools at home and instructions to mix part of the stools immediately with a sodium acetate-acetic acid-formalin ( SAF ) solution . Both fixed and unfixed portions were sent to ITMA for examination . In case the stool sample was produced at ITMA , part of it was mixed with SAF-solution within 20 minutes on request by the treating physician . All SAF-fixed stool samples were examined by microscopy after iron hematoxylin Kinyoun staining . Antigen detection with the enzyme-linked immunosorbent assay ( ELISA ) E . histolytica ProSpecT ELISA Microplate assay ( Remel , Lenexa , Kansas , USA ) , was performed when requested by the treating physician . Since microscopic distinction of E . histolytica , E . dispar and some other Entamoeba species is not possible , an E . histolytica and E . dispar specific real-time PCR ( Cnops and Van Esbroeck , 2010[19] ) was performed on all samples positive by microscopy and/or antigen detection . Direct smears were examined for the presence of hematophagy . In SAF-fixed stool this feature cannot be used , given possible superposition of erythrocytes over parasites , instead of within parasites . Among individuals found with E . histolytica/dispar intestinal infection , we analyzed the respective frequencies of the presence of E . histolytica and E . dispar trophozoites and cysts as well as the pattern of clinical findings ( blood and/or mucus in stool , watery diarrhea , presence of abdominal cramps , fever or any symptom ) . Sensitivity , specificity and likelihood ratios ( LRs ) were calculated , using the PCR as reference diagnostic standard . Finally , we assessed whether hematophagy can be used as a criterium to distinguish E . histolytica and E . dispar species in direct stool smears . Laboratory test results were stored in the Laboratory Information System AS/400 ( IBM , USA ) . Data mining was performed with the SAP Business Objects ( SAP , USA ) program . Statistical analyses were done with Epi-Info ( CDC 2015 ) . Dichotomic variables where compared with Fisher exact test , minimum significance p<0 . 05 . This was a retrospective analysis of data collected during clinical care over an 11-year period . Ethical clearance was obtained from the institutional review board at ITMA . Laboratory queries were obtained in an anonymous way . Clinical data were then retrieved through an encoded link and de-identified for analysis according to the Belgian legislation .
From May 2006 till March 2016 parasitological examination was performed on 40 , 638 stool samples . Of these 868 ( 2 . 1% ) were found positive for Entamoeba histolytica/dispar through antigen detection and/or through microscopy confirmed by PCR . After removing results of follow-up samples , E . histolytica was detected in 30/826 samples: 3 . 6% of all stool samples positive for E . histolytica/dispar and 0 . 07% of all examined stool samples . E . dispar was detected in 714 ( 86 . 4% ) samples , neither E . histolytica nor E . dispar in 50 , and PCR was technically not feasible in 16 because no fresh stool sample was received . No co-infections with E . histolytica and E . dispar were found . Antigen detection was performed in 396 of the 744 samples with E . histolytica or E . dispar as confirmed by PCR . In 16 samples , the antigen test was positive , with negative PCR for E . histolytica or E . dispar and negative microscopy ( or microscopy not done ) , while in 1 E . histolytica PCR-confirmed patient antigen testing was positive with negative microscopy . The antigen test was positive in 15/16 ( 94% ) E . histolytica positive and 275/380 ( 72% ) E . dispar positive samples . When only examination of direct smears was considered , the finding of trophozoites was predictive of E . histolytica ( p = 0 . 02 ) , although sensitivity was very low ( 14% ) ( Table 3 ) In contrast , the finding of trophozoites in fixed samples was not predictive of E . histolytica ( p = 0 . 2; Table 4 ) .
In our Belgian reference clinic for tropical medicine we identified 3 . 6% ( 30/826 ) of Entamoeba histolytica/dispar infections as true E . histolytica infections by PCR . This confirms the finding in other studies[20] , [21] that the bulk of Entamoeba histolytica/dispar infections are caused by E . dispar amoeba . True E . histolytica enteritis is a rare finding in patients presenting in our reference center , with on average less than 3 cases detected per year . In our study , the presence of blood or mucus in stool or abdominal cramps are clearly significant predictors ( p < 0 . 005 ) of true E . histolytica infections in case Entamoeba histolytica/dispar cysts or trophozoites were found on microscopy . Likelihood ratios of symptoms can be used similarly to test results to calculate the probability of disease according to the Bayes theorem . Since the positive LR is the ratio between true positive and false positive rates , a symptom , even if infrequent in a given disease , can have a high LR+ ( a high confirming power ) if it is rarer in the competing[22] . Indeed we observed that blood or mucus in stool or abdominal cramps were not that frequent in true E . histolytica infections , but that these symptoms were almost never present in the matched patients with E . dispar , which explains the high LR+ . Therefore , in a context where only microscopy is available , a patient presenting with blood or mucus in stool or cramps should anyhow be treated as amoebiasis if Entamoeba histolytica/dispar cysts/trophozoites are found . Nevertheless it is also worth noting that a sizeable proportion of E . histolytica cases were asymptomatic . Relying only on one of the three clinical predictors would have missed 10 true E . histolytica infections in our cohort . Hematophagy is considered a discriminative microscopic criterion to distinguish E . histolytica from E . dispar infection[15] , [16] , [17] . This was also demonstrated in this study in which 5/5 hematophagous trophozoites found in immediately examined samples proved to be E . histolytica . Finding trophozoites in direct smears had a LR+ for E . histolytica of 4 . 8 , corresponding to a good confirming power . However , the LR- of 0 . 9 indicated that the absence of trophozoites , did not rule out E . histolytica infection . The non-significant LR+ of 1 . 2 for trophozoites in SAF fixed stool samples confirmed that this method cannot be used for species prediction . The non-pathogenicity of Entamoeba dispar is questioned by several authors[23] , [14] . A study by Ximénez and colleagues suggests the existence of several different genotypes of E . dispar that can be associated to , or be potentiality responsible for , intestinal or liver tissue damage , similar to that observed with E . histolytica[13] . The difference in percentage of patients presenting with any symptom in patients with mono-infections with E . histolytica vs E . dispar was not significant ( 61% vs 55% , p value 0 . 42 ) . This is not equivalent to stating that all symptoms of the 55% patients with symptomatic E . dispar infections were attributable to the E . dispar amoebae . Our study was not designed to show a pathogenic effect of E . dispar . However , the high frequency of symptoms in patients with E . dispar mono-infection supports Ximénez’s hypothesis , but symptoms in E . histolytica infected patients were clearly more often suggestive of intestinal tissue invasion . Our study has several limitations . It was a single-center study and the total number of E . histolytica infections found might not be representative for all returning travelers . In patients consulting at our center , we found 30 E . histolytica infections over 10 years , whereas the total number of E . histolytica infections diagnosed in our laboratory receiving stool samples from all over Belgium was 124 over the same period . Next , it was a retrospective study meaning that collection of data was not systematic . However , given the low number of confirmed E . histolytica infections in the 810 samples tested by PCR , the impact of missing analyses is likely marginal . In 50 samples positive by microscopy PCR was negative for both E . histolytica and E . dispar which probably indicates incorrect identification as infections with species such as E . moshkovskii and E . polecki are considered to be rare . A difference in clinical presentation in patients with E . histolytica and E . dispar infection is a possible confounding factor since clinicians might have asked less stool samples in asymptomatic patients . This might have underestimated the true prevalence of these infections . Nevertheless , the proportion of asymptomatic patients in our case-control group did not differ significantly . Furthermore , requesting stool analysis including antigen testing was clinician driven and an unknown number of E . histolytica/dispar infections may have been missed , in particular in asymptomatic travelers . The most trustworthy method to detect all E . histolytica and E . dispar infections , would have been to perform PCR on all stool samples of all symptomatic and asymptomatic travelers[7] , [24] . During the study period , this method was not part of common practice , though this may change with the deployment of multiplex PCR platforms to analyze stool samples . Last , quantification of pathogens is usually linked with disease severity , which is mostly demonstrated for bacterial diseases[25] . We opted however to correlate our symptoms to the qualitative and not the quantitative interpretation of the PCR results because the goal of our study was identification of E . histolytica as such–which is treated even in asymptomatic patients–and not determination of pathogenicity .
In conclusion , even in a national reference travel clinic in Europe , E . histolytica intestinal infections are rarely diagnosed . Finding trophozoites is helpful in discriminating between E . histolytica and E . dispar infection in direct smears but not in SAF fixed samples . History of blood and mucus in stool and cramps in individuals with microscopic evidence of E . histolytica/dispar infection had good to strong predictive weights for actual E . histolytica infection . Hematophagy was a very rare finding but in our experience was always associated , when requested , with E . histolytica infection . Our study suggests that E . dispar might be pathogenic but symptoms in E . histolytica infected patients were clearly more often suggestive of intestinal tissue invasion . | In the present work , we found that E . histolytica intestinal infections are rarely diagnosed among travelers and migrants presenting in a national reference travel clinic in Europe . Microscopic finding of cysts or trophozoites and antigen testing cannot discriminate between Entamoeba histolytica/dispar infection , which leads to overdiagnosis of E . histolytica infections in low resource settings where PCR is not available . We found visualization of trophozoites under the microscope helpful in discriminating between E . histolytica and E . dispar infection in direct smears . Hematophagy is a very rare finding but in our experience was always associated with E . histolytica infection . In a context where only microscopy is available , a patient presenting with blood or mucus in stool or cramps should anyhow be treated as amoebiasis if Entamoeba histolytica/dispar cysts/trophozoites are found . Nevertheless it is worth noting that a sizeable proportion of E . histolytica cases were asymptomatic . Last , our study suggests that E . dispar might be pathogenic but symptoms in E . histolytica infected patients were clearly more often suggestive of intestinal tissue invasion . | [
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] | 2018 | Clinical and microscopic predictors of Entamoeba histolytica intestinal infection in travelers and migrants diagnosed with Entamoeba histolytica/dispar infection |
Most organisms live in ever-changing environments , and have to cope with a range of different conditions . Often , the set of biological traits that are needed to grow , reproduce , and survive varies between conditions . As a consequence , organisms have evolved sensory systems to detect environmental signals , and to modify the expression of biological traits in response . However , there are limits to the ability of such plastic responses to cope with changing environments . Sometimes , environmental shifts might occur suddenly , and without preceding signals , so that organisms might not have time to react . Other times , signals might be unreliable , causing organisms to prepare themselves for changes that then do not occur . Here , we focus on such unreliable signals that indicate the onset of adverse conditions . We use analytical and individual-based models to investigate the evolution of simple rules that organisms use to decide whether or not to switch to a protective state . We find evolutionary transitions towards organisms that use a combination of random switching and switching in response to the signal . We also observe that , in spatially heterogeneous environments , selection on the switching strategy depends on the composition of the population , and on population size . These results are in line with recent experiments that showed that many unicellular organisms can attain different phenotypic states in a probabilistic manner , and lead to testable predictions about how this could help organisms cope with unreliable signals .
Most organisms , from bacteria to multicellular eukaryotes , have sensory systems that allow measuring environmental cues , and responding to these cues by adjusting gene expression and modifying patterns of development and growth [1] , [2] . The ability to modify the phenotype in response to signals can increase survival and reproduction in variable environments [3] . One important and well-studied example is the response to stressful conditions ( reviewed in [4] , [5] ) . The concept of stress response is based on the idea that organisms can express protective features that allow them to survive adverse conditions , and that the expression of these features comes at a metabolic cost [6] . Due to such cost , it is usually assumed that organisms express these features only in response to signals that indicate stress . Examples for such stresses , and organismal responses to these stresses , include nutrient starvation in bacteria , which can induce the expression of alternative metabolic pathways or sporulation [7] , [8] , or antibiotic stress , which can be counteracted by keeping a part of a bacterial population dormant and thus insensitive to the antimicrobial [9] . Here , we are interested in the evolution of stress responses under conditions where organisms are faced with unreliable environmental signals; a situation where episodes of stress are usually preceded by a cue to which organisms can react – but in some cases , signals are not followed by stress , or stress is not preceded by a signal . These assumptions are realistic in biological systems: examples for stress without signals could include infections by pathogens , exposure to solar radiation , or rapid translocation from one habitat to another . We assume signals to be low levels of any environmental condition that would , at higher levels , cause stress and impact organismal functioning if not countered by stress response . In such situations , a deterministic response to environmental cues might not be ideal . Organisms that always express protective features in response to signals , and never express them without signals , face two types of problems: they might suffer high metabolic costs by always responding to the signal , even if it is often not followed by stress; and if stress occurs without preceding signal , all individuals are in an unprotected state , and are thus vulnerable to the deleterious effects of stress . A number of previous studies investigated evolutionary responses to uncertain environments [10]–[17] . A common result is that such conditions can lead to the evolution of organisms that express phenotypes probabilistically; an individual can express a set of different phenotypes ( for example protected and unprotected ) , and each phenotype is expressed with a certain probability . These probabilities will typically depend on the genotype of the individual , as well as on the state of the environment . Recent experimental advances have provided a solid basis for the notion of probabilistic phenotypes . A number of experiments with genetically identical microorganisms that live in homogeneous environments have found substantial phenotypic variation between individuals [18]–[20] . In some cases , clonal populations differentiate into two or more discrete groups of phenotypes [5] , [8] , [9] , [21] . The basis of this phenotypic differentiation is usually thought to be stochastic gene expression [18] . Importantly , although individuals with different phenotypes are genetically identical , the propensity to express different phenotypes is genetically encoded , and is thus an evolvable trait [22] . Both theoretical and experimental studies suggest that probabilistic expression of the phenotype can help organisms cope with uncertain environments , in two different ways [10]–[15] , [17] , [23]–[26] . First , consider environments that undergo rapid changes without preceding signals . Such situations can select for genotypes in which each individual expresses an alternative ( for example protected ) phenotype with a low probability , irrespective of environmental cues . As a consequence , some carriers of this genotype are in a state in which they are prepared for new environmental conditions but typically perform worse in the present environment . This strategy is known as bet-hedging and it increases mean reproductive output over time by minimizing its variance , at the cost of a fraction of the individuals always being maladapted [17] , [23] , [27]–[32] . For the remainder of this text , we refer to this strategy as ‘random switching’ . Second , consider a situation where environmental shifts are usually preceded by signals , but these signals are not reliable . Such a situation can lead to the evolution of types that sense signals , and respond to them with a certain probability , rather than deterministically . It has been shown in a game-theoretic model that such a strategy can outcompete random switching [13] . We refer to such sensing-based strategies with probabilistic responses as ‘responsive switching’ . Previous studies have investigated how selection for random or responsive strategies depends on their costs and on the type and timescale of environmental change [12] , [13] , [33] , [34] . A number of relevant studies approached this topic from the perspective of information processing [12] , [35] , [36] . Donaldson-Matasci and colleagues used information theoretic measures to analyze a similar problem , and found that switching probabilities for partially reliable signals evolve to intermediate values [26] . This is in line with previous results , which showed that bet-hedging strategies could be improved by adjusting the probabilities of a phenotypic decision in response to environmental cues [24] , [25] , [37] . Our model looks at the production of phenotypes in response to presence or absence of a signal . This analogous to previous models [24] , [25] , [37] that investigate the production of phenotypes in response to cues that can take on different values , if we treat the presence and absence of signal as two different values of a cue . Here we consider phenotype switching in the absence of environmental signals , and phenotype switching in response to environmental signals as two different , evolvable traits , a situation that we think is realistic in a natural situation: consider , for example , a bacterium expressing a transcription factor at a certain base-line level . This expression might be due to leaky regulation of the gene encoding the transcription factor , and will vary slightly between individual cells in a population as a consequence of stochastic effects of gene expression [18] . In some of those cells it can exceed a threshold value , triggering a positive feedback loop and changing the transcriptional program of this cell . The probability of exceeding this threshold corresponds to the probability of random switching in our model . If there is an environmental cue indicating changing conditions , the bacterial population will sense this . In some individuals , depending on how strongly they sense the signal , expression of the transcription factor will be regulated in response to the cue , and its level will rise above the threshold , resulting in induction of the response . The probability of up regulating the transcription factor in response to the cue is an example for what we call responsive switching in our model . We are interested in two main questions . First , we investigate the simultaneous evolution of random and responsive switching . We are interested in the conditions that favor one over the other strategy , and we analyze how combining the two strategies can help organisms cope with environmental uncertainty . Second , we are interested in how the evolution of random and responsive switching depends on the ecological setting . Specifically , we address the question how selection on the response to unreliable signals can depend on the composition of the population . We are using two theoretical approaches to address these issues . First , we use an analytical model to derive a mathematical expression of the long-term growth rate of a genotype , as a function of random and responsive switching , and of the properties of the environment . We use this approach to analyze the combinations of random and responsive phenotype switching values that maximize the long-term growth rate . We find that , when signals are only partially reliable , genotypes can evolve that use both strategies simultaneously . Second , we use an individual-based approach to assess the impact of the ecological setting on the evolutionary dynamics . We first consider unstructured environments , where the evolutionary outcome is simply dependent on how well different genotypes can match environmental fluctuations , and on how well they balance costs and benefits of entering a protected state . Then , we turn to environments that are divided into patches , and in which the population density is locally regulated in each patch . In these situations , the success of a genotype depends on the strategies of the other individuals in the population . In populations of risk prone individuals , risk averse types benefit , but this benefit vanishes once their numbers increase . This indicates that the evolutionary success of a given type depends on the composition of the population , and that the evolutionary dynamics of bet-hedging depends on the ecological setting .
We first derive an analytical expression for the long-term growth rate of a population in a single habitat ( single patch ) and two habitats ( two patches ) , given parameters of the model defined above: , , , , and . We assume that the switching probabilities and are continuous traits , and ask which combination of these traits maximizes the obtained long-term growth rate . In the case of a single patch , the long-term growth rate is the geometric mean of the growth rates in each of the four environmental states , weighted by the frequency of the four states: ( 3 ) where the weights are given in ( 1 ) . In the case of two patches , we assume that the environmental state in the first patch is independent from the environmental state in the second patch . This results in sixteen combinations of and , where , namely , , , , , . We assume unlimited migration at the end of each time step , and thus full mixing between the two patches . For one time step , the growth rate of a given genotype is the arithmetic mean of the growth rates in each of the two patches . We can thus calculate the mean growth rate of a given type for each of the sixteen environmental states . The long-term growth rate is then given by ( 4 ) For the individual-based approach , we bin the two switching probabilities and into discrete phenotype categories that span the range between 0 and 1 with gradation of . At the start of the simulation , the number of individuals in every bin is drawn from a normal distribution with mean , where is the ( constant ) population size of one patch , and a standard deviation of . At each generation , the environmental state is drawn from a multinomial distribution with the probabilities given in ( 1 ) , and selection , density regulation , migration , and mutation follow . This process is repeated for generations . For presentation of the results , the and values of the bin that contains the highest number of individuals are determined , and those values are averaged over runs .
Our focus is on how organisms evolve to respond to environmental signals that indicate stressful conditions , and how the course of evolution depends on the reliability of the signals . We assume an environment that occurs in two distinct states , benign and stressful . We further assume discrete time steps . During each time step , the environment is in one of the two states; it can change the state during the transition to the next time step . There is a signal that tends to indicate stressful conditions . If there is a signal , it occurs at the beginning of a time step , and organisms can react to the signal during that time step . The signal is not necessarily reliable . Sometimes , signals are not followed by stress; other times , there is no signal , but there is stress . The organisms can also exist in two states , vegetative ( unprotected ) and protected . The vegetative state confers a high fertility in time steps without stress , but a high mortality during time steps with stress . Individuals in the protected state have a lower fertility , but survive stress better . An individual's transition from the vegetative to the protected state is referred to as ‘phenotypic switch’ . Responsive switching occurs in response to the signal , while random switching occurs without signal . Both traits are genetically encoded , and can thus evolve . Each individual has two loci to encode these two traits , and there are an infinite number of alleles at each locus , ranging from 0 ( the organism never switches ) to 1 ( the organisms switches with probability one ) . Our goal here is to investigate how the evolution of these two traits depends on the environmental conditions . We employ two different modeling approaches: an analytical model , to calculate the long-term growth rate of a genotype , and an individual-based approach , to model the evolution of random and responsive switching in heterogenous environments . The results of the analytical model are then compared to the individual-based model , which gives us an idea of the impact of stochasticity as well as population effects on the evolution of phenotypic heterogeneity . ( See Methods for a detailed description of the two models and the parameters used . ) We first examine a situation where stress is always preceded by a signal , but where signals are not necessarily followed by stress . In other words , we assume that the probability of a signal , , exceeds the probability of a stress event , , and that the statistical association between signal and stress , , is maximal ( ) ; this is the case if stress is always preceded by a signal . In this case , there is no benefit of switching in the absence of signal . Random switching ( ) is thus expected to evolve towards zero , and this is indeed what the analytical model shows ( Fig . S1A ) . We thus examine how the reliability of environmental signals affects the evolution of responsive switching , . We do not vary the reliability of signals by varying , but by changing the probability of signals ( ) relative to the probability of stress ( ) , reflecting a situation where signal reliability is solely determined by the prevalence of signals . When signals get more prevalent than stress , their reliability decreases , even if stays maximal . We then calculate the long-term growth rate of a genotype as a function of its , and identify the value of that maximizes the long-term growth rate . We find that , as the reliability of signals increases ( approaches ) , the long-term growth rate is maximized by increasingly larger values of responsive switching ( Fig . 1A ) . Therefore , the analytical model predicts that increasing signal reliability leads to the evolution of increasingly high responsive switching . The strength of this effect depends critically on the penalty imposed if the phenotype does not match the state of the environment: relaxing the very stringent mortality used in Fig . 1 ( , i . e . the unprotected type has a growth rate of under stress conditions ) makes it less important for individuals to invest in protection , and the long-term growth rate becomes less dependent on the rates of random and responsive switching ( Fig . S2" ) . We then analyze the situation where every stress event is preceded by a signal , but there are more stress events than signals; formally , this corresponds to at maximal . Responsive switching is expected to evolve to 1 in this situation , which our analytical model shows ( Fig . S1B ) . We thus examine how the frequency of stress events affects the evolution of . We calculate long-term growth rates of genotypes as a function of their values of . As the frequency of stress events increases relative to signals , the values of that maximize long-term growth rate increase ( Fig . 1B ) . This is what one would expect; if all signals are followed by stress , the only way to increase protection is to increase random switching with increasing stress frequency . We next consider a more general scenario where stress is not always preceded by signals and , as before , signals are not always followed by stress . Individuals can only protect themselves against stress that is not preceded by a signal if they sometimes switch randomly to a protected state , i . e . , if their is larger than zero . We would thus assume that , at least for certain parameter combinations , long-term growth rate is maximal for individuals that have intermediate values of both and . We investigate how different signal reliabilities affect combinations of these two traits that maximize long-term growth rates . To vary signal reliability , we vary , the association between signal and stress , while keeping the probabilities of stress and signal constant . Fig . 2 shows the long-term growth rate as a function of and . Fig . 2A–C depict the function relating a genotype's long-term growth rate to its values of random and responsive switching . At least two interesting observations can be made . First , we see that for intermediate values of , the analytical model predicts that the long-term growth rate is maximized by a genotype that has intermediate values of both random and responsive switching ( Fig . 2A–C , contour plots ) . Second , we see that an increasing signal reliability drives the evolution of higher responsive switching and lower random switching . Individual-based simulations support these results ( Fig . 2A–C , yellow filled circles ) . They show that the dominant genotype after many generations is close to the combination of and that maximize the long-term growth rate according to the analytical model . Varying the cost of protection , , and the penalty of expressing an unprotected phenotype in a stressful environment , , changes the results quantitatively . More costly protection ( higher values of ) leads to decreasing rates of as well as , whereas higher penalties for not being protected ( higher values of ) lead to higher switching rates ( Fig . S3 ) . The consequences of the evolutionary dynamics of random and responsive switching are presented in Fig . 2D–F as scaled Venn diagrams . Each scaled Venn diagram is plotted for one particular genotype , namely the genotype whose combination of random switching and responsive switching maximizes the long-term growth rate , given certain signal reliability . The diagrams depict the sets ‘signal’ , ‘stress’ , and ‘switching’ . Areas of overlap between sets present the frequency of different outcomes . For example , the overlap between the three sets ‘signal’ , ‘stress’ , and ‘switching’ presents the proportion of time steps that fulfill three conditions: there is a stressful event , this event is preceded by a signal , and the genotype switches to the protective state . From these diagrams , one can thus read the probability of all possible outcomes , including correct decisions , false positive decisions ( switch if there is no stress ) and false negative decisions ( do not switch if there is a stress ) . These diagrams show that if signal reliability is high , switching occurs almost exclusively in response to the signal , and the fraction of correct decisions is high . As the signal reliability decreases ( from D to F ) , there is a shift from responsive switching to random switching . However , despite this shift , the fraction of correct decisions decreases . Overall , we see that the evolution of random and responsive phenotype switching strategies is strongly affected by the reliability of environmental signals , and both strategies can evolve simultaneously . So far , we have assumed a simple ecological setting – a population that lives in a homogeneous environment , and where all individuals are always subject to the same conditions . Would the conclusions change substantially if we modified the ecological setting ? To investigate this , we consider a situation where the population evolves in two spatially separated patches , and where the environmental conditions imposed in the two patches are independent from each other ( see Methods ) . We assume unlimited migration of individuals at the end of each time step , so that individuals are completely mixed . We use the analytical model to determine the combination of random and responsive switching that maximizes long-term growth rate , and use the individual-based model to investigate the evolutionary dynamics ( Fig . 2G ) . The analytical model predicts that in this case the switching values that maximize the long-term growth rate will be lower than in case of a single patch . An intuitive explanation for this is the following: distributing the carriers of a particular genotype over two patches with independent environments leads to decreasing variation in performance of this genotype over time , since it decreases the chance that all carriers will be exposed to stressful conditions at the same time . Avoiding risk by investing more often into protection thus becomes less important for a genotype's survival . A similar effect was analyzed in earlier studies , showing that optimal germination rates of annual plants increase as dispersal rates increase [38] , [39] . In this case , germination is the riskier strategy , and increased dispersal allows risk-prone types to persist . We then again used an individual-based model to analyze the evolutionary dynamics with different types of density regulation , which are not captured in our analytical model . It is essential to include density regulation in our individual based model; without density regulation , the number of individuals will either decline to zero , or grow without limit . We thus assume that the environment has a constant carrying capacity , , and implement two different types of density regulation . With global density regulation , we pool all individuals in the two patches at the end of each time step , and impose mortality ( to bring the number of individuals down if it exceeds the carrying capacity ) and reproduction ( to increase the number of individuals if it is lower than the carrying capacity ) ; the imposed rates of mortality and reproduction are identical in the two patches . With local density regulation , we assume a carrying capacity for each patch ( equal for the two patches , and equal to ) , and adjust the density locally in each patch at the end of each time step ( see Methods ) . Both local and global density regulation are relevant mechanisms in natural environments . An example for local density regulation is a bacterial infection: bacteria infect different hosts , and are exposed to selection and reproduce in those hosts , where population density is regulated locally . An example for global density regulation would be the following: individuals live in discrete patches that are spatially separated , but live off a resource that is freely diffusible . By consumption of this resource all individuals are equally affected , and their density is thus regulated globally . With global density regulation , the phenotype that dominates the individual-based models after many generations is close to the combination of random and responsive switching that , according to the analytical model , maximizes the long-term growth rate ( Fig . 2G , contour plot , and orange circles , respectively ) . In other words , the two modeling approaches give consistent results . This is expected , since previous research [40] , [41] showed that if density regulation acts in the same way on different strategies within a population , which is the case in our global density regulation regime , then the dynamics of selection is the same as if there was no density regulation . In this situation , relative fitness of the individuals is unchanged , and it is the relative fitness of each individual in its environment that is important , rather than an individual's absolute fitness over all environments . However , with local density regulation , the phenotypes that dominate the individual-based models after many generations have higher values of random switching than predicted by the analytical model ( Fig . 2G , purple circles ) . The intuitive explanation for this is the following: if the population undergoes local density regulation , individuals that survive in those patches where most other individuals die because of stress experience low population density after selection and can produce a larger number of offspring . Consequently , individuals with higher random switching values have an advantage in cases when stressful events are not preceded by signals , which tend to eliminate most individuals in the patch . However , the benefit for individuals with higher switching values depends on the composition of the population; if the population is already dominated by individuals with high random switching , their benefit vanishes . One would thus expect that this leads to negative frequency-dependent selection on the switching strategy . The analytical model , which does not include density regulation , does not capture this effect . To investigate this effect in more detail , we perform a pairwise invasibility analysis [42] of different switching strategies . We consider a population consisting of two genotypes , and . We assume that both genotypes have equal responsive switching values , and different random switching values and . Henceforth , we refer to values and as strategies and , respectively . Pairwise invasibility analysis is then carried out to investigate whether can invade into populations of , for all possible combinations of and , and vice versa . To determine the invasion success , we run semi-deterministic individual-based models with a single genotype , and introduce the invading strategy at 1% of the population size ( see legend of Fig . 3 ) . The resulting pairwise invasibility plots ( PIPs ) for a single patch as well as for two patches with global and local density regulation are shown in Fig . 3 . The PIPs show that populations with small random switching values can be invaded by mutants with higher values , while populations with large random switching values can be invaded by mutants with lower values . There is an intermediate strategy ( “singular strategy” ) that cannot be invaded by any mutant . In other words , the singular strategy is convergence stable , and it is evolutionary stable [43] . One would thus expect that populations initiated with very small or very large random switching values would evolve towards the singular strategy , and then reside there [42] , [43] . The PIPs also support the result of the individual based models stating that local density regulation promotes the evolution of higher values of random switching: with local density regulation ( Fig . 3G ) , the singular strategy is at a higher value of random switching than with global density regulation ( Fig . 3D ) . Interestingly , with the numerical resolution of our analysis , the singular strategy for one patch is indistinguishable to that for two patches with local density regulation . As discussed above , we expect two effects when increasing the number of patches from one to two . The variation in performance decreases , favoring risk prone types; and local density regulation promotes types that survive when most individuals in a patch die , favoring risk averse types . Our finding suggests that , at least for the conditions analyzed here , these two effects cancel each other , so that the singular strategy is the same for one or two patches . Increasing the number of patches further beyond two does not change the value of the singular strategy ( not shown ) . We have discussed above how local density regulation is expected to result in negative frequency dependent selection on the rate of random switching . This effect manifests in the PIPs: with local density regulation ( but not with global density regulation , or with a single patch ) , there are combinations of and that can invade each other . Such combinations of and are expected to coexist ecologically , i . e . to coexist as long as they do not mutate and evolve [42] , [43] . If they are subject to mutations that change the rate of random switching , both strategies evolve towards the convergent and evolutionary stable singular strategy , and the population becomes dominated by that strategy ( Fig . S4 ) . The biological relevance of this coexistence is thus limited; it might play a role in situations where the populations are often not in their evolutionary equilibrium , for example because the environmental regime changes frequently . The two density regulation regimes we employ here have similarities to the concepts of ‘hard’ and ‘soft’ selection in ecology ( [44] , [45] , and reviewed in [46] ) . There , the term ‘soft selection’ refers to constant habitat output , and ‘hard selection’ to variable habitat output , which is similar to the local and global density regulation regimes we use in our individual-based model . These models find that soft selection promotes the emergence of polymorphisms that are based on local adaptation to the different habitats [44]–[46] . In our case , conditions vary over time , rather than ( consistently ) across habitats , and we find no protected polymorphisms . We find , however , that the two types of regulation regimes lead to differences in the evolutionary endpoints of random and responsive switching , for reasons discussed above . It is also interesting to note that the evolutionary dynamics of random and responsive switching does quantitatively depend on the population size: the individual-based model shows that , in small populations , both random and responsive switching evolve to slightly higher values than predicted by the analytical model ( Fig . S5 ) . With increasing population size , these values decline , and approach the values predicted by the analytical model . We interpret this finding as follows: types with low switching values have higher variance in performance across time , and therefore more often reach low densities . In small populations , small densities translate to small numbers of individuals , and thus a risk of extinction; small populations are therefore dominated by types that have higher switching values , and are thus less prone to extinction . This is in line with previous results on the effects of population size [47] and population bottlenecks [32] on the evolution of bet-hedging strategies . An important example of the impact of small populations is the onset of bacterial infections: for the human pathogens Shigella and Salmonella , for example , there have been reports that ingestion of fewer than 100 bacteria is sufficient to cause disease [48] . Increased phenotypic switching might decrease the extinction risk during such population bottlenecks , and the diversity of molecular mechanisms that promote phenotypic variation in bacterial pathogens [49] are in line with this interpretation . Overall , our results point to the importance of probabilistic behavior in response to unreliable signals . We focused on environments where episodes of stress are usually preceded by a signal , but where this signal is not absolutely reliable . We find that such conditions promote the evolution of types whose phenotype expression is statistically associated with the signal , but also deviates from it in a significant way . In clonal populations of these types , not all individuals enter a protective state in response to the signal , and some individuals also enter this state when there is no signal . This probabilistic behavior balances the costs and benefits of stress protection . By limiting the number of individuals that respond to the signal , it decreases the average metabolic costs of protection . And by inducing the protective state in some individuals even in the absence of the signal , it increases the chance that the genotype survives rare events of stress that occur without warning . Interestingly , the costs and benefits of protection , and therefore the evolutionary dynamics of bet-hedging , depend on the ecological setting . Under conditions where the population is distributed across discrete patches , and where lone survivors of stress events in a patch benefit from reduced crowding , the benefit of surviving stress events increases . Consequently , such populations evolve towards a state where they are dominated by types that frequently enter the protective state even in the absence of a signal . These results emphasize the role of the ecological setting for bet-hedging . To describe the evolutionary dynamics of bet-hedging , it is not always sufficient to analyze the fit between the phenotypes expressed by a give genotype and the state of the environment . In some situations , the success of a bet-hedging strategy depends on the phenotypes expressed by others , and thus on the composition of the population . | Most organisms are occasionally exposed to adverse environmental conditions , and can express protective features that help them mitigate the harmful effects of environmental stresses , such as infections , exposure to UV light or chemicals , or sudden habitat changes . Interestingly , a number of recent experiments with unicellular microbes revealed marked variability in the responses to such stress between genetically identical individuals . Some individuals express protective features even in the absence of stress; others do not express these features even if stress reaches substantial levels . Why is stress response , which seems so important for organisms , not more tightly controlled ? One possibility is that this variation can help organisms mediate between costs and benefits of protection . These protective features are usually expressed in response to environmental signals that indicate stress . However , most signals are not absolutely reliable . Sometimes stressful conditions will not be preceded by a signal; other times , a signal might not be followed by stress . We used analytical and individual-based models to investigate how a probabilistic expression of stress response can evolve in response to unreliable signals , and in how the ecological setting influences the evolutionary dynamics . | [
"Abstract",
"Introduction",
"Methods",
"Results/Discussion"
] | [
"theoretical",
"biology",
"ecology",
"biology",
"evolutionary",
"biology"
] | 2012 | Evolution of Stress Response in the Face of Unreliable Environmental Signals |
The ability to precisely modify genomes and regulate specific genes will greatly accelerate several medical and engineering applications . The CRISPR/Cas9 ( Type II ) system binds and cuts DNA using guide RNAs , though the variables that control its on-target and off-target activity remain poorly characterized . Here , we develop and parameterize a system-wide biophysical model of Cas9-based genome editing and gene regulation to predict how changing guide RNA sequences , DNA superhelical densities , Cas9 and crRNA expression levels , organisms and growth conditions , and experimental conditions collectively control the dynamics of dCas9-based binding and Cas9-based cleavage at all DNA sites with both canonical and non-canonical PAMs . We combine statistical thermodynamics and kinetics to model Cas9:crRNA complex formation , diffusion , site selection , reversible R-loop formation , and cleavage , using large amounts of structural , biochemical , expression , and next-generation sequencing data to determine kinetic parameters and develop free energy models . Our results identify DNA supercoiling as a novel mechanism controlling Cas9 binding . Using the model , we predict Cas9 off-target binding frequencies across the lambdaphage and human genomes , and explain why Cas9’s off-target activity can be so high . With this improved understanding , we propose several rules for designing experiments for minimizing off-target activity . We also discuss the implications for engineering dCas9-based genetic circuits .
The RNA-mediated Cas9 adaptive immunity system ( CRISPR type II ) has revolutionized genome engineering by enabling the precision cutting of DNA that can be customized to target any sequence [1 , 2 , 3 , 4 , 5 , 6] , while being functional in a broad range of prokaryotes and eukaryotes , including bacteria , yeast , flies , fish , plants , worms , monkeys , mice , rats , rabbits , frogs , and human cell lines [3 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18] . By forcing the host to repair these precision DNA cuts , the CRISPR/Cas9 system allows recombinant DNA to be inserted at desired genome locations , and therefore can be used for performing high-throughput gene knockouts , loss-of-function screening , artificial immunization , removal of latent genome-encoded viruses , and site-specific gene therapy applications [19 , 20 , 21 , 22] . A nuclease-deficient version of Cas9 , called dCas9 , retains its RNA-guided DNA binding activity and has been used as a transcription factor to tightly control gene expression levels and rewire a host's transcriptional regulatory network [23] . Multiple dCas9-based repression and activation devices , including within layered genetic circuits , have been developed in bacteria , yeast , and mammalian cells; these genetic circuits can regulate a targeted promoter's transcription rate by up to 1000-fold [5 , 24 , 25 , 26 , 27] . In principle , the expression of multiple guide RNAs , working with dCas9 , enables the regulation of many promoters simultaneously , and provides an almost limitless source of programmable transcription factors . Based on recent observations , the CRISPR/Cas9/dCas9 system is highly versatile , but has imperfect specificity and activity under a wide range of environmental and genotypic conditions [25 , 28 , 29] , motivating a study of its mechanisms and the development of a model to rationally design its guide RNAs [21] . One major challenge has been binding to off-target DNA sites , resulting in off-target mis-cutting of genomic DNA by Cas9 or gene mis-regulation by dCas9 [28 , 30 , 31 , 32 , 33] . Several strategies have been shown to reduce Cas9 off-target behavior by manipulating its cleavage activity [33 , 34 , 35 , 36 , 37 , 38 , 39 , 40] . For example , two guide RNAs expressed together with a partially nuclease-deficient Cas9 nickase have been used to make two single-strand cuts at adjacent locations , increasing the rate of on-target repair by homologous recombination [40] . Further , fusing dCas9 to the FokI nuclease increased the specificity of its nuclease activity to a 20 bp recognition sequence [39] . These strategies address off-target cutting , but not off-target binding and gene regulation . A system-wide understanding of how guide RNAs work together with Cas9/dCas9 to control off- and on-targeting binding would enable the rational design of guide RNAs , and other controllable factors , to improve Cas9/dCas9 specificity and activity . In particular , when engineering dCas9-based genetic circuits , it will be desirable to modulate dCas9's ability to regulate gene expression through the introduction of guide RNA mismatches [8] . However , the quantitative relationship between guide RNA sequence and dCas9's binding affinity is currently unknown . In this work , we develop a comprehensive , mechanistic model of CRISPR/Cas9 that predicts how experimental conditions and guide RNA sequences ( crRNAs ) control target site selection and cleavage activity . To initially parameterize this model , we analyze the large amount of structural , biochemical , and next-generation sequencing data that has recently measured several aspects of CRISPR/Cas9's function with different crRNAs under varied experimental conditions [4 , 29 , 33 , 35 , 37 , 38 , 41 , 42 , 43 , 44] . We formulate a single system-wide model that explains how these disparate observations can originate from the same CRISPR/Cas9 mechanism of function . We also present quantitative criteria for designing guide RNA sequences with targeted binding and cleavage activities . By accounting for several important factors beyond the guide RNA sequence , our design rules are a significant improvement over existing , and somewhat contradictory , sequence design rules whose outcomes have also depended on the selected experimental conditions [8 , 21 , 33 , 37 , 42] . To develop this model , we employed statistical thermodynamics and the law of mass action to formulate a five-step mechanism that accounts for concentration-dependent , cell volume-dependent , host genome-dependent , and crRNA-dependent changes to Cas9 complex formation , diffusion , target specificity , and target activity ( Fig 1 ) . Kinetic and thermodynamic constants were estimated by analyzing six studies of Cas9/dCas9 function ( Table 1 ) . We validated this model using in vitro Cas9-dependent cleavage rate data ( Fig 2 ) , obtained by Sternberg et al . [38] , together with new data collected in this study , measuring in vivo dCas9-dependent transcriptional repression in synthetic genetic circuits within bacterial cells ( Fig 3 ) . Further , to predict how a guide RNA controls target specificity , we used deep sequencing data [3 , 33 , 37 , 41] to compile a position-dependent , nearest neighbor binding model that accounts for canonical and non-canonical PAM recognition sites , R-loop formation , and mismatches with DNA target sites ( Fig 4 ) . We then employ the model to predict the binding occupancies of dCas9 to the lambda phage genome , mirroring a recent experimental study utilizing DNA curtains , to illustrate the differing dynamics between on-target and off-target DNA sites ( Fig 5 ) . Finally , we applied the model to predict the frequency and location of off-target cleavage sites in a medically relevant example , where Cas9 was used to remove latent HIV viral DNA segments from a human cell line [45] ( Fig 6 ) . Finally , by performing a sensitivity analysis on the model , we show the optimal experimental conditions to maximize on-target ( d ) Cas9 activity and minimize ( d ) Cas9 off-target binding ( Fig 7 ) .
The activity of Cas9-mediated cleavage is dictated by a 5-step mechanism that includes the expression of Cas9 and crRNA , the formation of active Cas9:crRNA complex , a random intracellular walk to search for DNA target sites , the formation of a Cas9:crRNA:DNA complex ( an R-loop ) at DNA sites , and finally DNA site cleavage ( Fig 1 ) . We developed a dynamical mechanistic model that incorporates all known biomolecular interactions and processes that control the rates of these steps ( Materials and Methods ) . The mechanistic model accounts for how several factors control all the DNA sites' cleavage rates , including changing Cas9 and crRNA expression levels , different crRNA protospacer ( guide ) sequences , different DNA site sequences , both canonical and non-canonical PAM recognition DNA site sequences , and the effects of DNA site supercoiling . The model also explicitly accounts for the host's specifications , including its genome sequence , genome length , cell size , and growth rate . Moreover , the model allows for the expression of multiple crRNA guide strands , and it will determine how the competitive binding of crRNAs to Cas9 will also affect the DNA sites' cleavage rates . When expressing Cas9 , the model calculates the numbers of all free , bound , and cleaved DNA sites that contain a canonical or non-canonical PAM site , encoded within the host genome or on plasmids . When expressing nuclease-deficient dCas9 , the model calculates the occupancy of stably bound dCas9:crRNA complexes to all DNA sites . Overall , the formulated model contained eight unknown parameters quantifying the binding interactions between Cas9 and crRNA as well as the effects of DNA site supercoiling on Cas9 binding affinity . In addition , the model also utilized a multi-parameter free energy model quantifying crRNA-DNA site interactions . We first utilized the in vitro measurements obtained by Sternberg et . al . to determine the kinetic parameter values that quantify Cas9:crRNA complex formation , pre-cleavage dissociation , and Cas9-dependent cleavage [38] . In this study , the binding locations and cleavage rates of Cas9 using a plasmid DNA substrate were measured to characterize the multi-step process by which Cas9 finds DNA targets , initiates R-loop formation , and cleaves DNA sites . Here , we utilized the authors' dynamic measurements of DNA site cleavage at different concentrations of Cas9 and crRNA , using either an on-target site on plasmid DNA ( Fig 2B in [38] ) or an on-target site on a double-stranded DNA fragment ( Extended Data Fig 5 in [38] ) . We also analyzed Cas9’s protein structure and its motility to estimate that Cas9's characteristic length is λCas9≈150°A [43 , 46] and its diffusivity in a cytoplasmic-like buffer is 45 μm2/s [47] . Therefore , we determined that Cas9 performs an isotropic random walk with a diffusive specific flow rate of 4 . 05 x10-10 1/sec . In the presence of 25 nM plasmid DNA , these calculations indicate that a Cas9:crRNA complex collides with a DNA site 61 times per second . We then determined the kinetic parameter values controlling Cas9:crRNA association ( kf ) , isomerization ( kI ) , pre-cleavage dissociation ( kd ) , and cleavage activity ( kC ) by calculating the rate of cleavage ( rC ) across a range of Cas9 and crRNA concentrations , mirroring the experimental conditions , and comparing to experimental cleavage measurements ( 56 experiments; R2 = 0 . 97; S1 Fig ) using 25 nM plasmid DNA [38] . The model solution was evaluated for an initial 10 minute time period , followed by in silico addition of the DNA substrate and an additional 30 minute time period . The best-fit kinetic parameter values were then determined through optimization to minimize the relative error between calculated and measured cleavage rates ( Materials and Methods ) . Based on our analysis ( S2 Fig ) , we could uniquely parameterize kf , kI , and the ratio kc/kd ( Table 2 ) . Surprisingly , the rate of cleavage was found to be less than the rate of pre-cleavage dissociation ( kc/kd << 1 ) , suggesting that ( d ) Cas9 must engage in multiple aborted rounds of binding and R-loop formation before successfully cleaving the DNA site . Using the best-fit parameter values , the model was able to accurately capture the experimentally observed time-dependent cleavage rates while varying the Cas9 and crRNA concentrations ( Fig 2 ) . The best-fit parameter values are reported in Table 2 . As expected , when the Cas9 concentration is limiting , the calculated amount of cleaved DNA is almost equal to the Cas9 concentration because Cas9 does not turn-over . However , when non-supercoiled , short ( 55 bp ) DNA fragments were used as template , Sternberg et . al . found that Cas9’s total cleavage activity dropped by 5-fold even though the apparent cleavage rate of DNA increased ( S3A Fig ) . The authors hypothesized that the reduced cleavage activity originated from a batch of partially active Cas9 enzyme . To test this possibility , we first reduced the concentration of Cas9 in silico to 20% of the reported concentration . The model reproduced the measured amount of cleaved DNA after the 10 minute incubation period , however , the model-calculated rise to steady-state was slower than the experimentally observed rise ( S3A Fig ) . Instead , if we also accounted for the much smaller number of DNA sites and the lack of negative supercoiling of the short DNA fragments , then the model correctly explains the experimentally observed fast rise time ( S3B and S3C Fig ) . Specifically , there were 5482 total possible DNA sites ( N ) when plasmid DNA template ( 2741 bp ) was used in the in vitro measurements , compared to only 110 possible DNA sites when short DNA fragments were used ( 55 bp ) , resulting in about 50-fold higher rise time . The difference in DNA site supercoiling partly counteracted this much higher model-calculated rise time by requiring an additional 0 . 43 kcal/mol energy for the Cas9:crRNA complex to successfully form an R-loop , lowering the model-calculated rise time to about 25-fold higher than when using the plasmid DNA as template , which is close to the experimental measurement . When using dCas9 to implement genetic forms of computing , we anticipated the need to introduce several adjacent crRNA binding sites to differentially regulate gene expression . However , according to the biophysics of R-loop formation , it was possible that the binding of a ( d ) Cas9:crRNA complex to one target DNA site could actually lower the affinity of ( d ) Cas9:crRNA complexes to adjacent DNA sites . Specifically , when a dCas9:crRNA complex binds to a DNA site , the creation of an R-loop will negatively supercoil the site’s DNA , for example , by untwisting it . Because DNA’s linking number is conserved , the negative supercoiling of one DNA site will increase the positive supercoiling of adjacent DNA sites . According to model Eq 13 , a higher superhelical density will make it less likely for another dCas9:crRNA complex to bind to adjacent DNA sites by requiring a higher free energy input to stably form an R-loop [48] . To investigate this effect , we constructed a three plasmid system that expresses dCas9 using a constitutive promoter , a single crRNA using an IPTG-inducible PTAC promoter , and a YFP reporter protein using a constitutive promoter containing a fully complementary ( on-target ) crRNA binding site ( Fig 3A ) . Using dCas9:crRNA as a transcriptional repressor , we measured steady-state YFP expression levels as the transcription rate of the crRNA was steadily increased via IPTG induction . We then introduced either one , two , four , or eight additional on-target crRNA binding sites at a distal location on the high-copy reporter plasmid , upstream of the YFP promoter , separated by a transcriptional terminator , and performed the same YFP fluorescence measurements . These auxiliary on-target crRNA binding sites were separated by 60 to 80 bp of non-repetitive DNA . The presence of the many additional crRNA binding sites in a non-regulatory location had the expected effect of sequestering dCas9:crRNA , resulting in lower amounts of transcriptional repression at YFP’s promoter and higher YFP expression levels ( Fig 3B ) . In light of this data , we consider two distinct hypotheses relating the number of artificially added crRNA binding sites to the apparent increase in YFP expression level . First , if dCas9-mediated R-loop formation has no effect on the superhelical density of surrounding crRNA binding sites , then we should expect that adding more crRNA binding sites will proportionally sequester more dCas9:CrRNA , resulting in greater YFP expression levels as more crRNA binding sites are added . Second , if dCas9-mediated R-loop formation does increase the supercoiling of adjacent DNA sites , then we should expect that many additional crRNA binding sites will not proportionally sequester more dCas9:crRNA , resulting in a sub-linear increase in YFP expression as more sites are added . To quantify the extent that R-loop formation increases the superhelical density of surrounding crRNA binding sites , we added a single parameter to our model ( Δσ ) . When n copies of dCas9:crRNA are bound to nearby DNA sites , the initial superhelical density of the remaining nearby DNA sites is increased by nΔσ , which increases the sites’ ΔGsupercoiling according to Eq 13 , and lowers the probability that they will be bound by additional dCas9:crRNA . If Δσ is zero , model calculations show that adding 8 crRNA binding sites to the plasmid will yield greater amounts of dCas9:crRNA sequestration , resulting in 300-fold more YFP expression ( Fig 3B , right ) . However , if Δσ is positive , adding more crRNA binding sites will yield diminishing amounts of dCas9:crRNA sequestration and sub-linear increases in YFP expression ( Fig 3B , left ) . Using this data-set to evaluate these two hypotheses , we found that adding 2 , 4 , or 8 additional crRNA binding sites increased dCas9:crRNA sequestration and YFP expression , but with lower-than-proportional amounts , suggesting that there is indeed a anti-cooperative mechanism affecting site occupancies ( Fig 3B ) . We found that a moderate site-to-site superhelical density penalty ( Δσ = 0 . 0065 ) was sufficient to explain how adding more crRNA binding sites sublinearly increased dCas9:crRNA sequestration and YFP expression level ( Fig 3B , left ) with a high degree of confidence ( R2 = 0 . 97 , p < 10−8; S4 Fig ) . The apparent site-to-site changes in superhelical density appear to be additive; for dCas9 to stably bind to the 8 binding site array , it would be necessary to untwist over 160 bp of the 900 bp region , equivalent to about 6 kcal/mol of free energy input , which would greatly destabilize R-loop formation and lower dCas9:crRNA occupancy . To compare , a model that ignores changes in superhelical density , and its effect on dCas9:crRNA occupancy , was not able to explain the measurements ( Fig 3B , right ) . Additionally , according to this data-set , it appears that crRNA concentration , and not dCas9 concentration , was limiting the total amount of dCas9:crRNA that could bind these additional crRNA bind sites or the promoter to repress YFP expression , discounting an alternative hypothesis . Cas9 requires the presence of a protospacer adjacent motif ( PAM ) sequence to bind to a DNA site , form an R-loop , and cleave DNA . While the consensus PAM sequence for the Cas9 from S . pyogenes is NGG , it was previously observed that R-loop formation could take place at non-canonical PAM sites , resulting in a considerable amount of off-target activity [3 , 35 , 42] . To quantify Cas9’s binding free energy to DNA sites that use either canonical and non-canonical PAM sites , we utilized data from a recent study that measured Cas9’s cleavage activity when bound to DNA sites with identical PAM-proximal sequences , but randomized PAM sequences , using a homolog of Cas9 from S . pneumonia [3] . We compared cleavage activities to a reference PAM site , which we defined by the four nucleotide sequence 5’-CGGT-3’ , with a corresponding reference free energy ( ΔGPAM , ref = -9 . 9 kcal/mol ) . This reference free energy was consistent with our in vivo measurements shown in Fig 3 . Importantly , we found that the first nucleotide ( N in NGG ) did not significantly contribute to Cas9’s cleavage activity , but that the fourth nucleotide did significantly alter cleavage activity . We then employed model Eqs 6 and 15 to calculate the change in ΔGtarget , and therefore the change in ΔGPAM , corresponding to each four nucleotide PAM sequence . As only the PAM sequences vary , the free energies ΔGexchange and ΔGsupercoiling were not expected to change significantly . To eliminate background noise , we excluded any PAM sequence that resulted in less than 1% cleavage . Further , we found that averaging cleavage activities over the first nucleotide position of each PAM sequence resulted in apparent free energies with a low coefficient of variation of 9% . Overall , we quantified the apparent ΔGPAM free energies of 26 PAM sequences and found that they vary by 4 kcal/mol ( Table 3 ) , which is equivalent to about 700-fold change in instantaneous cleavage activity ( all other factors being equal ) . As expected , the canonical PAM site NGGN binds with the highest affinity to Cas9 with ΔGPAM energies exceeding -9 kcal/mol . However , there are several non-canonical PAM sites with sufficiently high affinities to contribute to off-target cleavage activity , including NAGN and NGWN . Further , the presence of a gap between a fully complementary protospacer and a PAM site does not fully ablate Cas9’s binding affinity; a single nucleotide gap ( NNGG ) penalized binding by 2 . 2 kcal/mol , while a single nucleotide bulge ( GGNN ) had a larger effect ( a 3 kcal/mol penalty ) . Recent studies have demonstrated that Cas9 can bind well to several non-canonical PAM site such as NAG , NGA , NAA , NTG , NGC , NCG , and NGT , though the extent of its promiscuity does depend on the Cas9 species origin [49 , 50] . Using the ΔGPAM free energies in Table 3 and an estimate of the DNA site’s superhelical density , the model can now calculate the binding free energy ( ΔGtarget ) of Cas9:crRNA when the crRNA’s guide sequence perfectly matches the DNA site’s sequence . To quantify the effects of mismatches , we next developed a free energy model ( ΔΔGexchange ) that accounts for changes in the crRNA's guide sequence . A mismatch between the crRNA guide sequence and a DNA site destabilizes the formation of the Cas9:crRNA:DNA R-loop and increases the likelihood that the Cas9:crRNA complex dissociates prior to cleaving the DNA site [35 , 38 , 44] . In our model , we quantify the thermodynamics of the R-loop strand displacement process , comparing the free energy of the initial double-stranded DNA state to the free energy of the Cas9:crRNA:RNA R-loop , resulting in a free energy change ( ΔΔGexchange ) . ΔΔGexchange will change whenever a mismatch is introduced , though the magnitude of the change will depend on both the position of the mismatch and the surrounding sequence composition . As the last step in developing our model , we utilized three next-generation sequencing datasets ( Table 1 ) to parameterize position- and sequence-dependent free energy models quantifying the Cas9:crRNA:DNA interactions during R-loop formation . Three types of free energy models were created and compared to investigate whether Cas9 plays a role in mediating these interactions , and whether these interactions varied across different host genomes . In the Pattanayak et al . , the on-target and off-target cleavage activities from four sgRNAs were measured via deep sequencing across a degenerate library of DNA sites within an in vitro reaction [33] . In Hsu et . al . and Mali et . al . , respectively , the amounts and locations of Cas9-based cleavage and dCas9-based transcriptional activation were recorded in vivo via deep sequencing [33 , 37 , 41] . We categorized these measurements into two data-sets , dataset I and II ( Table 1 ) . To analyze these data-sets , we first identified all DNA sites that utilized a canonical PAM sequence similar to the PAM sequence adjacent to the targeted sequences and yielded greater than 50 read counts , finding 3671 sites in data-set I and 5979 sites in data-set II . Further , the superhelical densities of DNA sites are the same within the in vitro data-set , and largely similar across the E . coli genome , enabling us to disregard changes in ΔΔGsupercoiling for this analysis . We then compared sequencing read counts between Cas9 cleavage at the perfectly complementary ( on-target ) site and all off-target sites , obtaining a direct relationship between changes in sequencing read count and changes in ΔΔGexchange , according to our model Eqs 7 and 8 . When analyzing dCas9-based transcriptional activation measurements , we assumed that the dCas9 binding probability was proportional to the transcription rate of the target promoters . For each sequence , this rate was also proportional to the ratio of the background-subtracted read counts from the samples and the background-subtracted read counts from the positive controls . We then utilized Eqs 7 and 8 to convert the normalized RNA-Seq read counts into changes in ΔΔGexchange [51] . By excluding alternative PAM sites , we were able to more precisely quantify the energetic effects of introducing mismatches into DNA site sequences . Comparing the Pattanayak et . al . and Hsu et . al . datasets , the overall average energetic penalty for a single mismatch was 0 . 14 and 0 . 78 kcal/mol , equivalent to a 1 . 26-fold and 3 . 7-fold drop in Cas9 activity , respectively , which suggests that the differences between in vivo and in vitro measurements and characterization protocol had an influence on off-target cleavage activities . However , some single mismatches were found to penalize ΔGexchange by 4 kcal/mol , equivalent to a 785-fold drop in Cas9 activity . Therefore , we next formulated position-dependent and sequence-dependent models to quantify how the introduction of mismatches in either the crRNA guide sequence or DNA site sequence affected Cas9 activity . In the first free energy model , we employed Eq 9 to calculate ΔΔGexchange , which quantifies the thermodynamic stability of the RNA-DNA and DNA-DNA complexes responsible for R-loop formation , together with 21 unknown position-dependent coefficients . While the free energies of DNA-DNA and DNA-RNA complementary duplexes have been measured [52] [53] , there has been limited measurements of DNA-RNA mismatch free energies . Using a dinucleotide nearest-neighbor model , there are 240 types of RNA-DNA mismatches; however , the free energies of only about 72 of them have been experimentally measured [54 , 55 , 56 , 57 , 58] . After incorporating the known complementary and mismatch DNA-DNA and RNA-DNA free energies into Eq 9 , and utilizing either dataset I or dataset II to parameterize the position-dependent coefficients , the resulting model was not able to predict Cas9 binding or cleavage activity ( R2 = 0 . 32 and 0 . 07 for dataset I and dataset II , respectively; S4 Fig ) . Consequently , we anticipate that additional measurements of RNA-DNA mismatch free energies and kinetic modeling will improve the development of accurate first principles models of R-loop formation . We then developed an alternative free energy model ( Eq 10 ) that does not rely on previous thermodynamic measurements of nucleic acid interactions , but instead uses measured Cas9 activities at thousands of DNA sites to determine unknown model parameters . The free energy model accounts for all possible guide RNA guide sequences and DNA site sequences , employing a dinucleotide nearest-neighbor model ( 256 unknown coefficients ) together with 21 position-dependent coefficients . We determined the unknown parameters using either dataset I ( 3671 measurements ) or dataset II ( 5979 measurements ) , utilizing nonlinear least-squares to minimize the error between the apparent and calculated ΔΔGexchange free energies ( Materials and Methods ) . This parameterization determined values for 86% and 80% of the unknown parameters , using dataset I and II , respectively . In particular , these datasets lacked DNA sites with two consecutive mismatches , resulting in several unidentified parameters . The resulting free energy models for ΔΔGexchange were qualitatively consistent with anecdotal observations; for example , the first eight position-dependent coefficients have the highest values , accounting for about 67% ( dataset II ) to 81% ( dataset I ) of ΔΔGexchange variation , quantifying the impact of PAM-proximal mismatches on Cas9 activity ( Fig 4A ) . As a comparison , in a recent in vivo study [29] , 87% of sequences with high binding affinities to a Cas9:crRNA complex have at most 1 mismatch within the first 8 nucleotides ( Fig 4B ) . The apparent mismatch free energies also varied up to 5 kcal/mol , suggesting that mismatch sequence composition is an additional factor that affects Cas9 activity . However , the energetic penalties of specific mismatched RNA:DNA sequences were not necessarily the same across the two models . When parameterized with in vitro Cas9 cleavage measurements ( dataset I ) , the most energetically unfavorable mismatches were found at dAG , dGG , and dCG dinucleotides that were positioned over rAC , rAG/rGA , or rGT/rTG dinucleotides . In contrast , when parameterized with in vivo Cas9 activity measurements ( dataset II ) , the mismatch free energy penalties were more evenly distributed , potentially due to confounding interactions arising from the DNA sites' chromatin states . Overall , the empirically parameterized free energy models were able to sufficiently account for the sequence- and position-dependent effects on Cas9 activity across the thousands of DNA sites ( R2 = 0 . 74 and 0 . 61 for datasets I and II , respectively; Fig 4C ) . However , the maximum uncertainty in a free energy parameter was 2 kcal/mol , indicating that there is significant opportunity for improving both the breadth and precision of Cas9 activity measurements with the objective of developing more accurate free energy models . Next , we applied the parameterized mechanistic model to calculate dCas9 binding occupancies across the lambda bacteriophage genome when using a crRNA guide sequence that targets a specific genomic location , designated λ2 . Our calculations mirror recently conducted experiments that monitored the dynamics of fluorescently labeled dCas9:crRNAλ2 as it interacted with an array of λ-phage genomic DNA within a flow chamber , called a DNA curtain [38] . Using these calculations , we examine how the sequence composition and PAM density of a genome affects the partitioning of dCas9 and its binding dynamics . Overall , the λ-phage genome contains 3179 and 2497 canonical PAM sites on its forward and reverse strands , respectively , together with 17933 and 16445 non-canonical PAM sites with a density of about one PAM site per 2 . 4 bp . To calculate the dCas9 binding free energies at all PAM sites , we identified their corresponding ΔGPAM binding free energies ( Table 3 ) and used both the λ2 guide and DNA site sequences to calculate the free energy change during R-loop formation ( ΔΔGexchange ) . Here , we utilized the previously parameterized distance-dependent coefficients ( Fig 4 ) and a DNA:RNA mismatch penalty of 0 . 78 kcal/mol , which was the overall average energetic penalty observed in the Hsu et . al . data-set . We also assumed that all λ-phage genomic sites are equally supercoiled ( ΔΔGsupercoiling = 0 ) . Model parameters are listed in S1 Table . We found that the binding free energies of dCas9:crRNAλ2 varied by 25 kcal/mol across the 40054 PAM sites , and only 3880 of them had negative dCas9:crRNAλ2 binding free energies ( ΔGtarget < 0 ) ( Fig 5A ) . Most PAM-proximal DNA sites had large numbers of mismatches with the crRNAλ2 guide sequence , causing ΔΔGexchange to be more positive than ΔGPAM ( Fig 5B and 5C ) . In particular , there were only 25 DNA sites that had highly negative binding free energies ( ΔGtarget < -6 kcal/mol ) . As expected , the λ2 DNA site formed a perfect DNA:RNA duplex with crRNAλ2 , resulting in a zero model-calculated ΔΔGexchange penalty and a ΔGtarget of -9 . 9 kcal/mol . However , a second off-target DNA site , designated OS1 , had a canonical PAM ( GGGA , ΔGPAM = -9 . 4 kcal/mol ) , only two mismatches within the 8 most PAM-proximal nucleotides , and an additional six mismatches in the remaining 12 nucleotides , yielding a ΔGtarget of -6 . 3 kcal/mol . Interestingly , fluorescently labeled dCas9 was observed to transiently bind to OS1’s position in the λ-genome [38] . By enumerating and calculating the dCas9 binding free energies for all PAM sites , we can then calculate the system’s overall partition function to determine their binding occupancies under several scenarios . The canonical partition function quantifies the amount of dCas9:crRNA that will be sequestered under equilibrium conditions . It is also used in Eq 7 to determine the instantaneous binding probabilities to all DNA sites . When using dCas9:crRNAλ2 , a fully accessible λ-genome has an overall partition function value of 162 . 6 . The λ2 DNA site contributes the largest amount ( 151 . 04 ) , indicating that it has the largest probability of being bound first . The off-target OS1 site contributes only 0 . 37 to the partition function summation , and therefore has a 408-fold lower probability of being bound first , compared to λ2 . However , the additional 3879 off-target sites provide a significant contribution to the partition function summation , which will affect the binding occupancies at all PAM sites; sites with canonical PAMs contribute 7 . 83 , while those with non-canonical PAMs contribute 3 . 36 . As a result , it is 30-fold more likely that dCas9:crRNAλ2 will initially bind to one of these minor off-target sites , compared to the major off-target site OS1 . Rather than searching only for PAMs with the most complementary DNA sites , it becomes important to enumerate all possible PAM sites to correctly determine their partition function contributions . We next applied the mechanistic model to determine how dCas9 binding occupancies to the λ-phage DNA curtains will change over time . Here , mirroring the experimental system , we assume constant dCas9 and crRNAλ2 concentrations of 10 nM and 100 nM , respectively , along with a system volume of 100 μL . While our initial partition function calculations assumed that all λ-genome DNA sites are equally accessible , as dCas9:crRNA diffuses and binds to its DNA targets , it will irreversibly sequester DNA sites and eliminate their contributions to the partition function . By substituting our partition function calculations into the model’s system of differential equations ( Materials and Methods ) , using parameters listed in Table 2 , we calculated how the numbers of accessible DNA sites change over time , which then alters the binding probabilities of the remaining DNA sites ( Fig 5D and 5F ) . As expected , the λ2 DNA site binds fastest to dCas9:crRNAλ2 and is predicted to be fully bound within a minute ( Fig 5E ) . During that time , the average binding occupancies of the other individual DNA sites do not appreciably increase . However , once the λ2 DNA site has been sequestered , the system’s overall partition function decreases from 162 . 6 to 11 . 56 , which increases the off-target binding rate of dCas9:crRNAλ2 by 14-fold . As a result , the OS1 major off-target DNA site becomes fully bound within the next 6 minutes . Then , once the OS1 off-target site has been sequestered , the remaining off-target DNA sites become the only possible locations where dCas9:crRNAλ2 can bind . DNA sites with ΔGtarget energies of -4 , -2 , and 0 kcal/mol will become fully bound after 6 hours , 7 days , and 200 days , respectively . These calculations assume that DNA sites remain indefinitely sequestered after dCas9 irreversibly binds , which is correct for this scenario . However , in growing cells , unbound DNA sites are continuously replenished through DNA replication according to the cell’s growth rate . For Cas9-based genome editing to become reliably used for therapeutic applications , the factors that determine the frequency and location of its off-target cleavage activity must be better understood . Here , we applied the mechanistic model to calculate the distribution of all possible off-target cleavage sites during Cas9-based human genome editing and the necessary next-generation sequencing coverage to detect the resulting indel mutations with high certainty . As a clinically relevant example , our calculations mirrored a recent study that applied Cas9-based genome editing to excise integrated copies of the HIV provirus from infected human U1 cell lines by cleaving genomic DNA at flanking LTR sites [45] . Using the parameterized model , we first calculated the binding free energies between Cas9:crRNALTR-B , using a guide RNA that complements the LTR-B recognition sequence , and all off-target DNA sites within the reference human genome , finding that there are 3105 DNA sites with negative binding free energies ( ΔGtarget < 0 ) [59] . We repeated these calculations on a single copy of the HIV provirus , including the on-target LTR-B site . We solved the model’s system of differential equations describing time-dependent cleavage at both on- and off-targets sites to determine their cleavage efficiencies after a 1000 hour time period , using the kinetic parameters in Table 2 , a genome length ( N ) of 6 . 4x109 bp ( forward and reverse strands ) , a 8-fold higher system volume , a cell doubling time of 20 hours , and 100 nM initial concentrations for both Cas9 and crRNALTR-B ( S2 Table ) . The resulting cleavage efficiencies varied considerably across eight orders of magnitude; the on-target LTR-B site reached 100% cleavage , while the off-target sites had cleavage efficiencies ranging from 1 in 10 , 000 , 000 ( 10−5% ) to 85% ( Fig 6A ) . The majority of off-target sites have extremely low cleavage efficiencies ( less than 1% ) , creating a mixture of cells with high genomic heterogeneity . Consequently , if we assume that cleavage events become indel mutations , then identifying their locations across an entire genome will require a highly sensitive indel-specific assay or next-generation sequencing with high coverage . For example , to detect 50% of all expected indel locations across a genome , an assay must be capable of positively identifying the presence of an indel at a single location even if its frequency is only 1 in 5 ( 20% ) within the genome mixture ( Fig 6B ) . The assay must be 20-fold more sensitive ( 1 in 100 ) to detect 90% of all indel locations . When using next-generation sequencing for detection under a best-case scenario , at least 13X coverage will be needed to identify 50% of all expected indels and at least 200X coverage to identify 90% of all indels ( Fig 6C ) . Using the model , we then determined how the DNA sites’ cleavage distributions were affected when lowering the Cas9 and sgRNA concentrations by 10-fold ( Fig 6A , blue ) . The distribution shifted leftward and the off-target DNA sites’ average cleavage efficiency decreased from 1% to 0 . 1% . While decreasing the frequency of indels is the prime objective , an even more sensitive assay will be needed to confirm their absence . According to model calculations , an 60X sequencing coverage will be needed to detect 50% of the expected indels , and 1000X coverage will be needed to identify 90% of indels ( Fig 6C ) . Importantly , instead of relying on next-generation sequencing , the model’s calculations can be used to design and prioritize the use of indel-specific assays that detect the presence of mutations in specific off-target sites with the highest model-predicted cleavage efficiencies . The efficiency of Cas9-based cleavage and dCas9-based gene regulation depends on several factors , some controllable and others uncontrollable and host-specific . By manipulating the controllable factors , while accounting for the host-specific ones , on-target and off-target ( d ) Cas9 activity can be appropriately varied as desired . In particular , in the future , it may become necessary to tune the extent of dCas9-based transcriptional regulation to more precisely control gene expression levels . To aid in rational experimental design , we use the model to show how all the system parameters affect ( d ) Cas9 activity and to present general guidelines for achieving desired on-target and off-target activities . First , we applied the model to calculate the dynamics of Cas9-based cleavage in actively growing cells , comparing several scenarios . The baseline model parameters are listed in S3 Table . Intuitively , DNA sites with additional mismatches have reduced cleavage efficiencies both at early and later time-points ( Fig 7A ) . Perhaps less intuitively , increasing the guide RNA’s concentration by 10-fold beyond the baseline of 207 nM does not significantly increase steady-state cleavage efficiencies , but instead accelerates the cleavage process so that the steady-state condition is reached earlier . Further , if the organism’s cellular division rate increases by 2-fold , for example if the growth conditions or media are altered , then both the rates of cleavage and the steady-state cleavage efficiencies will drop by up to 2-fold . An increased growth rate has two general effects: it more quickly replenishes bound DNA sites with newly replicated ones , and it lowers the concentrations of Cas9 and guide RNA by dilution . Finally , and more substantially , carrying out genome mutagenesis in another organism with a 10-fold larger genome has a large slowing effect on Cas9 diffusion and overall cleavage activity , greatly reducing cleavage efficiencies at all DNA sites . We next performed a sensitivity analysis on the model to examine how varying a system parameter affected cleavage at a single DNA site in an actively growing and dividing cell . If the DNA is fully complementary to the guide RNA sequence , its model-calculated minimum possible binding free energy will be ΔGtarget = -9 . 9 kcal/mol , which yields a steady-state cleavage efficiency of 98% ( Fig 7B ) . Consistent with our earlier examples , as Cas9’s binding free energy increases ( lower affinity ) above -9 kcal/mol , there will be significant drop in cleavage efficiency . DNA sites with ΔGtarget > -4 . 9 kcal/mol will have <1% cleavage efficiencies . The concentration ( or number ) of guide RNA will also have a significant effect on cleavage efficiencies , but only when the guide RNA is a limiting substrate in the formation of the active Cas9:crRNA complex . As a result , when increasing the guide RNA concentration , cleavage efficiencies will rise until a critical threshold and thereafter there will be a plateau in cleavage efficiency ( Fig 7B ) . We next examined how these same parameters affected dCas9-based transcriptional regulation , and found similar relationships . The binding free energy between a guide RNA and its DNA site ( ΔGtarget ) controls both the dynamics and steady-state transcription rate of a dCas9-regulated promoter ( Fig 7C ) . The binding free energy can be tuned by purposefully introducing mismatches into the guide RNA; within the linear regime , a 1 . 0 kcal/mol increase in ΔGtarget will lower the binding occupancy of dCas9:crRNA by about 5-fold , which will increase a promoter’s transcription rate if dCas9 is utilized as a repressor ( Fig 7D ) . The guide RNA concentration may also be controlled by employing environmentally-sensitive or inducible promoters . dCas9:crRNA’s binding occupancy at a particular DNA site depends sigmoidally on the crRNA expression level . There is a small range of crRNA expression levels where the largest change in dCas9:crRNA and promoter repression will take place . The addition of auxiliary crRNA binding sites will shift this sigmoidal curve rightwards . Large changes in binding occupancy also occur when the organism’s growth rate is increased or when gene regulation takes place in another organism with a larger genome ( Fig 7C and 7D ) . Below , we discuss the implications of these parameter sensitivities when engineering dCas9-based genetic circuits .
We have developed the first mechanistic , quantitative model of CRISPR/Cas9 that encompasses the multi-step process responsible for Cas9-based genome editing and dCas9-based gene regulation . Our dynamical model holistically accounts for the kinetics of expression and formation of the active Cas9:crRNA complex , mass transfer by passive three-dimensional diffusion , genome-wide site selection according to the formation of R-loops at PAM-containing DNA sites , and the kinetics of irreversible site binding ( Fig 1 ) . We parameterized the model by combining both in vitro and in vivo measurements of ( d ) Cas9 activity ( Table 1 ) , arriving at a 11 parameter model ( Table 2 and S2 Fig ) that could explain how the concentrations of crRNA and Cas9 ( Figs 2 and S3 ) , DNA site supercoiling ( Fig 3 ) , canonical and non-canonical PAM sites ( Table 3 ) , and the thermodynamics of R-loop formation ( Figs 4 and S4 ) all collectively control genome-wide ( d ) Cas9 activity . In particular , we provide newly obtained measurements showing that R-loop formation at adjacent crRNA binding sites has an anti-cooperative effect on dCas9-based gene regulation , which can be explained by positive supercoiling of the surrounding DNA and the destabilization of R-loop formation ( Fig 3 ) . As part of our model-building , we found that once Cas9 binds to a target DNA site and begins to form an R-loop , it is far more likely to spontaneously dissociate than successfully form the R-loop and cleave the DNA site . Based on in vitro cleavage assay measurements ( Fig 2 ) , Cas9’s dissociation kinetic constant ( kd ) is 625-fold higher than its cleavage kinetic constant ( kc ) , suggesting that hundreds of rounds of binding , melting , strand displacement , and abortive dissociation occur before cleavage takes place , which would be similar to the binding dynamics of RNA polymerase during transcriptional initiation ( Table 2 ) . Interestingly , coincident with this observation , a recent study utilized FRET to show that the rate of dCas9 binding is much faster and more indiscriminate than the rate of Cas9 cleavage , due to coupled changes ( allostery ) in Cas9 that only activates DNA cleavage under a restricted protein conformation [59] . There have also been recent measurements of Cas9 activity at off-target sites that use non-canonical PAMs , including NAG , NGA , NAA , NTG , NGC , NCG , and NGT [3 , 49 , 50] , independent of our model-building process . Many of these alternative PAMs arise from a bulge or gap between a canonical PAM site and the guide RNA sequence [3 , 50]; through systematic comparisons , we determined the energetic penalties of these gaps and bulges on ( d ) Cas9’s binding affinity ( Table 3 ) . We also developed three different free energy models for R-loop formation , showing that the thermodynamics of R-loop formation cannot be predicted using existing measurements of RNA:DNA mismatch free energies ( S5 Fig ) . Instead , we developed a 277 parameter empirical nearest-neighbor model and parameterized it using over 5000 measurements of ( d ) Cas9 activity ( Fig 4 ) . According to our model , the PAM-proximal 8 nucleotide seed region is responsible for up to 81% of ( d ) Cas9’s binding affinity and a single mismatch in this region lowers Cas9’s binding affinity by 14-fold . However , it is clear that the differences between in vitro and in vivo measurements have a confounding effect on ( d ) Cas9 R-loop formation and activity ( Fig 4 ) . By carrying out genome-wide calculations on the lambdaphage and human genomes , mirroring recent experimental studies , we illustrated several physical principles governing ( d ) Cas9 activity that remain relevant regardless of model parameterization . First , Cas9 irreversibly binds to DNA sites in a hierarchical order , and its occupation of the highest affinity on-target sites causes its rate of binding to off-target sites to substantially increase ( Fig 5 ) . Therefore , both Cas9 concentration as well as incubation time are critical factors that control off-target activity as anecdotally observed in previous studies [60] . Second , off-target binding is highly heterogeneous across a genome; the binding occupancy at individual off-target DNA sites may be small , but the collective binding of Cas9 to all off-target sites is substantial . When Cas9-based genome editing is used as a therapeutic , the verification of off-target cleavage events will require very high sequencing coverage or rationally selected indel-specific detection assays ( Fig 6 ) . Third , our model explains why Cas9 off-target activity greatly varies across organisms . In bacteria , ( d ) Cas9 activity has been observed to be highly specific to its on-target sites [5 , 8] whereas , in human cells , next-generation sequencing has revealed thousands of off-target DNA cleavage events [5 , 29 , 42 , 50] . By modeling diffusion and genome-wide site specificity , we showed that the large increase in genome size , and not the difference in cell growth rate , is responsible for the observed increase in off-target activity . Importantly , it was necessary to identify and include both canonical and non-canonical PAM sites in our ensemble calculations to fully account for the breadth of off-target activity . These insights have the potential to greatly improve the predictive power of existing in silico target prediction methods [61 , 62] as recent observations have found that about 60% of off-target sites are not correctly predicted by existing bioinformatics models [50] . There are several practical steps that one can take to improve on-target Cas9 activity , while limiting off-target activity . First , the active Cas9:crRNA complex concentration controls the overall extent of off-target activity; if it’s high for a short period of time ( 10 nM for only 2 minutes and 100 nM for only 3 hours in bacterial and mammalian cells , respectively ) , then the rate of binding to off-target sites will not substantially rise as on-target sites have become occupied . During preparation of this article , two recent studies have implemented inducible Cas9 activity by expressing Cas9 using a doxycycline-inducible promoter [63] or by expressing a split version of Cas9 that uses rapamycin-inducible FRB domains to activate self-assembly [64] . Both approaches lowered the number of observed , off-target indel mutations . Second , during the design of guide RNA sequences , the search for off-target DNA sites must at least include both canonical and non-canonical PAMs ( Table 3 ) . A more thorough search would calculate Cas9’s binding affinity ( ΔGtarget ) across all accessible DNA sites , which would explicitly account for non-canonical PAMs as well as sequence- and distance-dependent mismatches . Third , several crRNAs may be designed and co-expressed to cleave the same genomic locus or to regulate the same promoter and thereby increase ( d ) Cas9 activity . According to our model , the occupancy of multiple Cas9:crRNA complexes at both on-target and off-target sites will be additive and independent so long as Cas9 expression is increased proportionally with the expression of additional crRNAs and when the on-target binding sites are separated by at least 200 bp to minimize the site-to-site effects of positive DNA supercoiling . Finally , our modeling and experimental results have several implications when using dCas9-based gene regulation to engineer synthetic genetic circuits . First , extremely low crRNA expression levels are sufficient to form enough active dCas9:crRNA complexes to efficiently repress transcription because bacteria have small genomes and a low number of off-target sites . Further increases in crRNA expression had only a 3 . 7-fold change in transcriptional regulation as we observed in our reporter protein measurements ( Fig 3 ) . In other words , there are not enough DNA sites in bacterial cells to “sponge up” excess amounts of dCas9:crRNA complex . To increase an “inverter” circuit’s dynamic range , we showed that adding auxiliary on-target DNA sites on a high copy R6K plasmid will sequester dCas9:crRNA and shift the sigmoidal relationship between crRNA expression level and output promoter transcription rate . Adding either 2 or 4 auxiliary binding sites per plasmid ( about 300 or 600 sites total ) increased the circuit’s dynamic range by 27- or 11-fold . Second , mismatches can be purposefully introduced into on-target DNA sites to control binding occupancy , and therefore control transcriptional regulation . According to our model , a mismatch in the first 8 bp PAM-proximal region will ( on average ) increase ΔGtarget by 0 . 78 kcal/mol and lower the binding occupancy of the dCas9:crRNA by 3 . 7-fold . Incorporating more mismatches will increase ΔGtarget additively and decrease binding occupancy in a multiplicative manner . Third , when several crRNAs are expressed , they will competitively bind to dCas9 to form different dCas9:crRNA complexes , causing the increased expression of one crRNA to lower the concentration of another Cas9:crRNA complex . Such non-orthogonal relationships are generally undesired when engineering digital genetic circuits , and can be alleviated by expressing dCas9 in proportion to the total crRNA level . However , mutual dependence between dCas9:crRNA activities may be productively used to engineer analog signal processing circuits . Fourth , the effects of DNA supercoiling will have an impact on genetic circuit function . For example , computations using several input signals can be performed by co-regulating the same output promoter using different dCas9:crRNA complexes at adjacent crRNA binding sites . Even though the crRNA binding sites are adequately spaced apart to prevent steric interactions , site-to-site DNA supercoiling will inhibit the binding of one dCas9:crRNA when another has already bound , for example , by 11-fold when there are 4 nearby auxiliary sites . This anti-cooperative mechanism should be taken into account when engineering such “fan-in” genetic circuits .
Mature crRNA guide strands can be expressed in two ways: transcription of a single chimeric synthetic guide RNA ( sgRNA ) that contains the 5' target recognition region , followed by a conserved Cas9-binding hairpin [43]; or transcription of a precrRNA array and a tracrRNA that form an RNA duplex that is subsequently processed by RNAse III into a mature crRNA [2 , 65] . As a key difference , the precrRNA can contain multiple target recognition sequences , each separated by a repetitive spacer sequence . The tracrRNA binds to these repetitive spacers and forms a double-stranded complex with precrRNA , becoming a target for RNAse III cleavage [2 , 65 , 66] . The resulting RNAse processing can generate multiple mature crRNAs from a single precrRNA . Cas9 may bind with the tracrRNA before landing on the precrRNA , and facilitate the tracrRNA:precrRNA hybridization [65] . After the mature crRNA is loaded into Cas9 , an unidentified RNA exonuclease trims its 5' end , leaving a target recognition sequence of about 20 nucleotides [46 , 66] . When not bound to a crRNA , wild-type Cas9 remains in a structural conformation that inhibits its cleavage activity [67] . During the crRNA loading process , Cas9 undergoes a rotational shift that exposes a DNA binding channel , yielding an active Cas9:crRNA complex . In our model , we first introduce the production rates of mature crRNA guide strands ( rcrRNA ) and Cas9 proteins ( rCas9 ) as zero order reactions . These production rates can be varied by altering the DNA copy numbers or transcription rates of the precrRNA , sgRNA , or Cas9 as well as the translation rate of Cas9's mRNA [5 , 49] . We then employ mass action kinetics to describe the irreversible formation of an intermediate Cas9:crRNA complex , followed by an irreversible isomerization reaction that produces an active Cas9:crRNA complex . The rate of intermediate complex formation is quantified using a second order kinetic constant kf and the isomerization reaction's rate is quantified using a first order kinetic constant kI . As first order reactions , the crRNA , Cas9 , and intermediate Cas9:crRNA complex degrade or become diluted at a rate quantified by the kinetic constants δcrRNA , δCas9 , and δCas9:crRNA . Finally , the rate of target binding for each active Cas9:crRNA complex is designated rbinding , and will be derived below . The resulting differential equations ( Eqs 1–4 ) describe the dynamics of Cas9 and crRNA expression and active complex formation in terms of their molecular counts , assuming that the cell has a constant volume . For our first biophysical model of the CRISPR/Cas9 system , we have ignored the effects of stochastic gene expression as well as the effects of discrete cellular division . In addition , to account for the production of multiple crRNA guide strands with different sequences , we expanded the system of differential equations by an index i to describe their production , active complex formation , and rate of target binding . We assumed that all expressed crRNA guide strands bind equally well to Cas9 , and form active complexes at the same rate , with the same kinetic parameters ( kf and kI ) . However , through competitive binding , the fraction of Cas9 bound to each crRNA guide strand will depend on the crRNAs' differing expression levels . The rates of Cas9-dependent cleavage will also differ across different crRNA guide strand sequences ( index i ) as well as different DNA site sequences ( index j ) , designated by rC [i , j] . Once formed , active Cas9:crRNA complexes do not undergo facilitated diffusion or hopping , but instead engage in three-dimensional molecular diffusion to search for DNA sites [38] . The rate of diffusion is governed by the diffusivity of the Cas9:crRNA complex ( D ) , and also several host-specific factors , including the volume of the compartment ( V ) and the characteristic length between sites of production and binding ( λ ) . Here , we assume that the cellular compartment is well-mixed such that the rate of net molar flow is zero , though the time required for a Cas9 protein to find a target DNA site depends on the rate of molecular diffusion . Accordingly , the rate of molecular diffusion for active Cas9:crRNA complexes using the ith crRNA guide strand ( rRW , i ) will be proportional to its concentration [68]: rRW , i=6Dλ NCas9:crRNA , iV ( 5 ) Eq ( 5 ) is the molar flow rate , or contact rate , between active Cas9:crRNA complexes and all possible DNA sites inside the cell . We then use the sequences of the crRNA guide strand and the DNA site to calculate the probability that , once contact has been made , the active Cas9:crRNA complex binds to the DNA site and forms a stable Cas9:crRNA:DNA complex , called an R-loop . The rate of binding of the ith Cas9:crRNA complex to the jth DNA site is simply the product of the contact rate and the binding probability ( P[i , j] ) : rbinding , [i , j]=P[i , j]rRW , i ( 6 ) To calculate this binding probability , we assume that the pool of active Cas9:crRNA complexes have reached chemical equilibrium with the pool of both on-target and off-target DNA sites . This assumption is valid because the number of potential DNA sites is always much larger than the number of Cas9:crRNA complexes . In addition , when the Cas9:crRNA levels have reached steady-state conditions , the system will become ergodic . Accordingly , we derive a partition function in terms of the ith active Cas9:crRNA complex's binding free energy to the jth DNA site sequence ( ΔGtarget , [i , j] ) as well as the number of accessible DNA sites with the jth sequence ( Ntarget , j ) . Here , our reference state is a DNA sequence that binds non-specifically to Cas9:crRNA with a zero binding free energy . As the total number of non-specific DNA binding sites , we use twice of the host's genome length N . The binding probability will follow a Boltzmann distribution , and we may use both the reference state and partition function as normalization factors to calculate the probability that the ith Cas9:crRNA complex binds successfully to the jth DNA site: P[i , j]=Ntarget , jNexp ( −ΔGtarget[i , j]kBT ) 1+∑mNtarget , mNexp ( −ΔGtarget[n , m]kBT ) ( 7 ) Together , Eqs 6 and 7 provide a systematic approach for comparing the rates of binding for different crRNA sequences . Our next step was to develop a sequence-dependent free energy model to calculate and predict these binding rates for any crRNA guide strand sequence . The binding free energy of an active Cas9:crRNA complex to a particular DNA site controls its binding occupancy , and ultimately , its cleavage rate . Several interactions control the magnitude of this binding free energy , including the presence of a protospacer adjacent motif ( PAM ) site , the rate of R-loop formation during a multi-step exchange reaction , and the effects of supercoiling at the DNA site . Here , we employed thermodynamics to quantify the energetics of these interactions and developed a multi-term free energy model that calculates ΔGtarget [i , j] for different crRNA guide strand sequences , DNA site sequences , canonical and non-canonical PAM sequences , and varying amounts of DNA site supercoiling . Altogether , the free energy model sums together the strengths of these interactions , according to: ΔGtarget[i , j]=ΔGPAM , j+ΔΔGexchange[i , j]+ΔΔGsupercoiling , j ( 8 ) Next , we describe the mechanism of R-loop formation and how these interactions' free energies are quantified . After contacting a DNA site , a Cas9:crRNA complex recognizes and binds to the PAM sequence [35 , 69] . The canonical PAM site for the Cas9 from Streptococcus pyogenes is NGG , though additional non-canonical sequences have also been recognized [3 , 4 , 8 , 37] . The Cas9:crRNA complex then pulls apart the double-stranded DNA upstream of the PAM sequence , which is an energetically intensive process . Cas9 does not hydrolyze an energy-providing cofactor , such as ATP or GTP . Instead , its only significant source of external energy input originates from the binding interactions between the Cas9 protein and the PAM recognition sequence [38] , which we designate as ΔGPAM . As we show below , the most canonical PAM recognition sequence has an apparent ΔGPAM of about -9 . 5 kcal/mol , which is sufficient to pull apart four G:C or eight A:T DNA base pairings . Non-canonical PAM sequences have less energetically favorable interactions with Cas9 , but can still support R-loop formation and cleavage [3 , 37 , 44 , 49] . The Cas9:crRNA complex continues to pull apart double-stranded DNA by performing an exchange reaction , allowing the crRNA guide strand to form RNA:DNA base pairings with its complementary DNA strand [38 , 44 , 70] . In step-wise transitions , each DNA base pair is pulled apart , and the corresponding nucleotide from the crRNA binds to form a Watson-Crick base pair , resulting in the formation of a DNA:Cas9:crRNA:DNA sandwich , called an R-loop . R-loop formation is directional and sequential , beginning at the PAM site , and proceeding upstream . Before the R-loop is completed , strand displacement can stall and reverse , resulting in Cas9:crRNA dissociation , whenever the DNA:DNA complex becomes more stable than the DNA:Cas9:crRNA:DNA complex . We designated this difference in stability as ΔΔGexchange; if ΔΔGexchange becomes positive and large , the R-loop can not successfully form . To investigate whether Cas9 plays a role in target specificity , we then developed and parameterized two versions of a free energy model to calculate ΔΔGexchange for a given crRNA and DNA site sequence , where the first model incorporates only nucleic acid interactions , while the second model accounts for both nucleic acid and Cas9-dependent interactions . In the first model version , when the crRNA and DNA site are fully complementary , ΔΔGexchange is governed by the difference in free energy between the RNA:DNA duplex and its corresponding DNA:DNA duplex . Interestingly , this difference is free energy is sequence-dependent; for example , the binding free energy of the dinucleotide base pair rAC:dGT is 1 . 0 kcal/mol more stable than dAC:dGT , while the binding free energy of rCG:dCG is 1 . 6 kcal/mol less stable than dCG:dCG [53] . These nearest-neighbor free energies are designated as ΔGRNA:DNA and ΔGDNA:DNA , and may be calculated using previously developed free energy models that have been parameterized using calorimetry measurements [52 , 53 , 54 , 55] . Second , because of the sequential nature of R-loop formation , when the crRNA has non-complementary bases with the DNA site , the effect of the resulting mismatches will depend on their distance from the PAM site . For simplicitly , we introduce a position-dependent multiplicative weight dk that modulates the impact of these free energy differences . k is location and varies from 0 to the crRNA guide strand's length; the value of d1 will be larger than d20 . Therefore , our first approach for calculating ΔΔGexchange compares the thermodynamic stability of the ith crRNA:DNA complex to the stability of the jth DNA:DNA duplex , using the following expression: ΔΔGexchange[i , j]=∑kdk[ΔGk , k+1RNA:DNA−ΔGk , k+1DNA:DNA] ( 9 ) where the summations proceed over the lengths of the crRNA:DNA and DNA:DNA sequences . Eq ( 9 ) has 21 unknown dk parameter values and uses dinucleotide free energies that were previously parameterized in the absence of Cas9 [52 , 53 , 54 , 55] . However , it is possible that the Cas9 protein alters the stability of the R-loop in a sequence-specific fashion . To investigate this possibility , our second approach to calculating ΔΔGexchange is to formulate an entirely empirical nearest-neighbor model , which enumerates all possible dinucleotide RNA:DNA duplexes and mismatches together with the distance-dependent coefficients , resulting in 277 unknown parameters . In the result section below , we determined these parameter values using thousands of experimental measurements of off-target and on-target Cas9 activity . Once parameterized , the following expression is used to calculate ΔΔGexchange for any crRNA and DNA site sequence: ΔΔGexchange[i , j]=∑kdkΔΔGk , k+1Cas9:crRNA:DNA ( 10 ) where the summation proceeds over the length of the crRNA:DNA sequence . In the results section , we systematically compared the accuracy of these two models to quantitatively determine Cas9's effect on DNA site specificity . Next , we incorporated the effects of DNA site supercoiling into the model of Cas9:crRNA's binding energetics . Negative supercoiling , the untwisting of helical DNA , increases the stability of an R-loop by lowering the stability of the competing DNA:DNA complex [44]; however , there is a free energy input to form supercoiled DNA . When relaxed B-form helical DNA of length n is ( un ) twisted by 10σ turns , the change in free energy will be ΔGsupercoiling = 10nσ2kbT , where σ is the superhelical density , kb is the Boltzmann constant , and T is temperature [71] . Due to the activity of topoisomerases and gyrases inside cells , the superhelical density of bacterial and human genomic DNA varies between σ = -0 . 02 and -0 . 1 , depending on the location's distance from the origin of replication and its proximity to highly transcribed genes [72 , 73] . If a DNA site has already been negatively supercoiled by the host’s native enzymes , then a free energy input is not needed to stabilize the R-loop . However , if the DNA site is relaxed or positively supercoiled , then the additional free energy needed to untwist it will increase the dissociation rate of the Cas9:crRNA complex as it forms the R-loop . Accordingly , the dissociation kinetic constant of the Cas9:crRNA complex will depend on the degree of DNA site supercoiling according to kd , j=kd*exp ( −ΔGsupercoiling , j/kbT ) ( 11 ) where we determine the free energy input needed to untwist the DNA site by comparing the superhelical density of an R-loop in its final state ( σF ) with the initial superhelical density of the DNA site using ( σI ) the expression . ΔGsupercoiling , j=−10nkbT ( σF2−σI2 ) ( 12 ) The change in supercoiling energy in Eq 7 is a result of binding to a target from a non-specific site . Therefore , the energy term must be calculated based on the change in superhelical density of these targets ( Eq 13 ) . An average superhelical density of -0 . 06 for all nonspecific binding sites ( σNS ) has been previously reported for E . coli genome [74 , 75] . After the R-loop has formed , the DNA:Cas9:crRNA:DNA complex has the ability to cut the DNA strands , one at a time , typically at the third nucleotide upstream of the PAM site [4] . As measured by a time-course cleavage assay , an appreciable amount of nicked DNA accumulates before double-stranded DNA breaks are observed , indicating that Cas9’s endonuclease reaction is a slow , rate-limiting step . Unlike most enzymes , after Cas9 has doubly cut its DNA site , the Cas9:crRNA complex remains stably bound to the DNA site and does not have the ability to cleave DNA at another site [38] . This absence of turnover causes Cas9 to become a limiting reactant . However , before Cas9 has doubly cut its DNA site , optical trap pulling experiments have shown that the formation of the R-loop is reversible and that the DNA:Cas9:crRNA:DNA complex can dissociate [44] . In light of these two competing pathways , we derived an expression for the cleavage rate of the ith Cas9:crRNA complex bound to the jth DNA site: rC[i , j]=kckc+kd , jrbinding[i , j] ( 14 ) where the rate of cleavage is controlled by a first-order kinetic constant kC and the effects of DNA supercoiling on the dissociation kinetic constant , kd , are determined using Eq 11 . Finally , we calculate the total numbers of free , bound , and cut DNA sites over time by accounting for the production of DNA sites via DNA replication and their consumption by Cas9-based cleavage . Initially , the host organism begins with Ntotal , j copies of an accessible DNA site ( type j ) . For chromosomally encoded DNA sites , Ntotal , j will vary between 0 and 2 , depending on their distance from the chromosome's origin of replication and whether the site is located within accessible euchromatin or inaccessible heterochromatin . For plasmid-encoded DNA sites , Ntotal , j is the plasmid’s DNA copy number . After Cas9 binds and cleaves a DNA site , we assume that Cas9 remains bound to the site . After cleavage , the rate of DNA repair via homologous recombination or non-homologous end-joining will depend on several factors , for example , the host organism and the concentration of the repair DNA template . Here , we assume that the rate of DNA repair is proportional to the number of cut DNA sites . We also assume that , in actively growing cells , the replication rate of DNA sites is the cell’s division rate , designated as μ . Once a newly available DNA site has been replicated , it is distributed to daughter cells during division . Therefore , the net production rate of available DNA sites is the cell’s growth rate multiplied by the number of cleaved DNA sites , which is equivalent to μ ( Ntotal , j—Ntarget , j ) , where Ntarget , j is the number of unbound DNA sites . Together , the rate of DNA replication and Cas9-dependent cleavage determines the total number of cut and uncut DNA sites within the organism , according to: dNtarget , jdt=μ ( Ntotal , j−Ntarget , j ) −∑irC[i , j] ( 15 ) Altogether , for a genetic system that expresses η crRNAs in a host with ζ available DNA sites , the formally complete biophysical model of CRISPR/Cas9 consists of 3η + ζ ( η+1 ) + 1 ordinary differential equations , which can be a large number . With further time-scale analysis that distinguishes between on-target and off-target DNA sites , there are several options for greatly reducing the number of partition function calculations and differential equations to determine the fraction of DNA sites that are free , bound , or cut . In one example , in early time periods , the low cleavage rates for the off-target DNA sites causes their differential equations to be well-approximated as linear , as compared to the highly coupled and non-linear differential equations for the on-target DNA sites . The analytical solutions to the differential equations for the off-target sites can then substituted into the numerical integration of the on-target DNA sites’ differential equations . In another example , determining the steady-state numbers of on-target and off-target DNA sites requires the solution of a system of multivariate quadratic polynomials , which can be efficiently computed using an iterative hybrid Krylov method [76] . With the availability of such analytical and numerical approximations , it is possible to solve the complete model using a mammalian genome without computational intractability , though an analysis to find the best approximation remains a topic for a future study . When a Cas9:crRNA complex binds to a DNA site , the formation of the R-loop will result in positive supercoiling of the surrounding DNA sites , due to conservation of the DNA linking number in the absence of topoisomerase or gyrase activity [77] . Positive supercoiling of DNA will alter the affinities of DNA-binding proteins , such as RNA polymerase [78] or other Cas9:crRNA complexes . These longer-range effects become important when crRNAs are designed to bind to several nearby on-target DNA sites , for example , when targeting two different DNA sites with a chimeric dCas9-FokI fusion [39] , when inserting recombinant DNA between two nicked or doubly cleaved DNA sites , or when using dCas9 to regulate the transcription rate of a promoter using multi-input logic . Whenever multiple on-target DNA sites are adjacently located , we therefore modified the free energy model for ΔGtarget to incorporate the site-to-site effects of supercoiling . Consider multiple DNA sites located within a short segment of DNA surrounded by a type of fixed end , for example , between two active promoters , DNA replication origins , or other sites where DNA-binding proteins constrain DNA topology . When Cas9:crRNA binds to one of these DNA sites , the unwinding of the DNA site during R-loop formation increases the superhelical density of the remaining DNA segment by an amount Δσ ( more positive ) , which depends on the lengths of the DNA site and the DNA segment . With the increase in supercoiling from σj to σj + Δσ ( from negative to less negative ) , Cas9:crRNA will require an additional free energy input to bind to the remaining DNA sites within the segment and form an R-loop , according to Eq ( 12 ) . As more DNA sites are bound by Cas9:crRNA , we assume that the linking number is conserved , yielding an increase in superhelical density from σj to σj + c Δσ for c bound DNA sites . Eventually , the free energy needed to stabilize the R-loop will become sufficiently large to prevent Cas9:crRNA from binding additional DNA sites within this DNA segment . According to our calculations below , Δσ is about 0 . 0065 . To calculate these binding probabilities , we modified the partition function in Eq ( 7 ) , accounting for the combinations of states where Cas9:crRNA has bound c adjacent DNA sites with their corresponding supercoiling-dependent energy penalties . There are additional factors , not included within this model , that can affect Cas9's ability to recognize and bind crRNAs as well as cleave DNA sites . Outside of the crRNA guide sequence , the tracrRNA and sgRNA form four stem loop structures that are responsible for recognizing and binding to Cas9 [43] . While the third and fourth stem loops are not essential for recognition , truncation of these structures did reduce the stability of the Cas9:crRNA complex . In another study , it was observed that truncated sgRNAs resulted in lower cleavage rates at both on-target and off-target DNA sites , which suggests that there were either fewer active Cas9:crRNA complexes or that active complexes had lower intrinsic cleavage activities [33] . Here , the biophysical model assumes that the tracrRNA and sgRNA fold into the wild-type structure . Further , while Cas9 can bind well to both single- and double-stranded DNA , its cleavage rate is significantly reduced when bound to single-stranded DNA or a truncated double-stranded DNA site [38] . The current biophysical model only considers double-stranded DNA sites within long contiguous DNA , such as plasmids and genomes . Overall , the developed mechanistic model can estimate the probability of binding and cleavage for any Cas9 target DNA . In addition to degradation rate of all the involved molecules ( δi ) , the final model’s parameters are kf , kI ( complex formation step ) , ΔGPAM , ΔΔGexchange , ΔGsupercoiling , kd , c ( stabilizing target binding ) , and kc ( cleavage step ) . The input parameters are the exposure time ( t ) and the production rate of Cas9 ( rCas9 ) and crRNA ( rcrRNA ) . For a system containing 1 type of crRNA and N on- and off-targets , the concentrations of free Cas9 , crRNA , intermediate complex , free active Cas9:crRNA complex and the targets are unknown , and can be calculated by solving N+4 ordinary differential equations ( Eqs 1–4 , 14 and 15 ) simultaneously . In the following sections , we have used multiple in vivo and in vitro measurements to estimate the model parameters in different conditions . A summary of the studies and the utilized data is provided in Table 1 . Differential equations were numerically integrated using a variable-order , adaptive time-stepping stiff numerical solver ( ode15s ) in MATLAB . For comparison to experimental measurements , the relative errors between model solution and experimental measurements were calculated over the measurements' time interval or after a steady-state condition was reached . In Table 1 , we summarize the several types of experimental measurements used to parameterize and validate the model , including the number of degrees of freedom and the number of data-points in each experimental data-set . To identify a narrow range of best-fit parameter values , model parameterization was performed by using either a simple simplex method ( fminsearch ) or a Levenberg-Marquardt method ( lsqnonlin ) in MATLAB to minimize the sum of squared relative errors , followed by a parameter sensitivity analysis and visual comparisons to more precisely identify best-fit model parameters . To validate model predictions , we constructed three plasmids that employ dCas9 to transcriptionally regulate expression of a reporter protein . The first plasmid expresses the YFP fluorescent protein reporter on a R6K vector using a KanR antibiotic marker . The YFP expression cassette contains a σ70 promoter ( J23100 ) , a synthetic ribosome binding site designed by the RBS Calculator [79 , 80 , 81] , a codon-optimized YFP coding sequence , and an efficient transcriptional terminator [82] . A primary crRNA binding site is located within the promoter region with the sequence ( 5'—TATCGTTAAGGTTACTAGAG—3' ) . Where noted , between one to eight auxiliary crRNA binding sites with the same sequence as the primary crRNA binding site , each separated by 80 nucleotides of randomized DNA , were inserted downstream of the transcriptional terminator . To insert auxiliary binding sites , gBLOCK DNA fragments ( Integrated DNA Technologies ) were synthesized and assembled with a digested vector fragment using T4 ligation . The second plasmid constitutively expresses Cas9 and tracrRNA on a p15A vector using an AmpR antibiotic marker . Plasmid construction was performed by PCR-amplifying the Cas9 and tracrRNA expression cassettes from the pdCas9 plasmid [8] and assembling with a PCR-amplified p15A vector fragment using Gibson's method [83] . The third plasmid expresses the precrRNA using an IPTG-inducible Ptac promoter on a ColE1 vector using a CmR antibiotic marker , and was constructed by PCR-amplifying the precrRNA cassette from pdCas9 and assembling it with a PCR-amplified ColE1 fragment using Gibson's method . The precrRNA contains two BsaI sites flanking the protospacer region , which were utilized to insert new crRNA guide sequences into the precrRNA with digestion and ligation of annealed oligonucleotides . Cloned plasmids were verified by sequencing . The three plasmids were electroporated together into E . coli pir116 , and selected on triple antibiotic agar plates . Transformed strains were grown overnight at 37°C and 200 RPM in LB Miller supplemented with 10 μg/ml chloramphenicol , kanamycin , and ampicillin ( Sigma-Aldrich ) . 5 μl of cultures were diluted into fresh selective media in a 96-well microplate , incubated , and shaken at 37°C in a TECAN M1000 spectrophotometer . Serial dilutions were performed twice to maintain cells in the exponential phase of growth for a 12 hour period . 10 μl samples were extracted after the second and third serial dilutions and added to 200 μl Phosphate buffered saline ( PBS ) supplemented with 2 mg/mL kanamycin for halting growth . Single-cell YFP fluorescence from at least 20 , 000 cells were recorded by an BD Fortessa flow cytometer . The average YFP expression level was determined by taking the average of the fluorescence distribution and subtracting the average auto-fluorescence of E . coli pir116 . | The CRISPR/Cas9 immunity system has the potential to revolutionize medicine and biotechnology by enabling researchers to cut an organism’s genomic DNA at precise locations . While Cas9 is perhaps the most versatile and easy-to-use technique for gene therapy developed yet , it is not perfect; the enzyme can also cut DNA at unwanted locations in an organism’s genome . Cas9’s off-target activity must be greatly minimized to further improve its utility . Here , we develop a system-wide , quantitative , physical model to better understand all the factors that collectively control Cas9’s off-target cleavage . We solve for the unknown parameters using gene regulation data from our laboratory as well as structural , biochemical , and next-generation sequencing data from other laboratories . Using the model in several examples , we explain how Cas9 identifies on-target versus off-target DNA sites , depending on the guide RNA sequence , the Cas9 and crRNA expression levels , the organism’s genome , and the organism’s cellular growth rate . We then propose several rules for designing experiments with minimal off-target activity . | [
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] | 2016 | A Biophysical Model of CRISPR/Cas9 Activity for Rational Design of Genome Editing and Gene Regulation |
Prochlorococcus , an extremely small cyanobacterium that is very abundant in the world's oceans , has a very streamlined genome . On average , these cells have about 2 , 000 genes and very few regulatory proteins . The limited capability of regulation is thought to be a result of selection imposed by a relatively stable environment in combination with a very small genome . Furthermore , only ten non-coding RNAs ( ncRNAs ) , which play crucial regulatory roles in all forms of life , have been described in Prochlorococcus . Most strains also lack the RNA chaperone Hfq , raising the question of how important this mode of regulation is for these cells . To explore this question , we examined the transcription of intergenic regions of Prochlorococcus MED4 cells subjected to a number of different stress conditions: changes in light qualities and quantities , phage infection , or phosphorus starvation . Analysis of Affymetrix microarray expression data from intergenic regions revealed 276 novel transcriptional units . Among these were 12 new ncRNAs , 24 antisense RNAs ( asRNAs ) , as well as 113 short mRNAs . Two additional ncRNAs were identified by homology , and all 14 new ncRNAs were independently verified by Northern hybridization and 5′RACE . Unlike its reduced suite of regulatory proteins , the number of ncRNAs relative to genome size in Prochlorococcus is comparable to that found in other bacteria , suggesting that RNA regulators likely play a major role in regulation in this group . Moreover , the ncRNAs are concentrated in previously identified genomic islands , which carry genes of significance to the ecology of this organism , many of which are not of cyanobacterial origin . Expression profiles of some of these ncRNAs suggest involvement in light stress adaptation and/or the response to phage infection consistent with their location in the hypervariable genomic islands .
Cyanobacteria are a diverse group of photoautotrophic bacteria that occupy a broad range of habitats , including the oceans , lakes , and soil , and are also found as symbionts in many different types of organisms . Prochlorococcus , a member of the cyanobacterial lineage , often accounts for up to 50% of the photosynthetic biomass in the open oceans between 40°N and 40°S [1] , [2] . In these areas Prochlorococcus numerically dominates the phytoplankton with cell numbers reaching 105 cells per mL [3] . Two major ecotypes can be differentiated within the Prochlorococcus group , which are relatively adapted to high or low light . They are genetically and physiologically distinct [4] and are distributed differently in the water column [5]–[7] , with the high light adapted cells dominating the surface waters , and the low light adapted cells abundant in deep waters . The genomes of 12 Prochlorococcus strains , spanning the known microdiversity within the group , have been sequenced ( http://www . ncbi . nlm . nih . gov/genomes/MICROBES/microbial_taxtree . html ) . The cells posses the most streamlined genome of a free-living photoautotroph with genome sizes ranging from 1 . 6 Mbp to 2 . 7 Mbp [8]–[10] . The number of modelled protein-coding genes in these genomes is 1 , 855–3 , 022 [9] and the core genome shared by all Prochlorococcus strains has been estimated at 1 , 273 genes [9] . Several hundred additional genes are specific for one or only a few strains , and they are frequently clustered in genomic islands [11] , [9] . Genome reduction in this genus has particularly affected the number of regulatory genes . Many otherwise widely distributed two-component systems and DNA-binding proteins are not present in Prochlorococcus . This has been linked to the fitness gain conferred by a streamlined genome to organisms existing in a relatively stable environment [8] . Although the ocean environment may be relatively stable , it does fluctuate , making one wonder how Prochlorococcus cells respond to these changing conditions . Perhaps each protein regulator performs multiple regulatory functions in this cell . Alternatively , non-coding RNAs ( ncRNAs ) may play a major regulatory role compensating the lack of regulatory proteins . ncRNAs are functional RNA molecules , mostly without a protein-coding function , and their genes are normally located in intergenic regions . They frequently play a crucial role in bacterial regulatory networks particularly in response to environmental stress [12] , [13] and are also known to control plasmid and viral replication [14] , bacterial virulence [15] and quorum sensing [16] . However the function of many ncRNAs remains unknown . Escherichia coli has over 70 ncRNAs most of which have been detected by computational prediction [17]–[20] and “experimental RNomics” [21]–[23] . These regulators were overlooked by traditional genome annotation due to their short length ( 50–400 nt in size ) , the lack of algorithms to search for sequences that are frequently more conserved in secondary structure rather than sequence , and the absence of a protein coding function . Another class of functional RNAs – chromosomally encoded antisense RNAs ( asRNAs ) also plays a role in the regulation of gene expression . There are no systematic approaches to screen for asRNAs , but RNomics approaches have inadvertently revealed the presence of asRNAs in Escherichia coli [21]–[23] . These cis-encoded asRNAs are transcribed from the opposite strand of the same genomic locus as the target ( m ) RNA and feature 100% base complementarity . In contrast , most ncRNAs studied so far act in trans in a different genomic locus having only a short and imperfect base complementarity with the target transcripts ( for a detailed review see [24] ) . Although a considerable number of Prochlorococcus strains have been fully sequenced , only a small number of ncRNAs have been identified in this group . In addition to the ubiquitous signal recognition particle RNA , RNAse P RNA and the tmRNA , encoded by ffs , rnpB and ssrA , seven ncRNAs have been identified in cyanobacteria , all of which were first described in Prochlorococcus MED4 and were denoted as Yfr1–Yfr7 , for cYanobacterial Functional RNA [25] . Amongst them is Yfr7 , which is homologous to 6S RNA [26] and known to have global regulatory functions in Escherichia coli . Another is Yfr1 , which has homologues in other cyanobacteria [27] and in Synechococcus elongatus PCC6301 , is required for growth under multiple stress conditions [28] . These two ncRNAs were classified as such after experimental verification of the expression of candidate ncRNAs initially identified from secondary structure conservation using a comparative genomics approach . Little is known regarding cis-acting asRNAs in cyanobacteria . Only 3 chromosomally cis-encoded asRNAs have been identified so far [29]–[31] , none of which occur in Prochlorococcus . Despite the presence of ncRNAs in Prochlorococcus the gene encoding the Hfq RNA chaperone is absent from 10 of the 12 sequenced Prochlorococcus strains , including MED4 . This is in contrast to other completely sequenced cyanobacteria that all contain an Hfq homologue . Hfq belongs to the eukaryotic and archaeal family of Sm and Sm-like ( Lsm ) proteins and is found in all domains of life . It facilitates the interaction of ncRNAs with their target mRNAs and is thus involved in many essential regulatory processes including ncRNA-mediated translational regulation [32]–[35] . Its loss during evolution of the Prochlorococcus group may be taken as evidence for a general decay in RNA-dependent gene regulation or as an indication that novel mechanisms for RNA - RNA interactions may exist in this group . In the past few years , new experimental strategies such as ‘experimental RNomics’ and mining microarray expression data in intergenic regions have demonstrated that the number of ncRNAs in microbial genomes is much greater than previously thought ( for reviews see [36] , [37] ) . In the light of the small number of ncRNAs detected thus far in Prochlorococcus we were curious to see whether more ncRNAs are present in Prochlorococcus than were detected in the comparative genomics analysis used by Axman et al . [25] . Using an alternative approach based on microarray expression profiling , we investigated the presence of ncRNAs in Prochlorococcus MED4 , which has the most compact genome of all sequenced Prochlorococcus strains , has few protein coding regulators , and is Hfq-deficient .
The design of the Prochlorococcus custom Affymetrix microarray , which contains probes not only in gene-coding regions but also in intergenic regions ( on both strands ) , and the availability of diverse data sets describing changes in gene expression in response to environmental stresses , allowed us to undertake a focused study of transcriptionally active intergenic regions . Three independent data sets were used: experiments investigating global changes in gene expression under different light quantities and qualities ( from here on referred to as the “light experiment” , [38] ) , under phage infection ( the “phage experiment” , [39] ) , and under phosphorus starvation ( the “phosphorus experiment” , [40] ) , encompassing a total of 95 microarrays . To designate expression signals as novel transcripts , probes had to be above a threshold expression level , and be further than 100 nt from flanking genes ( see Materials and Methods for details ) . After identifying 553 probes that met these criteria from the light , phage and phosphorus experiments , we combined adjacent probes , yielding 276 unique transcriptional units ( Figure 1 ) . These transcripts were classified as 5′-UTRs , 3′–UTRs , operon elements , pseudogenes and “other” transcripts based on their genome location and experimental information . The “other” transcripts were then classified: as i ) ORFs if they had a protein-coding reading frame with a start and stop codon without a frame shift in Prochlorococcus MED4 and in the genome of at least one other Prochlorococcus strain; ii ) ncRNAs if they lacked an ORF , but had structural features typical of ncRNAs , such as compensatory mutations; and iii ) asRNAs if they were located on the opposite strand of mRNAs . No assignment was made for 89 transcripts which either could not be verified in independent experiments ( see below ) or did not have homologues in other genomes . While some of these unclassified transcripts may represent unverified small ORFs or ncRNAs , they may also be the result of artificial expression signals . These may have occurred due to: i ) cross-hybridization with duplicated regions that have only small sequence differences; or ii ) artificial antisense signals caused by self-priming through hairpin loop extension of the first-strand cDNA , re-priming either from RNA fragments formed during degradation of the RNA templates , or from primers present in the reaction [41] . Twelve novel ncRNAs were identified through microarray analyses . They were verified in independent experiments by rapid analysis of cDNA 5′ ends ( 5′ RACEs ) and Northern hybridizations , which also served to map their first nucleotide and estimate their lengths ( Table 1 , Figure 2 ) . In addition , 5 of the 7 previously described ncRNAs ( Yfr2 and Yfr4–Yfr7 , [25] ) were also detected . Yfr1 may not have been detected due to its extraordinary small size ( 54 nt ) which may have resulted in its removal during the cDNA clean-up process . Regardless , Yfr1 would be excluded from our analysis because of its close proximity to an annotated protein-coding trxA gene ( within 100 nt ) that is transcribed in the same direction . Expression levels of Yfr3 were the lowest of the previously reported ncRNAs [25] , which likely explains why we did not detect this ncRNA in the microarray analysis . The internal consistency of these findings provide confidence in our approach , and suggest that even more ncRNAs may exist in Prochlorococcus , especially if they are very short , minimally expressed or close to protein-coding genes . Indeed two additional ncRNAs ( Yfr12 and Yfr18 ) are described below that were identified by sequence homology , and that were not found through the microarray analysis because their expression signal was below the set threshold value . Unlike Yfr1 , which a sequence motif-based approach [27] revealed has homologues throughout the cyanobacterial lineage , none of our newly detected ncRNAs were universally present among the cyanobacteria . Indeed BlastN analyses yielded no evidence for their existence outside of the Prochlorococcus genus . With the exception of Yfr13 , homologues of newly identified ncRNAs were only found in other high light-adapted Prochlorococcus strains ( Table 1 ) . Yfr13 has homologues in seven different Prochlorococcus strains , including the two low light-adapted isolates NATL1A and NATL2A , although the genome location is variable in the different strains ( Figure 3 ) . ncRNAs are non-randomly distributed within the MED4 genome . They are often associated with hypervariable genomic islands , thought to arise by horizontal gene transfer [11] . MED4 has 5 genomic islands that constitute only about one tenth of the total genome , whereas 9 of the 21 ncRNAs , described here or by Axmann et al . [25] , are in one of these islands ( Table 1 ) . The majority of island-associated ncRNAs are located in island 1 ( Yfr8–Yfr11 and Yfr2 ) . Three additional ncRNAs occur in island 2 ( Yfr15 , Yfr16 and Yfr3 ) and one is found in island 5 ( Yfr20 ) . Interestingly , the homologous ncRNAs in other Prochlorococcus strains are not always located in the corresponding island but occur somewhere else in the genome . The reverse is also true: some ncRNAs that are not island-associated in MED4 are located in an island region in other strains , indicative of recombination events . No ncRNAs were detected in island 4 even though this is the largest ( 74 . 5 kb long ) of all islands present in MED4 . Island 4 mainly encodes cell surface-relevant proteins such as glycosyltransferases or lipopolysaccharide-forming enzymes [11] , suggesting these functions are not controlled through ncRNAs . Yfr11 and Yfr16 are highly similar to each other . Based on their sequence identity ( 74% ) and their highly similar secondary structures ( Figure S1 ) both ncRNAs may regulate the same targets as has been shown for PrrF1 and PrrF2 in Pseudomonas aeruginosa [42] and for Qrr1 , Qrr2 , Qrr3 , and Qrr4 in several Vibrio species [16] . Alternatively , they might act in a related context but with non-identical functions as has recently been described for GlmY and GlmZ ncRNAs of Escherichia coli [43] . Alternatively , Yfr11 and Yfr16 could be functionally equivalent but expressed in a different regulatory context , as is frequently the case for protein-coding genes that occur in multiple copies in a single genome . Since the expression of many regulatory RNAs is coupled to the process they help regulate [12] , [44] , [45] , we explored the differential expression of the ncRNAs we identified as a function of different environmental stresses . The expression levels of several ncRNAs were influenced by light and phage induced stress , but not by phosphorus stress . Two ncRNAs – Yfr19 and Yfr11 were more than twofold downregulated after transfer from darkness to high white light , normal white light or blue light , but were upregulated when DCMU ( an inhibitor of the photosynthetic electron transport chain ) was added to cells grown in normal white light conditions ( Figure 4 , Table S1 ) . Expression of Yfr16 , the homolog of Yfr11 , followed the same trends , but was less pronounced than for Yfr11 . Both the reduced transcript levels during light exposure and the increased amount upon DCMU treatment indicate a link between the redox status of the photosynthetic electron transport chain and these three ncRNAs . High light induced differential expression in the largest number of ncRNAs , and of the highest magnitude , as has been observed for the response of protein-coding genes [38] . However , only a single ncRNA , Yfr20 , was upregulated when cells were transferred from darkness to high light ( Figure 4 , Table S1 ) , whereas all other ncRNAs responsive to light stress decreased in their transcript levels . Yfr20 accumulates in high absolute amounts ( Figure 2 ) . According to 5′RACE , the major accumulating transcript of 89 nt results from a specific initiation of transcription at position 1336435 ( accession number BX548174 . 1 ) . In addition 5′RACE results show that Yfr20 is transcribed together with the upstream located ORF PMED4_15791 as a dicistronic element . PMED4_15791 showed constitutive expression . Thus , the light-dependent expression of Yfr20 is under control of its own promoter . The dicistronic gene arrangement with an upstream located ORF is split in Prochlorococcus strains MIT9515 and MIT9312 and contains an additional hli gene in between the ncRNA and the ORF homologous to PMED4_15791 ( Figure 5 ) providing further evidence for a possible light-regulatory function of Yfr20 . Intriguingly , Yfr20 is the only ncRNA encoded in genomic island 5 . This island has been characterized as a “phosphorus” island in MED4 since nine genes ( nearly all of unknown function ) responded when MED4 cells were starved for phosphorus [40] . However , high light stress caused an additional 15 genes to respond in genomic island 5 [11] , among them hli11 and hli12 , which are located at a distance of less than 2 kb from yfr20 ( Figure 5 ) . Although hli ( high light inducible ) proteins can be factors in other stress responses as well , their mode of regulation here indeed suggests that this island plays a role not only in the adaptation to phosphorus starvation but also to stress caused by high light . Two distinct stress responses of the cell in response to phage infection have been identified . Lindell and co-workers [39] observed an overall reduction in expression of host genes as the major response to phage infection . However , 41 protein-coding host genes were upregulated in the initial or the mid-to-late phases of phage infection . It is hypothesized that genes belonging to group 1 ( the first wave of upregulation ) constitute a direct defence to phage infection whereas group 2 genes ( the second wave ) may be induced by the phage [39] . Two ncRNAs – Yfr9 and Yfr14 - were upregulated in the initial phase of infection ( from 1–3 hours after infection , corresponding to group 1 upregulated protein-coding genes [39] , Figure 4 , Table S1 ) . Interestingly , both ncRNAs have an antisense-located ncRNA – Yfr8 and Yfr6 – that are constitutively expressed during that time ( Figure 4 , Table S1 ) . The two pairs of overlapping ncRNAs are characterized in more detail below ( see section on overlapping ncRNAs ) . An additional ncRNA – Yfr15 - was upregulated during the mid to late phases of infection ( from 3 to 8 hours corresponding to group 2 upregulated protein-coding genes [39] , Figure 4 , Table S1 ) . Yfr15 is located in genomic island 2 in the vicinity of PMED4_07441 ( PMM0686 ) , the most highly upregulated host mRNA during phage infection , although the two genes are located on opposite strands . Also PMED4_07401 ( PMM0684 ) and PMED4_07421 ( PMM0685 ) , two further genes that belong to group 2 phage-induced host genes , are located nearby in genomic island 2 . This region and Yfr15 may therefore be of prime importance for phage-host interactions . We did not detect a single ncRNA that was significantly differentially expressed under phosphorus limitation . This was very surprising , in light of the 34 protein encoding-genes that are differentially expressed under P-stress in MED4 [40] , and because in Escherichia coli the existence of such ncRNAs was hypothesized based on the observation of Hfq-dependent regulation of rpoS in response to this stress [46] . The ncRNA Yfr10 contains the conserved unadecanucleotide motif 5′-ACUCCUCACAC-3′ ( Figure 6 ) . This motif occurs 3 times in the MED4 genome sequence , which is more frequent than expected by chance: One would expect approximately 0 . 5 instances of a specific 11 nt motif in a 2 MB genome at equal base distribution . The second occurrence has already been described as belonging to another ncRNA in MED4 , Yfr1 [27] , which is also found throughout the cyanobacterial radiation . [27] . Using Northern analysis and 5′RACE , we showed that also the third copy of this motif is expressed , revealing another ncRNA - Yfr18 ( Figure 2 , Table 1 ) . This one was not detected from our microarray analyses nor the comparative genomics approach [25] . If two base transitions are allowed there is even a fourth member of this ‘unadecanucleotide-containing’ class of ncRNAs in MED4 . This ncRNA - Yfr12 - was identified by sequence similarity to Yfr10 and was verified as an ncRNA as described above ( Figure 2 , Table 1 ) . Yfr12 appears to be a mutated variant of the other three , since the processed 5′ end of the major accumulating RNA species was mapped to the middle of the unadecanucleotide , and two mutations change the sequence at the 3′ end of the motif from CACACAC to CAUAUAC ( Figure 6 ) . Furthermore , the motif can be extended in all 4 ncRNAs by another AC dinucleotide ( Figure 6 ) in comparison to the published cyanobacterial consensus [27] , probably a peculiarity of these RNAs in Prochlorococcus . The functions of Yfr10 , Yfr12 and Yfr18 in MED4 remain unknown at present . However , a hint about their potential function may be found from their genome context and the fact that the vast majority of functional interactions between ncRNAs and their targets is exerted through base pairing . The genes for Yfr10 , Yfr12 and Yfr18 are each directly adjacent to those for ncRNAs Yfr2 , Yfr4 and Yfr5 respectively ( Table 1 ) , the 5′ ends of which may basepair to the 13-nucleotide consensus of Yfr1 , Yfr10 , Yfr12 and Yfr18 if a single bulging C and one mismatch is allowed ( i . e . 5′-aCUCCUcACACAC-3′ pairs with 5′-GUGUGUAGGAG-3′ ) . Moreover , one may note that also the two C – to - U transitions in Yfr12 are compatible with this suggested base pairing , and that secondary structure predictions suggest that the conserved motifs in Yfr1 , Yfr10 and Yfr18 are exposed as single stranded elements in an otherwise folded region ( Figure 6 ) . The same is true for the complementary motif in Yfr2–Yfr5 , making physical interactions very likely . Evidence for two regulatory RNAs acting upon each other has recently been reported for the first time for GlmY and GlmZ of Escherichia coli , and cascades of hierarchically acting regulatory RNAs have been hypothesized for other bacteria as well [43] . The ncRNAs described in this section are candidates for such interactions in Prochlorococcus . The difference between trans- and cis-encoded ncRNAs and asRNAs is frequently considered fuzzy since both act through base complementarity . However , depending on the length of the overlap , interactions between transcripts from the forward and the reverse DNA strand can be very strong due to the extended perfect sequence complementarity . asRNAs may act as the antidote in toxin-antitoxin systems [47] , [48] or in gene regulation [30] , [31] as has been reported for some bacteria . We detected two regions with probable sense/antisense pairing between ncRNAs . One of these regions contains Yfr6 with Yfr14 on the opposite strand ( Figure 7 , Table 1 ) . The second region is located in genomic island 1 containing Yfr8 and Yfr9 , each of which are 290 nt in size ( Figures 2 and 7 , Table 1 ) . One of the ncRNAs in both pairs is upregulated during phage infection ( Figure 4 ) and both ncRNA pairs contain a potential peptide-coding open reading frame within the sequence of one of the RNAs ( Figure 7 ) . The peptide sequence associated with Yfr6 is 33 amino acids long and is highly conserved and widely distributed among high light- and low light Prochlorococcus strains . The potential 44 amino acids peptide-coding frame within Yfr9 was found in three other Prochlorococcus isolates ( MIT9515 , MIT9301 , AS9601 ) , but has been lost in Prochlorococcus strains MIT9312 and MIT9215 due to a frame shift . Homologues of Yfr6 and Yfr9 , respectively , have high sequence conservation over their complete ncRNA genes – including the upstream and downstream regions of the potential peptides . Yfr6 and Yfr9 resemble RNAIII from Staphylococcus aureus being both relatively long and consisting of a small peptide-coding unit as well as a regulatory RNA . RNAIII is a 510 nt long riboregulator from which the 26 amino acid δ-hemolysin peptide is also translated [49] . Another bifunctional ncRNA ( SgrS ) has been described in Escherichia coli that contains a conserved ORF ( SgrT ) in the 5′ region of SgrS , both of which promote recovery from glucose stress in mechanistically distinct fashions [50] . Moreover , secondary structure predictions of Yfr6 [25] and of Yfr14 , Yfr8 , and Yfr9 ( Figures S2 and S3 ) support the potential role of these transcripts as functional RNAs as they contain many G - C base pairings and compensatory mutations , which conserve the structure rather than the sequence – a feature of many ncRNAs . On the other hand , highly structured transcript regions are also found in certain mRNAs where they serve as platforms for sophisticated ncRNA-mediated translational control . In the case of the Escherichia coli tisB mRNA , for example , this transcript encodes a peptide as short as 29 amino acids towards its 3′ end , yet there is no evidence that tisB would act as a riboregulator [17] , [51] . The fact that Yfr6 and Yfr9 overlap with other ncRNAs ( Yfr14 and Yfr8 respectively ) , suggests that this could be a toxin-antitoxin system – i . e . pairs of genes that code for a stable toxin and an unstable antitoxin . These are well-characterized in other bacteria , where the toxin is usually a toxic peptide that is neutralized or whose synthesis is prevented by the action of the product of the second gene , the antitoxin , which is either protein or RNA . Toxin-antitoxin systems such as the hok/sok system of Escherichia coli can serve as a natural genetic selection system to ensure presence of a plasmid [52] . Alternatively , chromosomally encoded toxin-antitoxin systems can be beneficial to cell survival under unfavorable growth conditions , sometimes in very sophisticated ways , for instance by transiently curtailing the consumption of nutrients during starvation or by temporarily inhibiting growth and thereby evading the killing effects of certain antibiotics [53] . Systematic searches for toxin-antitoxin systems have revealed a high abundance in free-living prokaryotes [54] but none of the seven known toxin-antitoxin families could be identified in Prochlorococcus . There is a growing number of examples of chromosomal toxin-antitoxin systems that use a cis-encoded asRNA as an antitoxin . Our data suggest that Yfr6/Yfr14 and Yfr8/Yfr9 may be potential candidates for toxin-antitoxin systems in Prochlorococcus MED4 . Little attention has been given to chromosomally cis-encoded asRNAs until recently , and only a few have been described for cyanobacteria [29]–[31] . Surprisingly , we detected 24 asRNAs in our analyses , which vary between 100 to 600 nt in size ( Table S2 ) . Some are differentially expressed under different light conditions and under phage infection ( Table S3 ) . High light treatment caused one asRNA to be upregulated and one to be downregulated ( Table S3 ) . asRNA asMED4_15721 was upregulated twofold when cells were transferred from darkness to high light ( Table S3 ) . This behavior is similar to that of Yfr20 ( see above ) , and like Yfr20 , this asRNA is located in genomic island 5 , lending additional support for a function of this island in light stress adaptation . Notably , five of the 24 asRNAs are complementary to mRNAs that code for photosystem I subunits ( psaB and psaC ) or for photosystem II subunits ( psbB and psbO , psbX ) , respectively . The concentrations of asRNAs of photosystem II genes did not change when cultures were shifted from darkness to different light quantities and qualities , whereas transcript levels of their target mRNA decreased slightly ( Table S3 ) . In contrast , levels of the photosystem I asRNAs asMED4_17331 ( antisense of psaB ) and asMED4_18171 ( antisense of psaC ) decreased when cells were shifted from darkness to light , following the same trend as their mRNA counterparts ( Table S3 ) . Surprisingly , however , the latter asRNAs decreased in amount when transferred from darkness to medium white light whereas their target mRNAs did not ( Table S3 ) , which might indicate a light-dosage specific regulation of these asRNAs . We also found asRNAs that are differentially expressed in cells infected by phage . asMED4_04601 is upregulated during the initial stages of phage infection , whereas its target mRNA ( PMED4_04601 ) is constitutively expressed throughout the infection process . Interestingly , PMED4_04601 shows 67% amino acid identity to the central region of the potential Yfr6 peptide . In the case of PMED4_07401 ( PMM0684 ) both the respective asRNA ( asMED4_07401 ) and its target mRNA are upregulated from mid-to-late phase of phage infection . The number of 24 asRNAs detected in our analyses appears high , especially as the microarrays used for this study did not contain probes for antisense regions of protein coding genes ( see methods ) . Therefore we could only detect those asRNAs found in intergenic regions whose corresponding ORF was not originally annotated and asRNAs located in 5′ and 3′ UTRs . However , our mapping results revealed that asRNAs located opposite of 5′/3′ UTRs frequently overlap major parts of the adjacent coding sequences . While it is not possible to infer the functions of these asRNAs in Prochlorococus , in the cyanobacterium Synechocystis PCC6803 the asRNA IsrR occurs in higher quantities than the cis-encoded mRNA isiA under normal growth conditions , leading to degradation of RNA duplexes by RNase III [30] . Under stress conditions the expression of the mRNA is increased leading to free mRNA molecules that can be translated . This mode of action is highly unlikely in the case of the PMED4_07401 mRNA/asRNA pair , because they are co-upregulated during phage infection , pointing towards a protective rather than a degradative role . Interestingly , RNAse E is also among the genes upregulated during phage infection . It is hypothesized that ribonuclease activity could be utilized by the phage to degrade host RNA to generate nucleotides for phage replication [39] . In addition to non-protein coding asRNAs , we found several pairs of transcripts that are transcribed from complementary strands and that potentially code for proteins . These complementary transcripts overlap entirely with each other or with a major part of their 5′ or 3′ UTR . We confirmed these overlapping regions experimentally and found that they span between 74 nt to 333 nt at least ( Table S2 ) . Eight out of twelve of these overlapping regions are found in the same position in other Prochlorococcus genomes , whereas the other 4 are found in different regions of the genomes . At this point it is not clear whether the overlaps between these protein-encoding transcripts would interfere with their transcription , transcript accumulation or translation . We found evidence for both scenarios . Whereas PMED4_14671 ( located in the opposite 3′UTR region of PMED4_14661 ) is upregulated 14 fold when light intensity is increased , PMED4_14661 ( PMM1300 ) remains at basal transcript levels . Contrary to the above , PMED4_11211 ( PMM0997 ) and PMED4_11201 ( located in the opposite 3′UTR region of PMED4_11211 ) are inversely regulated under different light conditions and DCMU treatment ( Table S4 ) indicative of either interference during transcription , or coupled degradation , as observed for the asRNA and mRNA IsrR/isiA in the cyanobacterium Synechocystis PCC6803 [30] . In cases where only 3′-UTRs overlap this might not be of relevance because the transcriptional machinery should not be constricted . However , most of the transcripts we found overlap the 5′-UTRs and/or complete protein-coding regions and therefore are highly likely to be of regulatory relevance . It is difficult to identify short genes in bacterial genomes using annotation algorithms , because the number of possible reading frames increases the shorter the search window becomes . Therefore , many of the widely used annotation programs ( e . g . GLIMMER , GeneMark and CRITICA ) constrain the minimum length of an ORF and thus a considerable number of small ORFs remain unannotated . In a recent study of the Prochlorococcus pan genome , for example , hypothetical ORFs shorter than 50 amino acids were excluded unless they were found in more than one genome [9] . Our microarray analyses lead to the observation of 113 new ORFs ( not including asRNAs with potential protein coding sequences; Table S2 ) ranging between 33 to 130 amino acids in size that were not annotated in the first published Prochlorococcus MED4 genome version ( accession number BX548174 ) . The new annotation of Kettler et al . [9] ( accession number BX548174 . 1 , for new ORF IDs refer to: www . microbesonline . org ) also found 89 of the new ORFs . BlastP searches against the non-redundant NCBI database revealed that 10 of the 24 remaining ORFs have an annotated counterpart in at least one other genome whereas 14 ORFs represent short proteins that have not been detected thus far ( Table S5 ) . Using TblastN , all of the additional 14 novel ORFs were found in other genomes , even though they had gone undetected by computational annotation tools ( Table S6 ) . Amongst the newly discovered protein-coding genes are three ORFs that have homologues in cyanophage genomes . PMED4_16122 , which is located in genomic island 5 in MED4 , is homologous to PSSM4_095 in the MED4-infecting cyanophage PSSM4 [55] , suggesting gene transfer between a PSSM4-like phage and this island , in particular since no other homologues were found in the nr database . PMED4_15491 is in vicinity of genomic island 5 in the host genome and has one homolog in each of two cyanophage genomes – P-SSM4 and P-SSM2 ( PSSM4_181 and PSSM2_278 ) . According to TblastN results this ORF is present in numerous Prochlorococcus genomes but not in other cyanobacteria . The third newly discovered ORF with a homolog in a cyanophage is PMED4_10681 , which is found in the genome of cyanophage P-SSM2 . Unlike PMED4_16122 , however , this ORF is not in a genomic island in the host genome , and furthermore , is widely distributed over the cyanobacterial radiation with homologues in all Prochlorococcus strains , Synechococcus elongatus strains PCC 6301 and PCC 7942 , Fremyella diplosiphon , Nostoc sp . PCC 7120 , Anabaena variabilis and Synechocystis sp . PCC 6803 ( Table S6 ) . The broad distribution of PMED4_10681 suggests that it plays an important function in cyanobacteria , and emphasizes the importance of better annotation of small ORFs . Here we have described 14 novel ncRNAs , which increases the total number of ncRNAs in this organism to 24 ( including Yfr1-7 , ffs , tmRNA , RNase P RNA ) . One sixth of the 24 ncRNAs ( Yfr1 , Yfr3 , Yfr12 and Yfr18 ) were undetectable from microarray analyses under the conditions tested . Therefore it is likely that even more ncRNAs are present in Prochlorococcus MED4 . The proportion of ncRNAs in the Prochlorococcus MED4 genome is comparable with those found in enterobacteria like Escherichia coli , i . e . 1–2% of the genes encode ncRNAs . In comparison , the 6 identified protein regulators in Prochlorococcus [10] is a small number relative to the 32 two-component response regulators present in Escherichia coli [56] . This suggests that regulation of gene expression through ncRNAs plays an important role in Prochlorococcus' response to environmental cues . The relatively high number of ncRNAs is intriguing as it may represent a mode of adaptation to the extremely low nutrient conditions of the open oceans . Regulation by ncRNAs may require fewer resources than would be required for the synthesis of protein regulators . Furthermore , in the course of genome reduction there might have been a positive selection pressure for keeping small regulators , e . g . ncRNAs rather than large protein regulators . How ncRNAs function in Prochlorococcus is at present unclear . The absence of Hfq in MED4 suggests that the ncRNAs found in this strain represent a core-set of ncRNAs that function without the support of a chaperone , or with a novel chaperone yet to be identified . The genomic islands of Prochlorococcus are disproportionately connected to ecological functions in this group of cyanobacteria [57] , [11] . Here we have shown that approximately half of the Prochlorococcus ncRNAs are located in genomic islands suggesting that the function of these molecules is relevant for determining the relative fitness of ecotypes within Prochlorococcus . This is analogous to the accumulation of genes coding for ncRNA in pathogenicity islands in Staphylococcus aureus [58] and Salmonella typhimurium [59] as well as in genomic islands of Sinorhizobium meliloti [60] , and suggests that this phenomenon could be wide-spread for finely tuned specialization within microbial groups .
Three independent microarray experiments investigating global changes of gene expression under different light quantities and qualities ( light experiment , [38] ) , under phage infection ( phage experiment , [39] ) and under phosphorus starvation ( phosphorus experiment , [40] ) were analyzed . The custom Affymetrix high-density array MED4-9313 that was used features 25-base oligomers identical to the target sequence that are spread over the complete genome comprising all gene coding regions as well as all intergenic regions on both forward and reverse strands with a coverage of every 45 bases in intergenic regions , a special feature that offers the detection of unknown transcripts . The Affymetrix array also contains probes for another Prochlorococcus genome ( MIT9313 ) and two cyanophage genomes P-SSP7 and P-SSM4 , whose average signal intensities were used to calculate threshold expression signals . For each set of experiments the threshold value used was re-evaluated to ensure high specificity of candidate probes . Specifically , we extracted probes with an expression signal of ≥200 in 18 of 21 arrays from the light experiment . Because of different experimental designs and thus resultant variations in overall expression signals , the threshold filter was adapted for the phage and phosphorus experiment extracting probes with expression signals ≥100 in 4 of 14 or 4 of 10 time points respectively ( corresponding to the average of biological triplicates ) , respectively , with a 2-fold change in at least one time point between control and stress condition . The distribution of probe intensities was adjusted by quantile normalization across different arrays within the same experiment . This procedure minimized array-specific effects and allowed us to determine fold changes of single probes targeting ncRNAs , asRNAs and overlapping transcripts . Rather strict criteria for transcript identification were chosen to ensure a high true positive rate for transcript detection . To minimize the number of 5′ and 3′UTRs detected , probes within 100 nt of the adjacent gene in the same orientation were excluded . Remaining probes were grouped in transcriptional units and further characterized to categories: ORF , asRNA , ncRNA , 5′/3′UTR , pseudogenes and operon elements . The grouping and characterization is based on the localization in the genome and on BLAST searches against 11 Prochlorococcus genomes ( http://www . ncbi . nlm . nih . gov/genomes/MICROBES/microbial_taxtree . html ) to identify conserved regions . Genes classified as ORFs encode for a peptide sequence with a start and stop codon without a frame shift and were present in at least two genomes . All PMED_xxxxx ORF notations ( including new ORFs ) follow that of Kettler et al . [9] and are available at www . microbesonline . org . ncRNAs and asRNAs were defined as genes without peptide-coding potential localized in intergenic regions and opposite protein-coding genes , respectively . In two special cases ncRNAs with a regulatory RNA component as well as a peptide-encoded component were allowed . For detailed information about grouping see Table S2 . Prochlorococcus MED4 was grown at 21°C in AMP1 medium [61] under 30 µmol quanta m−2 s−1 continuous white cool light . Culture conditions for microarray experiments are provided elsewhere [39] , [40] , [38] . Total RNA was isolated as previously described [38] with the following modifications . Cells were harvested by centrifugation at 10 , 000×g for 10 min at 20°C . The pellet was resuspended in RNA resuspension buffer ( 10 mM sodium acetate [pH 5 . 2] , 200 mM sucrose , 5 mM EDTA ) , snap frozen in liquid nitrogen and subsequently stored at −80°C . Total nucleic acids were DNase-treated with Turbo DNA-free ( 1 U/8 µg RNA , Ambion , USA ) for 15 min at 37°C . RNA was precipitated with 1/10 volume 3 M sodium acetate ( pH 5 . 2 ) and 3 volumes ethanol by centrifugation at 13 , 000×g for 30 min at 4°C and subsequently resuspended in water . Transcriptional start sites were determined by 5′-RACE following the method of Bensing et al . [62] . Briefly , RNA was treated with tobacco acid pyrophosphorylase ( 1 U/1 µg RNA; Epicentre , USA ) for 1 h at 37°C followed by phenol/chloroform extraction and ethanol precipitation . A synthetic RNA oligonucleotide ( 0 . 5 µl oligonucleotide [10 µM]/ 4 µg RNA; AUA UGC GCG AAU UCC UGU AGA ACG AAC ACU AGA AGA AA , Invitrogen , Germany ) was ligated to RNA using T4 RNA ligase ( 3 U/1 µg RNA; Fermentas , Germany ) for 1 h at 37°C followed by phenol/chloroform extraction and ethanol precipitation . Three control reactions were performed: i ) omitting tobacco acid pyrophosphorylase , ii ) omitting tobacco acid pyrophosphorylase and RNA oligonucleotide and iii ) dephosphorylating RNA prior to ligation with calf intestine alkaline phosphatase ( 0 . 1 U/1 µg RNA; Fermentas , Germany ) at 37°C for 1 h , followed by phenol/chloroform extraction and ethanol precipitation . For reverse transcription 250 ng linked RNA per gene was incubated with 0 . 8 U of the Omniscript reverse transcriptase ( Qiagen , Germany ) in the provided reaction buffer containing 0 . 08 µM gene specific primer and 1 mM dNTPs . Incubation was carried out at 42°C for 2 h with a final inactivation step at 95°C for 5 min . All reactions were performed in the presence of 40 U Ribolock RNase Inhibitor ( Fermentas , Germany ) . cDNA was amplified by PCR using a gene-specific primer ( 0 . 2 µM ) and an RNA oligonucleotide-specific primer ( 0 . 2 µM ) with following the cycling conditions: 93°C/3 min; 35 cycles of 93°C/30 s; 50°C/30 or 55°C/30 or 60°C/30 s , 72°C/45 s; 72°C/5 min in GoTaq reaction buffer containing 1 U GoTaq polymerase ( Promega , Germany ) , 0 . 2 mM dNTPs and 3 . 5 mM MgCl2 . A complete list with all primers used is provided in Table S7 . Amplified PCR fragments were gel-excised and purified on Nucleospin columns ( Macherey & Nagel , Germany ) and then cloned into plasmid pGEMT ( Promega , Germany ) . After transformation into E . coli XL1-Blue , plasmid inserts were amplified by colony PCR , purified on Nucleospin columns ( Macherey & Nagel , Germany ) and sequenced using an ABI 3130XL automatic DNA sequencer ( Applied Biosystems , USA ) . To determine the 3′ end of RNAs , 3′RACE was performed following the method described previously [17] . Briefly , RNA was treated as described above followed by a dephosphorylation with calf intestine alkaline phosphatase ( 0 . 2 U/1 µg RNA; Fermentas , Germany ) at 37°C for 1 h and a subsequent phenol/chloroform extraction and ethanol precipitation . RNA 3′ ends were linked to a 3′ end blocked RNA oligonucleotide ( 0 . 5 µl oligonucleotide [10 µM]/4 µg RNA , pAAG AUG AAU GCA ACA CUU CUG UAC GAC UAG AGC AC , Metabion , Germany ) using 0 . 8 U/1 µg RNA T4 RNA Ligase ( Fermentas , Germany ) followed by phenol/chloroform extraction and ethanol precipitation . Reverse transcription was performed as described above with the following modifications: 0 . 2 µM 3′ RNA oligonucleotide-specific primer and 2 . 5 mM dNTPs . Subsequent PCR , cloning and sequencing was performed as described above . Determined 5′ and 3′ ends are given in Tables S2 and S8 . RNA samples ( 50 µg ) were denatured for 5 min at 65°C in loading buffer ( Fermentas , Germany ) , separated on 10% urea-polyacrylamide gels for 16 h at 100 V and transferred to Hybond-N nylon membranes ( Amersham , Germany ) by electroblotting for 1 h at 400 mA . The membranes were hybridized with specific [γ -32P]ATP end-labelled oligonucleotides or [α-32P]UTP-incorporated transcripts . Hybridization in 50% deionized formamide , 7% SDS , 250 mM NaCl and 120 mM Na ( PO4 ) pH 7 . 2 was performed over night at 42°C or at 62°C with labelled oligonucleotide probes or labelled transcript probes , respectively . The membranes were washed in 2×SSC ( 3 M NaCl , 0 . 3 M sodium citrate , pH 7 . 0 ) [55] , 1% SDS for 10 minutes; 1×SSC , 0 . 5% SDS for 10 min; and briefly in 0 . 1×SSC , 0 . 1% SDS . All wash steps were performed 5°C below hybridization temperature . Signals were detected and analyzed on a Personal Molecular Imager FX system with Quantity One software ( BIO-RAD , Germany ) . Gene-specific oligonucleotides were labelled with [γ-32P]ATP by the exchange reaction of T4 polynucleotide kinase ( Fermentas , Germany ) using 0 . 5 U of enzyme , 1 . 25 µM oligonucleotide , 15 µCi [γ-32P]ATP in reaction buffer A for 30 min at 37°C followed by inactivation for 5 min at 95°C . The MAXIscript Kit ( Ambion , USA ) was used for transcription of probes for use in Northern analyses containing 100 ng PCR-generated DNA template , 500 µM each of ATP , CTP , GTP , 20 µM UTP , 50 µCi [α-32P]UTP , 1 µl T7 enzyme mix in reaction buffer amended with SUPERase In RNase inhibitor ( Ambion , USA ) . Transcription was carried out at 37°C for 10 min . Thereafter , the reactions were treated with 2 U of Turbo DNase-free ( Ambion , USA ) at 37°C for 15 min . The enzyme was heat inactivated for 10 min in the presence of 23 mM EDTA . | Prochlorococcus is the most abundant phototroph in the vast , nutrient-poor areas of the ocean . It plays an important role in the ocean carbon cycle , and is a key component of the base of the food web . All cells share a core set of about 1 , 200 genes , augmented with a variable number of “flexible” genes . Many of the latter are located in genomic islands—hypervariable regions of the genome that encode functions important in differentiating the niches of “ecotypes . ” Of major interest is how cells with such a small genome regulate cellular processes , as they lack many of the regulatory proteins commonly found in bacteria . We show here that contrary to the regulatory proteins , ncRNAs are present at levels typical of bacteria , revealing that they might have a disproportional regulatory role in Prochlorococcus—likely an adaptation to the extremely low-nutrient conditions of the open oceans , combined with the constraints of a small genome . Some of the ncRNAs were differentially expressed under stress conditions , and a high number of them were found to be associated with genomic islands , suggesting functional links between these RNAs and the response of Prochlorococcus to particular environmental challenges . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] | [
"biochemistry",
"genetics",
"and",
"genomics",
"microbiology",
"molecular",
"biology"
] | 2008 | The Challenge of Regulation in a Minimal Photoautotroph: Non-Coding RNAs in Prochlorococcus |
Aedes aegypti is the most important vector of dengue fever in Brazil , where severe epidemics have recently taken place . Ae . aegypti in Brazil was the subject of an intense eradication program in the 1940s and 50s to control yellow fever . Brazil was the largest country declared free of this mosquito by the Pan-American Health Organization in 1958 . Soon after relaxation of this program , Ae . aegypti reappeared in this country , and by the early 1980s dengue fever had been reported . The aim of this study is to analyze the present-day genetic patterns of Ae . aegypti populations in Brazil . We studied the genetic variation in samples of 11 widely spread populations of Ae . aegypti in Brazil based on 12 well-established microsatellite loci . Our principal finding is that present-day Brazilian Ae . aegypti populations form two distinct groups , one in the northwest and one in the southeast of the country . These two groups have genetic affinities to northern South American countries and the Caribbean , respectively . This is consistent with what has been reported for other genetic markers such as mitochondrial DNA and allele frequencies at the insecticide resistance gene , kdr . We conclude that the genetic patterns in present day populations of Ae . aegypti in Brazil are more consistent with a complete eradication of the species in the recent past followed by re-colonization , rather than the alternative possibility of expansion from residual pockets of refugia . At least two colonizations are likely to have taken place , one from northern South American countries ( e . g . , Venezuela ) that founded the northwestern group , and one from the Caribbean that founded the southeastern group . The proposed source areas were never declared free of Ae . aegypti .
Dengue fever is a viral disease transmitted by Aedes mosquitoes that occur in tropical and subtropical areas around the world . Due to a widespread distribution , this disease could be more important than malaria in terms of economic impact and morbidity [1]–[4] . It is estimated that more than two billion people ( over 40% of the world's population ) are at risk of infection by one or more dengue serotypes [5] , [6] . Brazil is especially vulnerable to dengue epidemics , with ten times more cases than other Latin American countries during recent outbreaks [6] , [7] . The main vector of dengue in Brazil is the mosquito Aedes aegypti , which is also a vector for yellow fever and chikungunya viruses [8] . Ae . aegypti is a particularly adaptable invasive species that has successfully colonized most tropical and subtropical regions of the world . This is due to the vector's highly anthropophilic behavior and ability to lay its desiccation-resistant eggs in man-made water containers , widely available in most developing countries where water distribution and sanitary conditions are rudimentary . Modern transportation and commerce have greatly contributed to the passive geographical spreading of this vector and , consequently , to disease dissemination . Due to the lack of an effective vaccine , currently , dengue control programs rely almost exclusively on vector control efforts [3] , [9] . Historically , neurotoxic insecticides have been the method of choice to control Ae . aegypti populations [10]–[12] . However , the large-scale unregulated use of insecticides , has exerted intense selective pressures on mosquito populations leading to the development of resistant strains not only in Brazil but also worldwide [13]–[18] . This undesired outcome increases the need for the creation of new vector control methods . Several emerging technologies are based on various genetic strategies ( RIDL , RNAi , HEG , Wolbachia ) and are either under development or are already being field-tested [19] . Regardless of the methods employed , knowledge of the genetic variability and population subdivision of mosquito populations is pivotal for the development of rational dengue control programs . In this context , Brazil is a particularly interesting country regarding dengue epidemiology because it has gone through a well-documented vector eradication program [10] , [20] . In the first half of the 20th century , when dengue was not yet a public health issue , Ae . aegypti populations were widespread and responsible for several yellow fever epidemics , especially in the northeast of Brazil . Motivated by the success achieved by the Anopheles gambiae control program , the Brazilian government launched , in 1947 , an initiative to eradicate Ae . aegypti populations based on the use of DDT . In 1958 , during the XV Conferencia Sanitária Panamericana in Puerto Rico , Brazil was declared free of Ae . aegypti . The species was again recorded in the late 1970's , probably as a consequence of a reduction in the efficacy of the vector control measures employed [10] , [20] . The first well-documented outbreak of dengue fever in the country occurred in 1982 , in Roraima state , north Brazil [21] . Today , the entire country is endemic for dengue and the last outbreak in 2013 accounted for more than 1 . 5 million cases ( BRAZIL/Health Ministry , 2014 ) . Therefore , dengue fever has become a major public health issue , especially because all four DENV serotypes co-circulate in the country [7] . The first studies to assess the genetic structure of Brazilian Ae . aegypti populations were based upon RAPD markers and revealed high levels of interpopulation genetic differentiation [22] , [23] . Allozyme-based studies also indicated a high degree of genetic structure and limited gene flow between regions connected by highways and railroads , suggesting that passive mosquito dispersal is not extensive [24] , [25] . Within cities , such as the densely populated Rio de Janeiro , local genetic differentiation has also been found indicating that this species has extremely limited dispersal capability [26] , [27] . The analyses of mtDNA sequence data ( ND4 and COI ) of several Brazilian populations revealed the co-occurrence of two distinct lineages in the country [28] , [29] . A study of frequencies of the kdr ( knock-down resistance ) mutations , which confer pyrethroid resistance , found at least three distinct genetic groups in 30 Brazilian populations . [18] . Microsatellites are assumed neutral , highly variable codominant markers commonly used in population genetics . However , they have never been used in a nationwide study of Ae . aegypti in Brazil . Here , we present the results of the analysis of 12 microsatellite loci in Brazilian Ae . aegypti populations in an effort to better understand the genetic structure of this vector in the country , which may lend insights into the presumed recolonization following eradication events .
Ae . aegypti samples were field-collected from 11 sites in Brazil ( Table 1 ) . Eggs were collected in multiple ovitraps per locality ( to avoid sampling of siblings ) and reared to adults for proper taxonomic identification . Samples from generation F0 up to F2 were preserved in 70–100% ethanol or dry at −80°C for further analysis . Eight previously studied populations from different countries across South , Central and North America [30] were included in the analyses ( Table 1 ) . Total genomic DNA was extracted with the DNeasy Kit ( Qiagen ) following the manufacturer's protocol . Individual genotypes were scored for 12 previously published microsatellite loci [30] , [31] . Microsatellite alleles were scored using Gene Mapper software ( Applied Biosystems ) . The experiments were performed in the Yale Laboratory using the same ABI machine as used by Brown et al . [30] and alleles scored in accordance with that publication , so the data presented here are directly comparable to data in Brown et al . [30] . To infer the statistical reliability of our markers , each locus was tested for deviations from Hardy-Weinberg expectations on the web version of Genepop v1 . 2 [32] , [33] . The same program was used to test all loci pairs for linkage disequilibrium ( LD ) . Markov chain parameters were set at 10 , 000 dememorizations , 1 , 000 batches and 10 , 000 iterations per batch for both HWE and LD . Critical significance levels were corrected for multiple tests using the Bonferroni correction . The probability of null allele occurrence in each locus within each population was calculated using MicroChecker v2 . 2 . 3 [34] . When null alleles were found , FreeNA [35] was used to infer the extent of bias imputed by their presence on FST values . Genetic diversity per locus and in each population was estimated by unbiased expected heterozygosity using GenALEx v6 . 5 [36] . The same program was used to compute allele frequencies for all loci across populations and for the Analysis of Molecular Variance ( AMOVA ) . Sample size corrected allelic richness and percentage of private alleles were calculated using HP-Rare v1 . 0 [37] , [38] . The software Arlequin v3 . 5 . 1 . 2 [39] was used to compute FST values and their significance between all pairs of populations with 1 , 000 permutations . Cavalli-Sforza and Edwards distances were computed using the software package Phylip 3 . 6 [40] . The Cavalli-Sforza distance was chosen since it has been shown to be more robust when null alleles are present [35] , [41] . Programs of the Phylip package ( SEQBOOT , GENEDIST , NEIGHBOR , CONSENSE ) were used to construct a neighbor-joining tree with 1 , 000 bootstrap replicates . A factorial correspondence analysis ( FCA ) was performed with the software Genetix v4 . 0 . 5 [42] to better analyze the Brazilian samples . Isolation by distance was tested on the IBD web server v3 . 23 [43] and also through a Mantel test of correlation between geographical ( LnKm ) and genetic distance matrices ( FST/ ( 1-FST ) ) . For both analyses significance was inferred with 1 , 000 permutations . The Bayesian approach used in the software STRUCTURE v2 . 3 . 2 [44] was used to infer the number of genetic clusters ( K ) in the whole data set , without prior information of sampling locations . An admixture model was used where alpha was allowed to vary and independent allele frequencies were assumed with lambda set to one . We performed ten independent runs for each value of K ( K = 1 to the maximum supposed number of populations ) with a burn-in phase of 200 , 000 iterations followed by 600 , 000 replications . The program Structure Harvester v0 . 6 . 93 [45] was used to summarize these results and determine the most likely number of clusters by calculating ΔK [46] . Results from STRUCTURE were summarized with the program CLUMPP v1 . 1 . 2 [47] and visualized using the program Distruct v1 . 1 [48] . The program GeneClass2 v2 . 0 [49] was used for self-assignment tests to infer the degree to which an individual mosquito could be assigned to a specific population . Self-assignment tests were performed with reference populations based on geography and clusters identified by the program STRUCTURE .
Although 15 of the 1 , 244 ( 1 . 2% ) locus-by-locus tests for LD remained significant after Bonferroni correction , no two loci were consistently correlated across populations . Eleven of the 231 ( 4 . 76% ) FIS values deviated significantly from Hardy-Weinberg expectations at the 5% significance level after sequential Bonferroni correction ( Table S1 ) . Of the 20 population-specific tests for each marker , zero ( AC1 , AC2 , AC4 , CT2 , AG5 , B2 and B3 ) , one ( AG1 , AG2 and A1 ) , three ( AC5 ) and five ( A9 ) tests were significant . For A9 , all significant tests resulted from an excess of homozygotes , probably due to null alleles as reported in Brown et al . [30] . Micro-checker results suggest that locus A9 has a high probability of having null alleles in 11 populations and AC5 in five . Null allele frequency varied from 0 to 0 . 32 among populations for the A9 locus and 0 to 0 . 21 for the AC5 locus ( Table S2 ) . Other loci had null allele frequencies predicted as well ( in four populations for AG2 , three populations for AC1 , two populations for B3 and one population for AC4 , AG1 and AC2 ) , although none with frequencies >0 . 14 ( Table S2 ) . Null alleles at microsatellite loci are commonly found in insects [50]–[52] and have been demonstrated to be especially common in species with large population sizes [35] , which is likely the case for Ae . aegypti populations . The decrease in diversity caused by null alleles can lead to an overestimation of statistics such as FST and identity values [53] , especially when there is low gene flow among populations [35] , [54] . Nevertheless , simulation studies have shown the bias to be small for lower FST values and almost none when assignment methods are used [54] . A comparison between FreeNA corrected and non-corrected pairwise FST values shows very small deviations in our dataset ( Table 2 ) . Gene frequencies , heterozygosities ( Ho and He ) , and allelic richness for all loci studied are given in Table S3 . All populations have similar diversity measures . AMOVA results show that within population differences account for 83% of the genetic variation found . Private allelic richness was low ( Np<0 . 08 ) with only Pau dos Ferros , São Gonçalo , Dominica and Miami with estimates greater than 0 . 16 ( Table S3 ) . Overall FST value ( FST = 0 . 175; 95% confidence interval 0 . 146–0 . 204 ) indicates a moderate level of population differentiation ( Table 2 ) . Coatzacoalcos and Houston were the only populations to have higher FST values ( ranging from 0 . 24 to 0 . 38 in Coatzacoalcos and 0 . 11 to 0 . 31 in Houston ) . Miami and some Brazilian populations had FST values lower than 0 . 10 ( Table 2 ) . The genetic distance based NJ tree is reasonably consistent with geographic distances among populations ( Figure S1 ) and was corroborated by Mantel tests of isolation by distance that found significant correlation between the geographical and genetic distance matrices ( P<0 . 001 , R2 = 0 . 53; Figure 1A ) . When only Brazilian samples were analyzed , weaker isolation by distance was detected by the Mantel tests ( P = 0 . 01 , R2 = 0 . 31; Figure 1B ) . A model-based clustering algorithm was used to identify subgroups with distinctive allele frequencies without prior information on population structure . In all analyses , most individuals from the same geographical origin shared similar membership coefficients in inferred clusters . The Evanno et al . [46] method identified K = 2 as the most likely number of clusters , but small peaks on the ΔK graph are also apparent at K = 5 and K = 13 ( Figure S2 ) . The two-cluster analysis groups include all Brazilian populations with Dominica , with the exception of Tucuruí and Marabá ( Figure 2 ) . Tucuruí and Marabá are more similar to populations from Venezuela , Mexico , Puerto Rico , and North America . Indeed , the FCA of the Brazilian samples and the NJ tree show that Tucuruí and Marabá ( 98% bootstrap support; Figures S1 and 3 ) are very different from all other Brazilian populations . In addition to these two , Mossoró , Aracajú and to some extent Pau dos Ferros also form a slightly differentiated genetic cluster on the FCA analysis ( Figure 3 ) . While the pattern described by the above two genetic clusters is the best supported by the ΔK method [46] , subtle substructure can be discerned by a more detailed analysis . The five-cluster STRUCTURE plot ( Figure 2 ) shows that most populations have mixed ancestry and only Coatzacoalcos ( Mexico ) shows a pure genetic composition . In this analysis , the Brazilian samples from Tucuruí and Marabá now group together with Mossoró , Aracajú and , to some extent , Pau dos Ferros , consistent with the FCA analysis that indicates that these last three populations are indeed genetically differentiated as well . The thirteen-cluster plot ( Figure 2 ) further describes the extent of Ae . aegypti complex genetic composition in each population . The analysis reflects admixture between groups probably due to recent gene flow among populations , although common ancestry cannot be excluded . The isolation by distance detected among samples also indicates that gene flow occurs between adjacent populations ( Figure 1 ) . The thirteen-cluster analysis further separates the Brazilian populations in five distinct clusters ( Figure 2 ) , with some mixed ancestry observed , especially in the population proximate to Rio de Janeiro ( São Gonçalo ) , a well-known tourist destination . Results from GeneClass2 show that when geographical locations were used as the reference populations , 83% of individuals were correctly assigned back to their population of origin . When the number of clusters inferred by STRUCTURE were used , this number increased drastically for K = 2 ( 94 . 6% ) but not so much for K = 5 ( 92% ) and even less for K = 13 ( 86 . 5% ) , corroborating the higher peak found for K = 2 in the Evanno plot ( Figure S2 ) . Since STRUCTURE seems to identify the higher hierarchy in population differences [46] , to better understand the relationships within the two groups identified ( Blue and Red in Figure 2 , K = 2 plot ) we performed additional analyses . When the blue group , that encompasses Tucuruí and Marabá with EUA , Mexico , Venezuela and Puerto Rico , is analyzed; the optimal number of clusters determined by the ΔK method are K = 2 and K = 8 ( Figure S3A and B ) . At K = 2 , the two Mexican populations are differentiated from the rest and display some mixed ancestry with other populations ( Figure S4 ) . North America seems to be the most influenced by the Mexican genetic background as was already determined by Brown et al . [30] . Brazilian and Venezuelan populations have less background from Mexico than North America and are , therefore , similar . With K = 8 , all populations except the two Brazilian ones seem to be genetically differentiated ( Figure S4 ) . When the red group is analyzed K = 2 , K = 3 , and K = 5 provide some insights ( Figure S5 ) . The two-cluster analysis separates the Brazilian populations from Dominica but a high degree of mixed ancestry can be observed in Jacobina , from the Northeast of Brazil . The three-cluster analysis further differentiates the Brazilian populations showing that Mossoró , Aracajú , Pau dos Ferros and , to some extent , Natal and Maceió group together , although high levels of mixed ancestry can be observed in most populations ( Figure S5 ) . The differentiation of Mossoró , Aracajú and Pau dos Ferros from other Brazilian populations can also be seen on the FCA analysis ( Figure 3 ) . The five-cluster analysis further separates Mossoró , Aracajú and Pau dos Ferros in one cluster and shows the geographically close Maceió population to have genetic similarities with Southeastern populations ( São Gonçalo and Cachoeiro ) . Some degree of mixed ancestry can be observed in all populations and this is most apparent in São Gonçalo , Jacobina , Maceió , Pau dos Ferros , Natal , and Cachoeiro ( Figure S5 ) . Pau dos Ferros is a small city in the state of Rio Grande do Norte that probably has both the influence of the geographically closer Mossoró and of its state capital , Natal . Interestingly , the two samples from Cachoeiro ( 2008 and 2012 ) , sampled four years apart , show some degree of differentiation . In a recent study carried out in São Paulo state , Brazil , no differentiation between five sampling years was found [55] . Despite these subtle genetic patterns , we have strong evidence to conclude that Brazilian populations of Ae . aegypti separate into two major genetic groups with distinct affinities to populations outside Brazil as indicated in Figure 4 .
Brazil was officially declared free of Ae . aegypti in 1958 [20] , but reappearance of the species occurred shortly after relaxation of control measures . In its assessment of the efficacy of its eradication program , the Pan American Health Organization ( PAHO ) admitted that eradication had not been successful in Venezuela , Suriname , Guyana , South USA and a few Caribbean Islands [10] . It is believed that re-colonization of Brazil happened in the 1970's probably from mosquitoes from neighboring countries [7] , [20] . Our results indicate that two major genetic groups are present in Brazil , one descending from Venezuela and probably other northern American countries and another one from the Caribbean ( Figure 4 ) . Bracco et al . [28] using the mitochondrial ND4 gene have also observed two major lineages in Brazil . The first genetic group identified suggests that mosquitoes from Venezuela and possibly the USA have contributed to the northern Brazilian population . Venezuela seems to be an important source of mosquitoes as well as dengue virus serotypes into Brazil [56]–[58] . Indeed , Silva et al . [59] , also using the mitochondrial ND4 gene , have found that populations from the Northern states in Brazil seemed to be similar to those from Venezuela and Peru . In that study , no Caribbean Island was sampled . Venezuelan Ae . aegypti are highly susceptible to DENV2 virus [60] and this could be the reason Lourenço-de-Oliveira et al . [56] have observed that northern Brazilian populations are more susceptible to DENV2 virus than are southern ones . The second genetic group comprises Brazilian southeast and central-west populations and is genetically similar to Dominica in the Caribbean ( Figure 2 ) . Brazil went through a nationwide vector control program based on pyrethroid insecticides from 2001 to 2009 . Nevertheless , Linss et al . [18] detected three kdr genetic groups in the country ( North , Northeast and Southeast-Central ) . Since differential selection pressures acting in the area studied could not account for their findings , the authors argued that the pattern observed could have resulted from genetic differences in the Ae . aegypti strains that founded those populations ( Linss et al . [18] ) . In our results , although the most important genetic break occurs between Northern populations and all others ( Figures 2 and 4 ) , the FCA also shows that Mossoró , Aracajú and , to some extent , Pau dos Ferros can be differentiated ( Figure 3 ) . When a higher cluster number is analyzed on the Bayesian clustering analysis , we see that the same three populations cluster together with the two Northern ones ( Figure 2 ) . Other studies of Brazilian Ae . aegypti have identified a genetic break between northern and southern populations [22]–[24] , [29] , [59] , [61] , although the exact location of the break is not always consistent . It is conceivable that the dynamics and mode of inheritance of different genetic markers can account for somewhat different patterns , e . g . , cytoplasmic mtDNA versus nuclear genes and neutral genes versus selected alleles such as at insecticide resistance genes ( kdr ) . The isolation by distance found within Brazilian samples suggests some connectivity among populations , so it is not surprising that the two lineages that may have initially re-invaded Brazil are now exchanging genes and perhaps merging . The origin of these two genetic units seem reasonably clear from our data , although with only a single Caribbean sample ( discounting Puerto Rico , considered part of the US ) to compare , the origin of the southern lineage is less well established . Bracco et al . [28] suggested that Asia may have been the origin of the southern group , however , they did not sample any Caribbean Islands . Brazil has a long history of international trade within the Americas and Caribbean and only recently has this been shifted to Asian countries . Another indication that indeed Caribbean and not Asian populations might be the source of a Brazilian Ae . aegypti is the fact that Linss et al . [18] have found , in Brazilian populations , the same Caribbean kdr mutation allele , Val1016Ile and not Val1016Gly , that is commonly observed in Asian populations . Furthermore , Brown et al . [62] studying a diverse set on SNPs and nuclear gene sequence data have found that Ae . aegypti probably came from West Africa into the New World , where it dispersed to Asia and Australia . In their study , a Brazilian population from the Southeast ( Cachoeiro ) is in the same clade as Venezuelan and Caribbean populations , consistent with our findings . While our data are consistent with the re-colonization hypothesis , we cannot exclude alternatives . The two major genetic groups observed today may have existed prior to 1958; following relaxation of vector control , the expansion from refugia within Brazil could have re-established the pattern present today . However , one expects small refugia to drift to heterogeneous gene frequencies such that subsequent expansion would lead to a mosaic of genetic units not geographically structured . Our data do not support such a scenario . Furthermore , a low genetic diversity would be expected due to a bottleneck period , which was not observed either . Measures of diversity ( 0 . 39<Ho<0 . 67 ) and allelic richness ( 2 . 46<Na<4 . 44 ) are similar in Brazilian samples and other populations from the Americas , even when compared to countries where eradication did not occur ( ) [30] . Studies with mitochondrial DNA markers ( COI and ND4 ) have also found high genetic variability in Brazilian samples [28] , [29] . Thus , while we cannot rule out incomplete eradication , for the reasons stated , recolonization from regions outside Brazil that were never declared free of Ae . aegypti is a simpler explanation consistent with the patterns observed in present day Brazil populations of this vector . | The mosquito , Aedes aegypti , was historically very important as the major vector of yellow fever , whereas today it is most notorious for being the major transmitter of dengue fever . In the 1940s and 50s , the Pan-American Health Organization organized a campaign to eradicate Ae . aegypti from the New World . They were partly successful , with Brazil being the largest country to be declared free of Ae . aegypti . Within ten years of relaxation of control efforts , Ae . aegypti reappeared in Brazil and today is the vector of the most intense dengue epidemics in the New World . Here , we present population genetic data that are most consistent with the species having truly been eradicated from Brazil rather than simply pushed into small refugia as a consequence of the eradication campaign . The re-infestation most likely resulted from two sources: 1 ) from northern S . American countries like Venezuela into northwest Brazil and 2 ) from the Caribbean into the southeast of the country . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"biogeography",
"public",
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"infectious",
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"population",
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"diseases",
"evolutionary",
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] | 2014 | Genetic Diversity of Brazilian Aedes aegypti: Patterns following an Eradication Program |
Lymphatic filariasis ( LF ) is a globally significant disease , with 1 . 3 billion persons in 83 countries at risk . A coordinated effort of administering annual macrofilaricidal prophylactics to the entire at-risk population has succeeded in impacting and eliminating LF transmission in multiple regions . However , some areas in the South Pacific are predicted to persist as transmission sites , due in part to the biology of the mosquito vector , which has led to a call for additional tools to augment drug treatments . Autocidal strategies against mosquitoes are resurging in the effort against invasive mosquitoes and vector borne disease , with examples that include field trials of genetically modified mosquitoes and Wolbachia population replacement . However , critical questions must be addressed in anticipation of full field trials , including assessments of field competitiveness of transfected males and the risk of unintended population replacement . We report the outcome of field experiments testing a strategy that employs Wolbachia as a biopesticide . The strategy is based upon Wolbachia-induced conditional sterility , known as cytoplasmic incompatibility , and the repeated release of incompatible males to suppress a population . A criticism of the Wolbachia biopesticide approach is that unintended female release or horizontal Wolbachia transmission can result in population replacement instead of suppression . We present the outcome of laboratory and field experiments assessing the competitiveness of transfected males and their ability to transmit Wolbachia via horizontal transmission . The results demonstrate that Wolbachia-transfected Aedes polynesiensis males are competitive under field conditions during a thirty-week open release period , as indicated by mark , release , recapture and brood-hatch failure among females at the release site . Experiments demonstrate the males to be ‘dead end hosts’ for Wolbachia and that methods were adequate to prevent population replacement at the field site . The findings encourage the continued development and extension of a Wolbachia autocidal approach to additional medically important mosquito species .
Lymphatic filariasis ( LF ) is a disfiguring and socioeconomically burdensome disease estimated to affect over 120 million people worldwide , with 1 . 3 billion people at risk [1] . An ongoing global strategy for eliminating this mosquito borne disease is to interrupt transmission by administering annual macrofilaricidal prophylactics through mass drug administration ( MDA ) programs . However , in some regions the efficacy of these area-wide treatment programs can be compromised by the biology of the mosquito vectors . In the South Pacific , the pattern of negative density dependent transmission displayed by the primary vector , Aedes polynesiensis makes this mosquito more efficient in low-level microfilaraemics [2] , [3] . This complication has been hypothesized as a contributor to an inability to eliminate LF in the some areas of South Pacific , despite decades of ongoing MDA [2] , [4] . As a result , augmentative vector control has been advised for areas where A . polynesiensis is the primary vector 1 , 4–7 . Unfortunately , conventional vector control for A . polynesiensis has not been effective , due to the numerous , cryptic and inaccessible breeding sites of this mosquito and the geography of the Pacific Islands , which hinder control efforts due to the difficult logistics of moving control personnel and equipment between islands , even in those countries with relatively well-developed vector control programs [6] , [8] . Prior laboratory and field cage trials have examined an autocidal approach based upon artificial infections of Wolbachia [9] , [10] , an obligate intracellular bacterium estimated to occur in a majority of insect species [11] . In mosquitoes , Wolbachia causes cytoplasmic incompatibility ( CI ) , which can lead to arrested embryonic development in populations that include individuals infected with different Wolbachia types . Bidirectional CI results in egg hatch failure in both cross directions and was the basis of a prior , successful suppression of a Culex quinquefasciatus population in Burma [12] . In brief , the approach is similar to the Sterile Insect Technique ( SIT ) [13]–[15] in which repeated , inundative releases of sterile males act to sterilize females in the targeted field population . Releasing male mosquitoes does not pose a health threat , since they do not blood feed or vector disease . The released males are also ‘dead end hosts’ for the maternally inherited Wolbachia , so that the released infection type does not become established in the field . Despite the successful prior field trial , the Wolbachia-based suppression approach was considered an isolated demonstration , since naturally occurring bidirectionally-incompatible populations are rare [16] . Recently however , the development of methods for the artificial generation of bidirectionally-incompatible mosquito strains permits broader application [10] , [17] , [18] . Natural populations of A . polynesiensis are infected with a single Wolbachia type [19]–[21] . In 2008 , an artificially infected A . polynesiensis strain ( CP ) was generated by introgressing an alternate Wolbachia type originating from A . riversi into the A . polynesiensis genotype . The resulting CP males of the Wolbachia transfected strain of A . polynesiensis are incompatible with wild type females and show mating competitiveness equal to that of wild type males in laboratory trials [9] , [10] . The fitness/competitiveness of released males is a critical component of SIT approaches , including both traditional irradiation-based sterility [22] and newer transgenic approaches [23] , [24] . Prior experiments within cages demonstrate good fitness of the CP males relative to the wild type males , with a high competitive index ( C ) ( C>0 . 8 ) [9] . But prior to full-scale field trials ( e . g . , intended to suppress and eliminate populations ) , competitiveness must be assessed in the field . An additional objective of the open release trial was to assess the risk of unintended population replacement [18] , [25]–[27] . While population replacement is a desired outcome in some Wolbachia-based strategies [28] and a potential goal for downstream strategies with CP [10] , it was not the goal here . In the Wolbachia-based suppression strategy , the establishment of the artificial Wolbachia type in the targeted population could allow compatibility and reduce the suppressive effect of CP male releases . Horizontal movement of Wolbachia at an evolutionary time scale is hypothesized , based upon prior phylogenetic studies [29] . However , it is unclear what role male hosts play in horizontal movement .
The importation of the CP strain and subsequent release of CP males were permitted via French Polynesia Ministry Council decision n° 1392 CM , Oct 17 , 2007 . Field-work conducted on private land was with permission from the owners . The use of laboratory mice ( Mus musculus ) at the Institut Louis Malardé was approved by the “Commission permanente de l'assemble de la Polynesie Francaise ( Tahiti ) ” [Deliberation#2001-16/APF] . Animal work at the University of Kentucky was approved by the Institutional Animal Care and Use Committee 00905A2005 ) .
To assess the risk of horizontal transmission of Wolbachia from CP males , large laboratory cage assays were performed prior to open field releases . CP males were added to cages containing virgin A . polynesiensis , A . albopictus and A . aegypti females . As shown in Table 1 , control crosses of intraspecific matings demonstrated good fertility of females ( >50% egg hatch ) . While females continued to produce eggs in the interspecific matings , low egg hatch was observed , with only three of >25 , 000 eggs hatching . Of the three resulting larvae , two survived to adult , and both were A . polynesiensis males . PCR assays showed both males to be infected with the wild type Wolbachia . Thus , the F1 individuals were from rare egg hatch that results from A . polynesiensis females that are incompatibly mated with CP males [9] , [19] . For field releases of CP males , the sites were ‘motu’ islands , selected due to their small size , isolation and absence of human inhabitants . Prior characterization of the A . polynesiensis populations demonstrate the targeted motu to be infested with unusually large populations , more than one hundred times more dense than sites on the adjacent mainland [37] . This large population size makes the motus unattractive locations for early population suppression attempts . However , their isolation and prior characterization make them useful for examining questions of male competitiveness and replacement risk . Prior to the start of CP releases , a standardized collection protocol was used to monitor adults from the sites intended as release and no-release locations ( Fig . 1 ) . Monitoring at the three sites was ongoing for more than a year prior to the release start [37] . The highest population densities of A . polynesiensis were observed on TOA ( 166±209 , n = 96; Avg ± StDev adult females , number of collections ) and HOR ( 96±157 , n = 76 ) . A lower population density was observed on ANO ( 12±14 , n = 76 ) , which received substantial source reduction activity by the landowner . The population densities were seasonally variable , and capable of reaching high densities , with a maximum of 1 , 260 A . polynesiensis females collected in a 20-minute period at TOA in late August of 2009 . Beginning in December 10 , 2009 , the TOA site received an average of 3 , 800 CP males/week . CP males were reared on Tahiti and transported to Raiatea for release . CP male releases continued for thirty weeks , with more than 117 , 000 CP males released in total . There is no marker that is transferred from Wolbachia in the male to the mate that can be detected in mated females . Therefore , we relied upon an indirect measure to assess CP male competitiveness in the field: the likelihood of a female producing a non-hatching brood . Females collected at the TOA and HOR sites were isolated and allowed to oviposit , and egg hatch was recorded . During the period in which CP males were released , the proportion of a female producing hatching eggs was significantly lower at TOA relative to HOR , X2 ( 1 , N = 887 ) = 38 . 18 , p<0 . 0001 . In contrast , females at the release and no-release sites were equally likely to produce hatching eggs both before the start of CP male releases , X2 ( 1 , N = 141 ) = 2 . 22 , p = 0 . 13 and following the termination of releases , X2 ( 1 , N = 154 ) = 0 . 49 , p = 0 . 48 ( Table 2 ) . An analysis of the same data , comparing the different trial phases ( ‘no release’ versus the ‘during release’ periods ) within a site shows no difference for HOR , X2 ( 2 , N = 412 ) = 4 . 69 , p = 0 . 096 and a significant difference at TOA , X2 ( 2 , N = 770 ) = 44 . 33 , p<0 . 0001 . The failure of females to produce hatching eggs at the release site could result from cytoplasmic incompatibility or a lack of insemination . To examine for the latter , field collected females were dissected to examine spermatheca . High rates of fertilization were observed throughout the study at both the release site ( 88% fertilized; n = 350 females ) and no-release site ( 85% fertilized; n = 231 ) sites , and no difference was observed between the sites , X2 ( 1 , N = 581 ) = 0 . 72 , p = 0 . 39 . Male competitiveness can be estimated based upon the number of released CP males , the estimated number of wild type males and the frequency of incompatible mating events . Existing collecting methods yield low numbers of A . polynesiensis males on Toamaro [37] , [38] . Therefore , a mark release recapture experiment was performed at the start of CP male releases . CP males were marked with DayGlo , released and recaptured as previously described [38] . Collection using backpack aspiration yielded a total of 96 males in the three days of sampling , five of which were recaptured males . A modified Lincoln index was used to estimate male population size [39] , [40] , where N = estimated population density on day t , S = estimated probability of daily survival [41] , R = number of released females , C = number of captured females , r = number of recaptured females . Across the three recapture days , the male population size was estimated at approximately 5 , 900 males . Thus the 2 , 162 marked and released CP males represented approximately 37% of the indigenous male population size . Using a previously defined index [42] , the field competitiveness ( C ) was estimated from the estimated number of indigenous males ( N ) and incompatible males ( S ) , The proportion of incompatible matings ( P ) was estimated at 0 . 2 , based upon measurements of female incompatibility on Toamaro ( Table 2 ) . Using this definition , the competitiveness of CP males is estimated at 0 . 68 , where 1 . 0 would be equivalent fitness with wild type males . Relative to analogous estimations of classical , irradiation based SIT and newer transgenic approaches , this represents a relatively good level of competitiveness [24] . Due to the low proportion of incompatible males on Toamaro , it was not clear that population-level impacts would result from the CP male releases . To examine for an effect of CP male releases on the targeted A . polynesiensis population , a statistical method developed for environmental impact assessment was used , known as Before-After-Control-Impact-Paired-Series ( BACIPS ) [34]–[36] . Pair-wise comparisons were performed for the population size ( i . e . , number of adult females ) for the ‘before release’ and the CP male ‘during release’ periods , including all combinations of the two no-release sites ( HOR and ANO ) and the release site ( TOA ) . Comparison of the two no-release sites indicated no difference between the two time periods , t ( 25 ) = 0 . 03 , p = 0 . 51 . In contrast , comparisons of the release site ( TOA ) showed a significant difference between the ‘before’ and ‘during’ periods for pairwise comparisons with both HOR , t ( 25 ) = −4 . 72 , p<0 . 0001 and ANO , t ( 25 ) = −5 . 67 , p<0 . 0001 ( Fig . 2 ) .
Horizontal transfer of infection from males did not occur in laboratory experiments . These results provide evidence against the ability of CP males to transmit Wolbachia to conspecific and congeneric females under conditions of close proximity and probable interaction and are consistent with prior experiments examining for horizontal transfer to predators [43] . A sustained open release of CP males provides an additional test for horizontal transfer . Furthermore , an additional route for unintended population replacement is via the accidental release of CP females . To examine for establishment of the CP Wolbachia type in the field ( i . e . , either by accidental CP female release or paternal transmission ) , females were collected from TOA ( n = 83 females ) and HOR ( n = 30 females ) populations throughout the study , ending in August 2010 , following the termination of releases . The presence of the wild type Wolbachia and absence of the CP male type Wolbachia was observed in all field-collected females [10] . The results demonstrate that laboratory reared , sorted , and delivered CP males survive and competitively mate with indigenous A . polynesiensis females within a field population . Despite the relatively small numbers of released males relative to the large indigenous population size , we observed a significant decrease in the number of TOA females able to produce viable embryos . In contrast , decreases were not observed at the two control sites , where CP males were not released . This observation supports that the observed decrease in egg hatch was due to CP male releases and not seasonal and weather driven events . In addition to the laboratory tests , the results of the open CP male releases showing the absence of the B-clade Wolbachia are also consistent with the hypothesized role of males as ‘dead end hosts’ for Wolbachia . Specifically , we have observed no evidence for the introduced Wolbachia type persisting on TOA outside of the released CP males , despite maintaining a sustained presence of CP males on TOA for more than 200 days and releasing more than 100 , 000 CP males . We note that , even with the introduction of a CP female into a population , the outcome may not be the establishment of the B-type Wolbachia . If a CP female were released , she must mate with a compatible male , blood feed and successfully oviposit . For the infection to become established , any resulting progeny must survive and compete successfully against wild type conspecifics . As described above , sons are unlikely to transmit Wolbachia . Daughters are expected to inherit the B-type Wolbachia , but must mate with compatible males and survive to oviposit . Prior comparisons show that CP immature and adult females display lower fitness relative to wild type mosquitoes [30] . The results support the continued development of additional methods in support of larger downstream applications . In particular , improved sex-separation tools can simplify the production process and reduce overall costs . This can include the development of methods to ‘inactivate’ any females that are unintentionally released [22] . The results show that following mass production , sex separation and delivery , CP males are competitive mates under field conditions . Existing methods were adequate for biological containment of the released Wolbachia type . An impact on the targeted population was observed despite relatively small release numbers . The results are consistent with traits desired for an IIT approach and encourage additional trials in which CP males are released at a larger scale and at an epidemiologically relevant site . Furthermore , the results support the continued development and expansion of the IIT approach to additional medically important systems [17] . | Additional tools are required to mitigate mosquito borne disease in the South Pacific , including human lymphatic filariasis ( LF ) . Wolbachia are obligate intracellular bacteria that occur in a majority of insect species and that cause a form of conditional sterility in mosquitoes . Prior work demonstrates that male Aedes polynesiensis mosquitoes , which are artificially infected with Wolbachia ( i . e . , transinfected ) can effectively sterilize wild type females in the laboratory , suggesting the potential applied use of Wolbachia as a pesticide for this medically important mosquito . As a critical intermediate step toward the development of the Wolbachia pesticide approach , we report on the field competitiveness of transinfected males and the risk of accidental horizontal transmission of Wolbachia from transinfected males . The outcome of laboratory cage trials and a thirty-week open release field trial provide evidence against horizontal transmission of Wolbachia from the transinfected males . Additionally , the field trial provides evidence for the competitiveness of transinfected males for indigenous female mates , as indicated by the failure of brood hatch and a resulting population level impact . No residual Wolbachia was detected in the targeted population during or after the male releases , showing released males to be ‘dead end hosts’ for Wolbachia . We discuss the results in relation to a disease control approach that integrates vector control with existing measures against LF . | [
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] | 2012 | Open Release of Male Mosquitoes Infected with a Wolbachia Biopesticide: Field Performance and Infection Containment |
The cereal pathogen Fusarium graminearum produces secondary metabolites toxic to humans and animals , yet coordinated transcriptional regulation of gene clusters remains largely a mystery . By chromatin immunoprecipitation and high-throughput DNA sequencing ( ChIP-seq ) we found that regions with secondary metabolite clusters are enriched for trimethylated histone H3 lysine 27 ( H3K27me3 ) , a histone modification associated with gene silencing . H3K27me3 was found predominantly in regions that lack synteny with other Fusarium species , generally subtelomeric regions . Di- or trimethylated H3K4 ( H3K4me2/3 ) , two modifications associated with gene activity , and H3K27me3 are predominantly found in mutually exclusive regions of the genome . To find functions for H3K27me3 , we deleted the gene for the putative H3K27 methyltransferase , KMT6 , a homolog of Drosophila Enhancer of zeste , E ( z ) . The kmt6 mutant lacks H3K27me3 , as shown by western blot and ChIP-seq , displays growth defects , is sterile , and constitutively expresses genes for mycotoxins , pigments and other secondary metabolites . Transcriptome analyses showed that 75% of 4 , 449 silent genes are enriched for H3K27me3 . A subset of genes that were enriched for H3K27me3 in WT gained H3K4me2/3 in kmt6 . A largely overlapping set of genes showed increased expression in kmt6 . Almost 95% of the remaining 2 , 720 annotated silent genes showed no enrichment for either H3K27me3 or H3K4me2/3 in kmt6 . In these cases mere absence of H3K27me3 was insufficient for expression , which suggests that additional changes are required to activate genes . Taken together , we show that absence of H3K27me3 allowed expression of an additional 14% of the genome , resulting in derepression of genes predominantly involved in secondary metabolite pathways and other species-specific functions , including putative secreted pathogenicity factors . Results from this study provide the framework for novel targeted strategies to control the “cryptic genome” , specifically secondary metabolite expression .
Histone lysine methylation provides an epigenetic layer for transcriptional regulation , with particular methylation sites associated with active ( H3K4me2/3 ) or repressive ( H3K9me2/3 and H3K27me2/3 ) regions of chromatin [1] . Polycomb group ( PcG ) transcriptional repressors that generate and read the H3K27me3 mark were first genetically identified in Drosophila as negative regulators of Hox developmental genes [2]; they repress many additional developmental regulators by generating “facultative heterochromatin” [3] , [4] , [5] . Certain genes can be associated with both activating ( e . g . , H3K4me2/3 ) and silencing ( e . g . H3K27me3 ) marks , and thus form “bivalent domains” [6] that are thought to be metastable and poised for either repression or activation during differentiation and development . The precise location of the two marks across genes is different , however , as H3K4me3 is found at the transcriptional start site ( TSS ) or directly downstream of it , while H3K27me3 is found both up- and downstream of the H3K4me3 peaks [7] . PcG repressive marks are opposed by activating H3K4me marks that are established by Trithorax group ( TrxG ) proteins in Drosophila [8] . In human [9] , [10] , Arabidopsis [11] , [12] , and yeast [13] , [14] , active gene promoters are associated with H3K4me3 , and H3K4me2 serves as an epigenetic memory of prior transcription . H3K36me3 methyltransferases are associated with elongating RNA polymerase and generate this mark at the 3′ end of transcribed genes [15] . With this study we are beginning to uncover an important physiological role for PcG proteins in fungi . Previously , H3K27me3 had been detected in Neurospora crassa [16] but its function in this species remains unclear [17] . In our studies on centromeres of the cereal pathogen Fusarium graminearum ( teleomorph: Gibberella zeae ) , we found that H3K27me3 was absent from pericentric regions , similar to what has been found in plants [18] , [19] . By chromatin immunoprecipitation ( ChIP ) followed by high-throughput DNA sequencing ( ChIP-seq ) , we found that extensive segments , covering a third of the genome , were enriched with H3K27me3 in F . graminearum . Here we show that a Fusarium homologue of the Drosophila H3K27 methyltransferase Enhancer of zeste [E ( z ) ] , an enzyme we call KMT6 in accordance with proposed nomenclature [20] , generates H3K27me3 marks that cover 46% of all genes . As expected , a majority of these genes is not expressed in wildtype cells , while in the absence of KMT6 an additional 14% of all genes were induced . These were predominantly genes involved in the production or detoxification of secondary metabolites or predicted to play a role in pathogenicity . We provide a chromatin-based model for coordinated expression of many gene clusters predominantly located in extant or ancestral subtelomeric regions and containing species-specific genes of unknown function .
We searched the currently available ∼200 fungal genomes for putative homologs of PRC1 and PRC2 components ( Table 1 ) . Budding yeast , Saccharomyces cerevisiae , and fission yeast , Schizosaccharomyces pombe , have been intensely studied but no H3K27 methylation has been detected . This is consistent with the absence of genes for known PRC2 or PRC1 complex components in their genomes [21] . Several other model fungi , e . g . the human pathogen Aspergillus fumigatus , the non-pathogenic Aspergillus nidulans and the widely used plant pathogen Ustilago maydis also lack genes for known PRC components . The human pathogens Cryptococcus neoformans and Cryptococcus gattii have homologues for KMT6 and EED with other PRC components not discernable by even low-stringency BLAST searches [21] . In contrast , many filamentous ascomycetes , such as the model organism N . crassa [17] and the widely studied genus Fusarium contain a full complement of PRC2 components , with one homologue each for EZH , EED , SUZ12 and the ubiquitous Nurf-55/RbAP46/48 . All fungal genomes we have investigated , however , lack Pc or other PRC1 subunits , and the components of known PRC2-targeting complexes ( Table 1 ) , suggesting that gene repression by PRC2 is mediated by a mechanism that is different from that in plants or animals [21] , [22] , [23] . Here we focus on F . graminearum KMT6 , the homologue of Drosophila E ( z ) and human EZH2 [22] , [23] , which has been identified in N . crassa as SET-7 [17] . Both fungal KMT6-type histone methyltransferases ( HMTs ) are significantly longer than the metazoan proteins ( Fig . S1 ) , and outside of the recognizable pre-SET ( or CXC ) and SET domains essential for HMT activity [24] there is little similarity to the metazoan proteins . Esc/EED- , Su ( z ) 12/SUZ12- and RNA-interaction motifs found in metazoan KMT6 proteins [23] are not recognizable by sequence comparisons , yet long stretches between KMT6 proteins from various fungi are conserved , suggesting the presence of fungal-specific motifs ( data not shown ) . The KMT6 CXC domain is characterized by cysteine repeats ( four Cys-X-Cys motifs interrupted by variable length spacers and single C residues ) , similar to the canonical CXC motif [25] , which is important for substrate recognition and enzymatic activity [24] . All four proteins have similar CXC domains ( Fig . S1 ) when compared to other HMTs [24] , suggesting that their catalytic motifs are more closely related than to HMTs with different substrates , e . g . KMT1 , an H3K9-specific KMT . Important conserved stretches common to all bona fide HMTs are present in both KMT6 and Neurospora SET-7 ( Fig . S1 ) , including the invariable tyrosine that is involved in catalysis [24] . The residues preceding this tyrosine ( GEELFF ) are more conserved within KMT6 proteins than in HMTs in general , again suggesting that KMT6 and SET-7 are most similar to E ( z ) and EZH2 . To understand genome organization of F . graminearum , we performed ChIP-seq with antibodies against histone modifications known to be associated with active ( H3K4me2 ) or silent ( H3K9me3 and H3K27me3 ) chromatin . H3K4me2 and H3K27me3 were found in large , mutually exclusive , gene-rich blocks of the genome ( Fig . 1 ) . About one third of the F . graminearum genome is associated with H3K27me3 when the fungus is grown in minimal medium with low nitrogen; global H3K27me3 enrichment is slightly reduced in high nitrogen medium . More than half ( 58% ) of chromosome 2 is covered by this silencing mark ( Fig . 1 ) . Comparative genome studies suggested that the four chromosomes of F . graminearum are the result of chromosome fusion events , as the most closely related Fusarium species have between 11 and 15 chromosomes each , and SNP maps between two different strains suggested recombination patterns that mark some internal regions of F . graminearum as ancestral subtelomeres [26] , [27] . We thus constructed synteny maps between F . graminearum , F . oxysporum , and F . verticillioides and compared them to histone modification maps ( Fig . 1 ) . H3K4me2 ( green track ) is found in well-conserved regions with high synteny between the three species . In contrast , H3K27me3 ( orange track ) is found in non-syntenic blocks unique to F . graminearum , predominantly in subtelomeric regions . In agreement with the chromosome fusion hypothesis , we found extended internal blocks of H3K27me3 on each chromosome that may constitute ancestral subtelomeric regions . H3K27me3 colocalizes with regions of high SNP density between the reference genome strain ( PH-1 ) and a second wild-collected strain that we re-sequenced , 00-676 [28] ( Fig . 1 , black histogram ) . In complementary experiments we observed similar patterns for the distribution of H3K27me3 in other Fusarium species , e . g . F . verticillioides , F . asiaticum and F . fujikuroi ( L . R . Connolly , L . Studt , K . M . Smith , B . Tudzysnki , S . -H . Yun and M . Freitag , unpublished data ) . Subtelomeric regions in filamentous fungi are enriched for lineage- or species-specific genes , for example secondary metabolite gene clusters , as well as genes for secreted pathogenicity factors and detoxifying enzymes [29] , [30] . Our observations led us to ask if genes enriched for H3K27me3 are repressed , and if these genes become active when H3K27me3 is removed by mutation of KMT6 . We identified the F . graminearum kmt6 gene ( FGSG_15795 . 3 ) based on BLAST searches with Drosophila E ( z ) . We used targeted gene replacement to disrupt kmt6 ( Fig . S2A ) . PCR and Southern analyses confirmed replacement of kmt6 in colonies that were exceptionally orange in pigmentation , suffered aberrant germination patterns and stunted growth . Southern blots showed absence of the kmt6 gene and replacement with neo+ ( confers G418 resistance ) in the mutant transformant ( Fig . S2A ) . A kmt6 mutant ( FMF248 ) with perfect neo+ integration was chosen for further studies . To test if H3K27 methylation was altered in the kmt6 mutant , we purified histones and carried out western analyses with antibodies against methylated histones . H3K27me3 was present in WT but completely absent from the kmt6 mutant ( Fig . 2A ) . We tested several different antibodies raised against H3K27me3 peptides ( Fig . 2A , Fig . S2B ) . All showed absence of H3K27me3 in kmt6 . Levels of H3K4me2 and another activating mark , H3K36me3 , are equivalent in WT and kmt6 ( Fig . S2B ) , suggesting that lack of H3K27me3 does not result in an overall increase in H3K4 or H3K36 methylation . H3K9me3 , while present , proved difficult to detect in F . graminearum by western blot ( Fig . S2B ) , matching our expectations from ChIP-seq ( data not shown ) . Levels of H3K27me3 were not altered in strains in which H3K9me3 was abolished by deletion of the single Fusarium Su ( var3-9 ) homologue ( kmt1 ) or in which Heterochromatin Protein-1 ( HP1 ) was deleted ( hpo ) ( Fig . 2A ) . We conclude that KMT6 has specificity for H3K27 , and that KMT6 is the sole or predominant H3K27 methyltransferase in F . graminearum . We repeated ChIP-seq of H3K4me2 and H3K27me3 , as well as H3K4me3 and H3K36me3 , in WT , kmt6 and the complemented strain under nitrogen limiting and nitrogen abundant conditions ( Fig . 2B , Table S1 ) . We used different nitrogen levels as one environmental factor that is known to affect gene regulation in many fungi . In the kmt6 mutant , H3K27me3 enrichment was completely lost , and only background genomic sequence was obtained by ChIP-seq ( Fig . 2B , Fig . S3 ) . After re-introduction of a wildtype kmt6 allele , H3K27me3 was restored to levels almost indistinguishable from WT . Our results confirm that H3K27me3 is generated by KMT6 , and that restoring gene function restores H3K27me3 enrichment in all regions by de novo mechanisms that may be similar to those in animals or plants . Certain blocks previously enriched with H3K27me3 showed acquisition of H3K4me2/3 in kmt6 mutants . No obvious differences in enrichment were observed between high and low nitrogen conditions when viewed at the whole chromosome level . Similarly , discrete H3K36me3 enrichment in genic regions was not resolvable at the whole chromosome scale . H3K4me2 and H3K4me3 were found in overlapping regions , mutually exclusive of H3K27me3 . All kmt6 mutants obtained are sterile , have morphological defects , and are altered in pigment production . We observed slower linear growth and morphological changes in kmt6 compared to WT on both minimal ( MIN ) and rich ( YPD ) media ( Fig . 3A–B ) . Plates are covered by WT after a few days , so we used Ryan ( “race” ) tubes [31] to carry out long-term growth experiments . We measured linear extension of WT and kmt6 for one month ( Fig . 3B ) . On both plates and in race tubes , linear growth on minimal medium was faster than on the richer YPD medium , but colonies on YPD grew more densely . Overall , WT growth was more than two-fold faster than kmt6 growth . We compared growth of kmt6 and WT in a wounded tomato assay [32] and found that kmt6 was unable to colonize fruit ( Fig . S4 ) , suggesting that the mutant has reduced pathogenicity . To show that kmt6 was responsible for the defects described , we complemented a mutant strain ( FMF248 ) with a wildtype allele of kmt6 flanked by the hph+ gene ( confers hygromycin resistance; Fig . S2A ) . The complemented strain ( FMF282 ) retains the neo marker at the endogenous kmt6 locus , but has an insertion of kmt6 and hph at an ectopic locus . The intensity of kmt6 probing and multiple hybridizing bands in the complemented strain suggest multiple tandem insertions of the wildtype kmt6 gene . The complemented strain showed intermediate growth rates , faster than the mutant but not fully restored to WT growth levels , and almost normal pigmentation on minimal medium ( Fig . 3 A–B ) . On rich medium ( YPD ) , WT grew roughly two fold faster than kmt6; on this medium the complemented strain grew as well as WT ( Fig . 3B ) . Fusarium graminearum is a homothallic , or self-fertile , fungus . When placed on carrot agar ( CAR ) , WT strains undergo sexual development to generate dark pigmented fruiting bodies , “perithecia” ( Fig . 3C , CAR ) . Depending on environmental conditions ( temperature , humidity ) , a selfing takes 10 to 14 days . After this time , ripe ascospores are shot or ooze from perithecia in cirrhi [33] , [34] . We found that kmt6 is completely infertile and does not undergo even the earliest stages of sexual development . The complemented strain initiated normal development , though production of ascospores took about twice as long as for WT strains ( Fig . 3C ) . We attempted to force heterokaryons between kmt6::neo+ and hph+ strains of the same lineage . When co-inoculated , heterokaryons never formed on selective medium , suggesting anastomosis defects; only the hph+ sectors were able to generate perithecia with viable ascospores . We carried out protoplast fusions to complement the kmt6 deletion by formation of [kmt6+kmt6+] heterokaryons ( Fig . 3C ) . No sexual development was observed even after extended periods of incubation , unlike for wildtype or complemented strains , which look similar to regular selfings but take 2–3 days longer to mature . We were able to isolate double-resistant G418+ and Hyg+ colonies from both the edge and center of the [kmt6+kmt6+] colonies on carrot agar , indicating that the heterokaryon had not broken down ( Fig . 3C ) . We conclude that the sexual differentiation defect can be complemented by transformation but surprisingly not by fusion with mycelia that should be competent for sexual development . These experiments suggest existence of dominant factors produced by kmt6 nuclei that may inhibit H3K27me3 regulation in kmt6+ nuclei or act as dominant factors inhibiting sexual development . Action of these factors cannot be easily overcome by hyphal fusion and further studies to unravel this gene regulatory developmental switch are underway . To distinguish genes that are enriched for a particular histone modification from those with background levels of ChIP-seq reads we used EpiChIP [35] , which calculates values for “normalized locus chromatin state” ( NLCS ) and false discovery rates for each gene . The NLCS is the area under each ChIP-seq peak , in a specified window , normalized for the length of the window and the sequencing depth . For our analysis we used genes from the current Broad Institute annotation ( http://www . broadinstitute . org/annotation/genome/fusarium_group/MultiHome . html ) as the window , without addition of upstream or downstream sequences . For most histone modifications we found two peaks in the NLCS distribution ( Fig . 4 , right panels ) , one for background signals ( B ) and one for enrichment ( E ) . In WT , H3K27me3 enrichment extended across gene bodies but was absent near the TSS , and genes with background levels ( log2∼3 ) are clearly distinguishable from genes with enrichment ( log2∼6 ) . In kmt6 , only background signal remained for H3K27me3 , resulting in a single peak in the NLCS distribution . As expected , H3K4me2 and -me3 enrichment were most pronounced near the 5′ end of genes , while H3K36me3 was found more enriched near the 3′ end of genes ( Fig . 4 ) . A single peak is observed in the distribution of H3K36me3 , but unlike H3K27me3 in kmt6 , the single peak in H3K36me3 NLCS distribution represents enrichment . The kmt6 mutant revealed similar patterns of enrichment for H3K4 and K36 methylation across genes when compared to WT . We performed RNA-seq on WT and kmt6 strains in high and low nitrogen conditions to investigate whether gene expression correlates with histone modifications in the expected manner . We used Tophat to map reads obtained by RNA-seq , and cufflinks to calculate reads per kilobase of transcript per million mapped reads ( RPKM ) , a value representative of gene expression and normalized for both transcript length and sequencing depth [36] . For each condition we plotted the RPKM of each gene from each of two biological replicates ( Fig . 5A ) . For most genes the replicates produced similar RPKM values , and all points fall near a line with a slope of 1 . Not unexpectedly , most of the variation was observed in genes with low expression . Comparing expression of genes from WT or kmt6 at low compared to high nitrogen ( Fig . 5B ) showed that a relatively small percentage of genes has altered expression in response to nitrogen levels . Overall we observed a trend toward decreased gene expression in low nitrogen , shown by the smooth fit regression line ( Fig . S5 ) . The kmt6 mutation caused a larger change in global gene expression than changing nitrogen availability , a well-studied environmental factor affecting expression of known metabolites [37] . The overall trend was towards increased gene expression in kmt6 . The distribution of RPKM values for all genes in each condition revealed that high nitrogen caused repression of only 5–10% of all genes in both strains , while the kmt6 mutation released repression of 15–30% of all genes; many of these were repressed by high nitrogen levels ( Fig . S5 ) . To address if histone modifications are truly predictive of gene expression in F . graminearum , we show the range of RPKM values plotted against enrichment of histone modifications , expressed as the normalized NLCS values from EpiChIP ( Fig . 5C ) . Overall , H3K27me3 enriched genes had low RPKM values; most genes enriched for H3K27me3 ( NLCS>16 ) had RPKM values <10 , indicating very low expression . There are several genes , however , with high H3K27me3 enrichment and RPKM>100 , suggesting gene expression in the presence of a usually silencing histone modification . As one would expect , genes with high values of enrichment for H3K4me2/3 also tended to have higher RPKM values , and for both H3K4me2 and H3K4me3 genes with no enrichment tended to have low expression levels , suggesting a stronger correlation between enrichment with H3K4me2/3 and expression than presence of H3K27me3 and silencing . H3K4me2 was found in far more genes than H3K4me3 . H3K36me3 was found in nearly all genes , regardless of expression level . We classified genes grown in low nitrogen as expressed or silent in both WT and kmt6 strains based on the distribution of RPKM values ( Fig . 5C ) . When comparing WT to mutant , we found that in WT 8 , 855 ( or 66% of all annotated 13 , 354 ) genes were expressed ( Fig . 5D ) . Of these , 1 , 627 genes were not associated with any of the histone modifications investigated . More than 30% of all expressed genes ( 2 , 760 of 8 , 855 ) were significantly enriched for the silencing H3K27me3 mark , though many of these genes showed H3K27me3 enrichment just above background levels . The 4 , 449 silent genes ( 33% of 13 , 354 annotated genes ) in WT were largely associated with H3K27me3 ( 76% or 3 , 373 of 4 , 499 ) , but almost 200 silent genes also had some significant H3K4me2/3 enrichment . When H3K27me3 is lost by kmt6 mutation , the number of expressed genes jumps to 10 , 635 ( Fig . 5D ) ; this number does not include genes that are expressed in WT yet are more highly expressed in kmt6 . Only half of these genes are newly enriched for H3K4me2/3 . The other half has none of the investigated modifications . Overall we found that about 14% of the genome is derepressed by absence of H3K27me3; many additional genes are overexpressed in kmt6 compared to WT . We immediately realized that regions of KMT6-dependent repression are home to secondary metabolite ( SM ) gene clusters , and thus generated heatmaps of expression changes for all primary and secondary metabolite genes to visualize the effect of high compared to low nitrogen and kmt6 mutation on expression of these genes . Growth in low and high nitrogen was compared because nitrogen is a known regulator of many SM gene clusters [37] . Primary metabolite ( PM ) genes were largely unaffected by either mutation of kmt6 or growth in high nitrogen ( Fig . 6A ) . Specific sets of genes , summarized in the clustered heatmap with 8 k-means ( Fig . 6A , right panel ) stand out as being repressed in high nitrogen ( 245 genes in cluster 7 ) , or induced by kmt6 mutation ( 41 genes in cluster 5 and 49 genes in cluster 2 ) . The genes repressed in high nitrogen include six out of 17 genes in the gluconeogenesis I pathway , and several genes for carbohydrate metabolism including glycolytic enzymes ( Table S2 ) . The 90 genes induced in kmt6 are enriched for genes involved in carbohydrate binding and degradation , peptidases , and cell signaling components . Five of the 90 genes , although classified as primary metabolic genes , are part of SM clusters ( carB , fus1 , tri5 , a kinase belonging to the zon pathway , and FGSG_10615 ) . In contrast to PM genes , the complete set of SM genes ( Fig . 6B ) was overall more derepressed in kmt6 ( 34% of SM genes compared to 6% of PM genes ) , but a smaller fraction was repressed in high nitrogen ( 10% of SM genes compared to 18% of PM genes ) . The ten genes repressed in high nitrogen ( Table S2 ) include most of the aurofusarin cluster ( aurO , aur1 , aurC , aurJ , aurF , gip1 and aurS ) , plus an ammonium permease from the carotenoid cluster , as well as pks1 and a multidrug resistance protein , both from SM cluster FG3_38 , which generates an unknown product [27] . Since our nitrogen source was ammonium nitrate , it is not surprising that an ammonium permease was downregulated . The 36 genes derepressed in kmt6 ( Table S2 ) include carO and carX from the carotenoid cluster , nine genes from the fusarin C cluster ( fus1 , fus2 , fus3 , fus5 , fus6 , fus7 , fus8 , and two other unnamed genes ) , five genes from cluster FG3_20 and six genes from FG3_40 , which both generate unknown products [27] . These 36 genes are the most derepressed SM genes in kmt6 ( log2 fold change >4 , or more than a 16-fold induction ) . Many other genes are derepressed to a smaller but still significant degree . To show that SM genes are found most often in KMT6-repressed regions we mapped genome-wide changes in gene expression ( log2 kmt6/WT ) , distribution of H3K27me3 , and genes for cytochrome P450 enzymes and gene clusters containing polyketide synthases ( PKS ) or non-ribosomal peptide synthases ( NRPS ) onto F . graminearum chromosomes ( Fig . 7A ) . We also generated heatmaps of expression data for these groups of genes ( Fig . 7B ) . Families of cytochrome P450s and PKSs are proposed to have evolved by gene duplication and divergence [38] , [39] . Most cytochrome P450 , PKS , and NRPS genes were enriched for H3K27me3 ( Table 2 ) . Several , but not all , cytochrome P450s were derepressed in kmt6 , notably tri4 ( FGSG_03535 ) and tri11 ( FGSG_03540 ) involved in deoxynivalenol ( DON ) synthesis , and fus8 ( FGSG_07804 ) in the fusarin C pathway . Other cytochrome P450 genes were repressed in high nitrogen , and generally derepressed in kmt6 to a much greater degree than when comparing high to low nitrogen conditions . These include a block of contiguous genes on chromosome 1 , FGSG_02111 , FGSG_02113 , FGSG_02114 , FGSG_02117 , and FGSG_02118 . The products of these genes are unknown , but the neighboring gene , FGSG_02115 , encodes a TRI7 ( toxin biosynthesis protein ) homolog and FGSG_02116 encodes an NAD-dependent epimerase or dehydratase , suggesting the existence of a novel SM cluster . The effects of KMT6 on the expression of known SM clusters with NRPS , PKS , DTC and STC signature genes are summarized and contrasted to the effects of nitrogen ( Table 2 ) . Of 45 clusters , 35 are enriched with H3K27me3 in both low and high nitrogen conditions , compared to 21 of 45 that are repressed by high nitrogen levels . In kmt6 , 32 of the 45 clusters are expressed with low ( 11/32 ) , high ( 6/32 ) or either nitrogen levels ( 15/32 ) . In contrast , in WT only five clusters are expressed constitutively , five are expressed in high nitrogen , 14 in low nitrogen and 21 remained silent regardless of the nitrogen level . Most of these gene clusters have unknown functions and putative compounds generated have not been defined for 29 of the 45 clusters shown . Overall , manipulation of H3K27me3 levels proved more successful for expressing these “cryptic” clusters than changes in nitrogen level . To illustrate effects of kmt6 at the gene level , changes in histone modifications and expression are shown for two representative SM gene clusters ( Fig . 8 ) . The fusarin C ( fus ) mycotoxin cluster was induced in kmt6 when H3K27me3 was lost , yet H3K4me2 enrichment was barely above background levels ( Fig . 8A ) . Nearly every gene in the fus cluster was induced more than 64-fold in both low and high nitrogen . Both fus6 ( G , FGSG_07803 ) , encoding a transporter , and fus8 ( I , FGSG_07804 ) , encoding a cytochrome P450 , acquired small peaks of H3K4me2 . The genes that had increased expression also lost enrichment of H3K36me3 . Overexpression of the fusarin C cluster genes can cause production of various fusarins [40] . The carotenoid cluster ( car ) encodes the enzymes required to synthesize the pigments neurosporaxanthin and torulene [41] , resulting in the orange kmt6 culture liquid and mycelium grown on plates ( Fig . 8 B and C ) . The transcription factor gene carR ( F ) was induced 3-fold in low nitrogen only , but in both high and low nitrogen the biosynthetic enzymes carO ( B ) , carB ( C ) , carRA ( D ) , and carX ( E ) were induced more than 4-fold in kmt6 . The carO gene acquired some H3K4me2 in high nitrogen , but none of the other genes in the cluster were enriched for H3K4me2 . The reduction in H3K36me3 , seen in the other examples at genes with increased expression , was most pronounced in the car cluster at gene G ( FGSG_03069 , dihydrodipicolinate synthetase ) . WT cultures of F . graminearum produce multiple dark red pigments in nitrogen limiting conditions , but expression is repressed under high nitrogen conditions in the dark [42] . In contrast , the culture supernatant of kmt6 was reproducibly bright red in low nitrogen , and turned bright orange in high nitrogen ( Fig . 8C ) . The predicted secretome [43] , composed of putative effector proteins required for virulence and also including plant cell wall degrading enzymes , phytotoxins and antifungals , is largely encoded in the same regions of the genome where we mapped the SM cluster genes , and secreted protein genes are overwhelmingly enriched for H3K27me3 ( Table S2 ) . In summary , the partially overlapping sets for SM gene clusters and secretome genes are localized to subtelomeric regions , enriched for H3K27me3 , and induced in kmt6 . Many genes with unknown function in the same regions follow these general trends , and we predict that they also function in pathogenicity or niche adaptation . The newly found ability to express many of these genes in a single mutant and in vitro represents an important step forward in the functional characterization of natural products , not just in F . graminearum but also in a wide variety of additional species .
One of our goals is to understand the genome organization of F . graminearum and the various types of chromatin associated with specific regions . To this end we carried out ChIP-seq with antibodies against di- or trimethylated H3K4 ( H3K4me2/3 ) as proxies for nucleosomes that are associated with active chromatin segments , or H3K27me3 for facultative heterochromatin . H3K4me2/3 and H3K27me3 were found in mutually exclusive , gene-rich blocks of the genome , as reported for mammals [44] . The patterns of histone modifications we observed differ from published reports of genome-wide patterns in other fungi . As mentioned above , both budding and fission yeast lack KMT6 homologs to generate H3K27me3 [21] . The best-studied filamentous fungus , N . crassa , has H3K4me2 in nearly all gene-rich chromatin , but large , heterochromatic , gene-poor , repeat-rich blocks near telomeres and centromeres are enriched with the silencing H3K9me3 mark [16] , [45] , [46] . In Neurospora , H3K27me3 is found in smaller blocks that cover genes and heterochromatic repeats close to telomere ends and these are exclusive of H3K9me3 [16] , [17] . Overlap with H3K4me2 distribution has not been studied in detail in Neurospora . Various species of Aspergillus seem to use H3K9me3 to silence subtelomeric gene clusters [47] , although genome-wide studies have not been published . All Aspergillus species lack clear KMT6 homologues ( our data and [21] ) . This suggests that different clades of filamentous fungi make use of different chromatin-based regulatory systems to control SM gene clusters . We assessed distribution of histone modifications across the “average” gene . Overall , H3K27me3 , H3K4me2/3 and H3K36me3 distributions across genes and proximal promoters were similar to previous results from plants , fungi and animals: H3K4me2/3 were most pronounced near the 5′ end of genes , while H3K36me3 was found more enriched near the 3′ end of genes . In N . crassa H3K4me2 is enriched uniformly throughout the gene body [48] , while H3K4me3 is enriched in 5′ ends of genes [45] and H3K36me3 is enriched in 3′ ends of genes [48] . Our findings agree with published animal studies [7 , with the exception of the vast extent of H3K36me3 enrichment in nearly all genes . The H3K36me3 KMT is thought to function in association with elongating RNAP and only modify actively transcribing genes [15] . However , in F . graminearum nearly all genes are significantly enriched for H3K36me3 , though almost half of all genes are not expressed in WT . Preferential enrichment of activating marks in exons compared to introns observed in Caenorhabditis elegans [49] was not found in F . graminearum . H3K36me3 can regulate mismatch repair by interactions with human MutS homologues [50] , suggesting additional roles for H3K36me3 beyond transcription elongation . To uncover the meaning of the strong H3K36me3 enrichment will require additional studies . Nevertheless , the overall patterns of enrichment for H3K4 and H3K36 methylation across genes were not altered in kmt6 , suggesting that there is little feedback into genic distribution of these marks by H3K27me3 or other putative activities of KMT6 . Enrichment of H3K4me2/3 at certain genes was altered in the absence of H3K27me3 , suggesting that nucleosomes with activating marks are incorporated into chromatin in the absence of the silencing H3K27me3 modification . While 627 genes were enriched with H3K27me3 and H3K4me2/3 , a hallmark of “bivalent” regions [6] , most of these genes had strong H3K4me2/3 enrichment in combination with H3K27me3 enrichment just above background levels . Examination of genes with strong enrichment for H3K4me2/3 and H3K27me3 did not reveal a functional enrichment for any particular group of genes . Thus , our data suggest that bivalent promoters or genes can occur in F . graminearum but additional work on the biological function of these regions is needed to confirm results from our genome-wide analyses . In Arabidopsis thaliana , expressed genes are associated with H3K4me3 and repressed genes are associated with H3K27me3 , but 13% of genes are marked with both modifications , including genes with tissue-specific expression and for some TFs that are poised for transcription [51] . Many individual genes , however , are thought to have one or the other modification , where the observed bivalency may have been caused by fractions of mixed nuclei [51] , something we cannot exclude for F . graminearum . There were 331 genes enriched with H3K27me3 that showed at least twofold decrease in expression in kmt6 . These include all four ammonium transporters , three out of six nucleoside permeases , amino acid and oligopeptide permeases , and hydrolases . As recently discussed for Drosphila [52] , kmt6 and the genes for the other PRC2 components showed significant H3K27me3 enrichment while they were expressed . The observed decrease in expression of some genes upon loss of H3K27me3 may be due to indirect effects , but it remains possible that H3K27me3 is in some cases required for transcription , which will be subject to further investigation . Lack of H3K27me3 resulted in activation of ∼14% of all predicted or known genes ( 1 , 780/13 , 354 ) that were silent in WT . It remains to be seen how many genes are activated directly ( e . g . by virtue of “poised” promoters ) and how many are activated indirectly ( e . g . by involving additional cis- or trans-acting factors that are controlled by KMT6 ) . Many genes ( 2 , 720/13 , 354 or ∼20% of the genome ) remain silent even in the absence of H3K27me3 , and 2 , 575 silent genes possessed none of the investigated modifications . This suggests that while transcription may be the default state in the absence of H3K27me3 regulation for many genes , additional activating factors may be required or some genes are subject to multiple layers of repression . In budding yeast , H3K4me2 is found in all euchromatic genes , and H3K4me3 is found in actively transcribing or recently transcribed genes [14] , [53] . While our study does not address issues of RNA stability , it is likely that many actively transcribing genes in F . graminearum lack H3K4me3 under our conditions , are associated with H3K4me2 or even with unmodified H3K4 . Our results suggest that “activating” histone modifications are not absolutely required for transcription and that their deposition at transcribed regions is slow and perhaps a secondary event to transcription . Taken together our results suggest a testable model in which absence of the silencing mark H3K27me3 removes an immediate block to transcription , allowing access to promoters by the basal transcription machinery or completion of the initiation phase of transcription by pre-assembled , or “poised” , transcription machineries on promoters . It appears that activating histone modification marks , such as H3K4me2/3 are only much later , if ever , deposited on these actively transcribing regions , as our growth experiments were carried out over several days . Curiously , the appearance of H3K4me2 appears to correlate with a reduction in H3K36me3 modification , even though all previous data supports the acquisition of both marks before or during transcription [14] , [15] , [53] , [54] . Pigment production was very much altered in kmt6; the exact pigment profile of kmt6 grown under various conditions is the subject of an ongoing study ( K . M . Smith , J . Gautschi , L . R . Connolly , M . Freitag , unpublished data ) . From the kmt6 expression data , some of it summarized in Table 2 , it appears that repression by high nitrogen can be overridden by loss of H3K27me3 . Overall , loss of H3K27me3 had more drastic effects on expression of SM gene clusters than the intensely studied regulation by nitrogen . For this study we did not measure concentrations of specific known or unknown metabolites , but previous work from several laboratories suggests that increased transcription from SM clusters by manipulation of nitrogen levels , histone H3K9 acetylation or H3K4 methylation levels results in overproduction of certain metabolites [37] , [40] , [55] , [56] . How exactly linear growth is retarded in kmt6 may be difficult to ascertain . One possibility is that increased synthesis of pigments , other secondary metabolites and detoxifying enzymes may account for the slower growth of kmt6 , either indirectly by shifting energy utilization away from primary metabolism or by direct toxic effects mediated by combinations of usually harmless metabolites . For example , the red pigment , aurofusarin , is synthesized by the aur gene cluster , which includes the polyketide synthase gene aur1/pks12 [42] , [57] . A Δaur1 mutant grew faster and generated more conidia than WT on media inducing aurofusarin production [57] , suggesting that costs are incurred by the production of specific metabolites . It remains to be seen how or if H3K27 methylation is altered in aur1 and similar mutants . Attempts to activate individual silent clusters for chemical genome mining has largely focused on overexpression of cluster-specific regulators , mostly transcription factors [58] , [59] , [60] or heterologous expression of partial or complete clusters [61 , [62] , [63] . A more general approach to activate silent gene clusters involves treatment with inhibitors of DNA or histone modifying enzymes . Silent clusters were activated by histone deacetylase inhibitors or DNA methyltransferase inhibitors in Cladosporium cladosporioides [64] and A . niger [65] , or by cocultivation with other organisms , e . g . bacteria or fungi , to mimic a natural environment [56] , [66] . These approaches were successful in inducing a few SM clusters , but they are little different from previous attempts to find the exact culture conditions for expression of specific gene clusters . Another strategy to activate silent clusters is focused on global regulators of secondary metabolism . Mutation of selected histone-modifying enzymes predicted to be global gene regulators , e . g . the histone deacetylase HdaA [67] , the CclA component of the H3K4MTase complex [55] , [68] and the histone acetyltransferase EsaA [69] proved successful in affecting certain clusters in Aspergillus . Individual SM gene clusters are affected in different ways by mutating or overexpressing these enzymes and the effects are not specific to SM clusters . The most widely studied general regulator is the “Velvet complex” , first identified in A . nidulans [70] and later also found in F . verticillioides [71] , F . fujikoroi [72] and F . graminearum [73] , [74] . This complex consists of the putative transcription factors VeA , VelB and a putative methyltransferase , LaeA [75] and regulates both fungal development and SM production . In A . nidulans , the complex inhibits asexual reproduction , promotes sexual development and increases SM production in dark conditions [70] . In A . fumigatus , 13 of 22 SM clusters and 20–40% of SM biosynthetic genes were expressed at lower levels in a ΔlaeA strain compared to WT [76] . The molecular mechanism of this pathway presumably involves the methyltransferase domain of LaeA [76] . LaeA controls protein levels and complex interactions between VeA and its partners [77] , a function separable from its role as global regulator of SM gene clusters . The precise function of VeA , VelB , and LaeA in changing transcriptional programs has not yet been determined , but it has been suggested to involve re-programming of the constitutive heterochromatin mark , H3K9me3 [47] , [78] , [79] . Here we revealed a novel mechanism that links fungal development and SM expression , and that appears at least partially conserved with formation of facultative heterochromatin in plants , Drosophila and mammals by generation of blocks of H3K27me3-enriched chromatin . Essentially these blocks generate a “cryptic genome” under normal laboratory culture conditions . There is no indication that this process is dependent on members of the velvet complex . We looked for changes in expression for the “white collar” genes ( i . e . the light-sensing complex that controls VeA activity ) , VeA , VelB , VosA and LaeA , and found no differences in expression between WT and kmt6 . All of these genes were enriched for H3K4me2 and expressed in both WT and mutant . We did , however , find changes in several predicted LaeA homologs whose functions are still largely unknown . Proteins encoded by these genes were found to interact with F . graminearum VeA and named “FgVeA interacting proteins” , or VIP [73] . Conserved VIPs are FgVIP1 ( FGSG_07660 ) , FgVIP2 ( FGSG_03525 ) , FgVIP3 ( FGSG_05685 ) , FgVIP4 ( FGSG_03567 ) , FgVIP5 ( FGSG_08741 ) , and FgVIP6 ( FGSG_03011 ) . All VIP genes were enriched for H3K27me3 , and loss of this modification in kmt6 caused increased transcription . Homologues of VIPs have been studied in A . nidulans , where LlmF ( LaeA-like methyltransferase ) interacts with velvet components and appears to shuttle the complex into the nucleus [80] . Thus it appears possible that LaeA homologs are involved in H3K27me3 regulation . One wonders why gene family expansions and acquisition of SM clusters occurs preferentially in subtelomeric locations . Subtelomeric regions of Aspergillus species contain numerous SM clusters , and based on previous results one would expect to find large H3K9me3-enriched domains in these regions [81] , [82] but this remains to be demonstrated . There is evidence , at least in Magnaporthe and Saccharomyces [83] , [84] , [85] , [86] , that subtelomeric regions are more prone to rearrangements than other regions of the genome . Published synteny maps of F . graminearum , F . verticilliodes , and F . oxysporum , as well as our preliminary results from studies with a close cousin of F . graminearum , F . asiaticum ( L . R . Connolly , K . M . Smith , S . -H . Yun , M . Freitag , unpublished data ) , show that subtelomeric regions are hypervariable between related organisms and accumulate SNP mutations at higher rates than other regions of the genome ( Fig . 1 ) . This suggests a model in which H3K27me3 is involved in the regulation of recombination or chromosome rearrangements . Why are SM genes in clusters ? This can be explained by the “selfish cluster” hypothesis [87] , at least if horizontal gene transfer is not exceedingly rare . Genes in a cluster are more likely to be transferred as a functional group if acquisition and loss of clusters is adaptive to the organism . Selective advantages to the new host organism , specifically by creation of novel clusters and maintenance of all clusters , however , remains unclear . Initially , uptake of novel DNA would not be dissimilar from invading transposable elements , which tend to be silenced by a combination of H3K9me3 and DNA methylation in N . crassa [46] , [88] . Further partitioning of the genome into additional chromatin domains by making use of H3K27me3 that eventually results in coordinate regulation is a plausible hypothesis to explain the maintenance of secondary metabolite genes in clusters . One wonders if subtelomeric silencing depends on PcG proteins in other fungi . So far , we only have data for N . crassa [16] , [17] and several Fusarium species , but many important animal and plant pathogens within the ascomycetes have predicted orthologues for PRC2 components .
Strains were grown in liquid YPD to collect vegetative tissue . To generate macroconidia , a small amount of frozen conidia or tissue was inoculated into 50 ml flasks containing CMC medium [89] and shaken at 150 rpm for 3–4 days at room temperature ( RT , ∼22C ) . Conidia were collected by filtration through cheesecloth and stored at −80C in 25% glycerol . For vegetative growth assays strains were inoculated onto YPD ( 0 . 3% yeast extract , 1% bacto-peptone , 2% dextrose ) or Fusarium Minimal Medium ( FMM; [90] ) agar plates . Crosses were performed on carrot agar at RT , taking usually ∼10 days . To assay pigment production , tissue was generated from macroconidia by shaking 100 ml cultures at 150 rpm in the dark in DVK medium ( 3% sucrose , 1 . 5% corn steep solids , 0 . 1% ( NH4 ) 2SO4 , and 0 . 7% CaCO3 ) for three days , after which 5 ml were used to inoculate 100 ml of liquid ICI medium [91] with 6 mM or 60 mM NH4NO3 for nitrogen limiting or sufficient conditions , respectively . Cultures were grown at 25C at 150 rpm in the dark and observations made after 3 and 7 days of growth . Replacement cassettes with the selectable hygromycin ( Hyg ) resistance marker ( hph+ ) , and neomycin/G418 resistance marker ( neo+ ) , encoding hygromycin and neomycin phosphotransferase , respectively , were generated by fusion PCR [92] . The 5′ and 3′ flanking regions of the kmt6 coding region were amplified from genomic DNA of PH-1 ( FGSC9075 , FMF1 ) with primers OMF1936 ( 5′-TCTTGGATATTGGCCAGCTC-3′ ) and OMF1930 ( 5′-GATAAGCTTGATATCGAATTCTTACTTGTGGCTfGCGGCTAATTGATGGCT-3′ ) or OMF1931 ( 5′-TGCTATACGAAGTTATGGATCCGAGCTCGTTTGGGCAGAGAAGCTTGAATA-3′ ) and OMF1937 ( 5′-GTGGAGGGAAAACTTGGTGA-3′ ) , respectively . The loxP-neo-loxP cassette was amplified from pLC13-Tom-loxP-neo-loxP with primers OMF1148 ( 5′-ACAAGTAAGAATTCGATATCAAGCTTATC-3′ ) and OMF84 ( 5′-CGAGCTCGGATCCATAACTTCGTATAGCA-3′ ) . The 5′ and 3′ kmt6 flanks were fused to the neo+ cassette by PCR with neo split marker primers OMF601 ( 5′-AGGCGATGCGCTGCGAATCGG-3′ ) and OMF1937 or OMF600 ( 5′-TTGAACAAGATGGATTGCACG-3′ ) and OMF1936 . PCR-amplified fragments were gel-purified using a Qiaquick gel purification kit . For transformations , ∼107 PH-1 conidia were inoculated into 100 ml of YPD and allowed to germinate overnight at 28C with shaking at 200 rpm . Mycelia were harvested on cheesecloth and about 1 g ( wet weight ) was transferred into 20 ml of 1 . 4 M KCl with 500 mg driselase ( Sigma , D8037 ) , 100 mg lysing enzyme ( Sigma , L1412 ) , and 1 mg chitinase ( Sigma , C6137 ) and shaken gently at 90 rpm at 28C for 2 . 5 hrs to induce protoplast formation . The suspension was filtered through Nitex membrane ( 30 µM ) and protoplasts were collected and counted . Transformants were generated by mixing ∼107 protoplasts with 1 µg of neo+ split marker fragments in 500 µl of STC and 30% PEG8000 ( 4∶1 ) and incubating at RT for 20 min . An additional 1 ml of 30% PEG was added and the mixture was incubated for another 5 min , after which 2 ml of STC were added and the mixture was combined with 87 ml of recovery medium ( RM ) and split between six 100 mm Petri dishes for a total 15 ml RM per dish . After 24 hrs at RT , the RM was overlayed with 15 ml RM+200 µg/ml G418 . Resistant colonies were picked and purified from single conidia by generating spores in liquid CMC medium . Strains were screened for gene replacements by PCR and Southern analyses . We generated a strain containing a wildtype kmt6 allele for complementation analyses ( FMF282 ) by random ectopic insertion into the kmt6 deletion strain FMF248 . We digested pFOLT4R4 [93] with ClaI and isolated a 4 kb fragment that contained telomere repeats . This fragment was digested with PvuII for cloning into the SmaI site of pBSII SK+ [94] , generating pLC14 . The SalI hph fragment of pCT74 [95] was inserted pLC14 to generate pLC15 . The kmt6 gene was PCR amplified with OMF1936 and OMF1937 and inserted into pCR4-TOPO ( Invitrogen ) to generate pLC40 . The kmt6 coding region with ∼1 kb 5′ and 3′ flanks was released from pLC40 with SpeI and inserted into the SpeI site of pLC15 to generate pLC41 . This plasmid was transformed into the Δkmt6 strain ( FMF248 ) as described above and Hyg+ transformants were screened for integration by Southern analyses . Approximately 5×106 protoplasts of FMF 225 ( heterokaryotic kmt1+/kmt1 ) and FMF 248 ( kmt6::neo+ ) were mixed and plated at a density of 2 . 5×106 protoplasts per dish on RM with 100 µg/ml Hyg , and 100 µg/ml G418 . After one week , a plug from a selected colony was transferred onto YPD agar with 200 µg/ml Hyg and 200 µg/ml G418 . Plugs from this heterokaryon were transferred to carrot agar for selfings . Selfings and crosses were performed as described previously [96] , with minor modifications . To assay colonization of tomato fruits , ripe organically grown “Roma” tomatoes ( Denison Farms , Corvallis , OR ) were surface-sterilized by gently wiping fruit with 95% ethanol , as described previously [32] . A small region of the epidermis ( ∼10 mm2 ) was peeled back and the wound was infiltrated with 10 µl of spore suspensions containing ∼1 , 000 conidia ( 1×105 conidia/ml ) . Fruits were incubated at 28C above water reservoirs , increasing humidity . Genomic DNA was isolated according to a previously published method [97] , digested with HindIII , and blotted as described elsewhere [98] . Tissue for histone extractions was generated by inoculating ∼107 macroconidia into 100 ml YPD and shaking at 200 rpm at 28C for 2 days . Mycelia were harvested by filtration , frozen in liquid nitrogen , and ground to a fine powder with a mortar and pestle . Histones were acid-extracted as previously described [99] . Approximately 10 to 20 µg of total protein per lane were analyzed by SDS-PAGE . Proteins were transferred to PVDF membrane and blotted using standard procedures [100] . Primary antibodies for westerns were Millipore 07-030 for H3K4Me2 , Active Motif 39159 for H3K4me3 , abcam ab8898 and Active Motif 39161 for H3K9Me3 , and abcam ab9050 for H3K36Me3 . We used four different antibodies to detect H3K27me3 , Active Motif 39535 , abcam ab6002 and ab6147 , and Active Motif 39155 ( which resulted in high background ) . Secondary antibodies were HRP-conjugated goat anti-rabbit ( Pierce 31460 ) or HRP-conjugated goat anti-mouse ( Invitrogen 62-6520 ) . ChIP was carried out on mycelia generated by growing conidia in 100 ml DVK medium for 3 days , transferring 5 ml of the suspension to 100 ml ICI medium supplemented with 6 mM or 60 mM NH4NO3 for nitrogen limiting or sufficient conditions , respectively , and shaken at 200 rpm for 48 to 72 hrs in the dark at 28C . ChIP methods were essentially as described previously [45] , [101] . Strains and antibodies used for ChIP are listed in Table S1 . DNA obtained by ChIP was end-repaired and ligated to adapters as described elsewhere [102]; adapter barcodes are listed in Table S1 ) . Fragments ( 300 to 500 bp long ) were gel-purified and amplified by 21–24 cycles of PCR with Phusion polymerase ( Finnzymes Oy , NEB ) and Illumina PCR primers [102] . Libraries were sequenced on an Illumina GAII and processed with RTA1 . 8 and CASAVA1 . 7 or on a HiSeq2000 genome analyzer and processed with CASAVA1 . 8 . Wild-collected strain 00-676 [28] was re-sequenced to identify SNPs , which were called with MAQ [103] . Total RNA was isolated from aliquots of the same tissue that was used for ChIP by a previously described method [104] , and mRNA was isolated using a Poly ( A ) Purist MAG kit ( Ambion ) . We removed DNA by treatment with RNase-free DNAase ( Qiagen ) , followed by column clean-up according to manufacturer's instructions . We used Illumina TruSeq RNA Sample Preparation kits to make RNA-seq libraries; cDNA was sequenced on an Illumina HiSeq2000 genome analyzer . ChIP-seq reads were sorted by adapter and adapter sequences were removed , then quality scores were converted to Sanger format with the MAQ sol2sanger command [103] if needed ( depending on Illumina pipeline output ) . HTS data from ChIP- and RNA-seq were submitted to the NCBI GEO database ( accession number: GSE50689 ) . Fastq files were used as input for BWA [105] and aligned to a reformatted assembly 3 of the F . graminearum genome ( http://www . broadinstitute . org/annotation/genome/fusarium_group/MultiHome . html ) , i . e . supercontigs were assembled into chromosomes and separated by 20 kb of Ns as placeholders for unassembled reads to match the Broad Institute v3 chromosome assembly . Sam-formatted alignment files from BWA were converted to bam format , sorted , and indexed with samtools [106] for viewing in the gbrowse2 genome browser [107] . Data are accessible at http://ascobase . cgrb . oregonstate . edu/cgi-bin/gb2/gbrowse/fgraminearum_public/ . Adapter-trimmed RNA-seq reads were mapped with Tophat [108] with options -a 5 -m 1 -i 30 -I 2000 and processed in the same way as BWA output with samtools . Cufflinks was used to quantify gene expression values as reads per kilobase of exon per million reads ( RPKM ) , and cuffdiff was used to identify differentially expressed genes between samples [36] , [109] . Figures showing global RNA expression and comparison between samples were generated in R with CummeRbund [36] . Heatmaps were generated in R with pheatmap ( http://cran . r-project . org/web/packages/pheatmap/pheatmap . pdf ) . In total , 1 , 628 genes in fungidb . org were found to be associated with GO term “0044238 , primary metabolic process” . Of these , 1 , 389 had significant data from cuffdiff and were used to generate heatmaps . Secondary metabolite genes were identified from the Broad Institute database . K-means clustering with 12 centers was done in R with the default Hartigan and Wong algorithm [110] to generate “clustered” heatmaps . We found 113 cytochrome P450 genes by searching for PFAM domain “PF00067: p450” at fungidb . org , and 77 of these genes had significant values from cuffdiff and were included in the heatmap . NRPS [111] and PKS [42] genes were previously described , but we use current FGSG numbers here [112] . A near complete secretome gene set has been described [43] . The list of transcription factors from a comparative study of three Fusarium species was used [27] . Transcription factors required for perithecial development were previously identified [113] . | Changes in chromatin structure are required for time- and tissue-specific gene regulation . How exactly these changes are mediated is under intense scrutiny . The interplay between activating histone modifications , e . g . H3K4me , and the silencing H3K27me3 mark has been recognized as critical to orchestrate differentiation and development in plants and animals . Here we show that filamentous fungi , exemplified by the cereal pathogen Fusarium graminearum , can use H3K27 methylation to generate silenced , facultative heterochromatin , covering more than a third of the genome , much more than the 5–8% of Neurospora or metazoan genomes . Removal of the silencing mark by mutation of the methyltransferase subunit of the PRC2 silencing complex resulted in activation of more than 1 , 500 genes , 14% of the genome . We show that generation of facultative heterochromatin by H3K27 methylation is an ancestral process that has been lost in certain lineages ( e . g . at least some hemiascomycetes , the genus Aspergillus and some basidiomycetes ) . Our studies will open the door to future precise “epigenetic engineering” of gene clusters that generate bioactive compounds , e . g . putative mycotoxins , antibiotics and industrial feedstocks . Availability of tractable fungal model systems for studies of the control and function of H3K27 methylation may accelerate mechanistic research . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"And",
"Methods"
] | [] | 2013 | The Fusarium graminearum Histone H3 K27 Methyltransferase KMT6 Regulates Development and Expression of Secondary Metabolite Gene Clusters |
The presence of animal reservoirs in Schistosoma japonicum infection has been a major obstacle in the control of schistosomiasis . Previous studies have proven that the inclusion of control measures on animal reservoir hosts for schistosomiasis contributed to the decrease of human cases . Animal surveillance should therefore be included to strengthen and improve the capabilities of current serological tests . Thioredoxin peroxidase-1 ( SjTPx-1 ) and four tandem repeat proteins ( Sj1TR , Sj2TR , Sj4TR , Sj7TR ) were initially evaluated against human sera . The previous test showed high sensitivity and specificity for antibody detection against SjTPx-1 and Sj7TR . In this study , the immunodiagnostic potential of these recombinant proteins was evaluated using enzyme-linked immunoassay on 50 water buffalo serum samples collected in Cagayan , the Philippines as compared with the soluble egg antigen ( SEA ) . For specificity , 3 goat serum samples positive with Fasciola hepatica were used and among the antigens used , only SEA showed cross-reaction . Stool PCR targeting the S . japonicum 82 bp mitochondrial NAD 1 gene was done to confirm the true positives and served as the standard test . Twenty three samples were positive for stool PCR . SjTPx-1 and Sj1TR gave the highest sensitivity among the recombinant proteins tested for water buffalo samples with 82 . 61% and 78 . 26% respectively which were higher than that of SEA ( 69 . 57% ) . These results prove that SjTPx-1 works both for humans and water buffaloes making it a good candidate antigen for zoonotic diagnosis . Sj1TR showed good results for water buffaloes and therefore can also be used as a possible candidate for detecting animal schistosome infection .
Intensified disease surveillance has become an essential public health instrument in providing necessary information for monitoring the disease and evaluating control measures . Schistosomiasis is considered as a neglected disease caused by Schistosoma japonicum in China and Southeast Asia , S . haematobium in the Middle East and Africa and S . mansoni in Africa . Among them , only S . japonicum is known to infect both humans and more than 40 other mammals [1] which complicate the control of the disease . Inclusion of zoonotic surveillance in national control programs in endemic countries might be a necessary tool for the control and elimination of schistosomiasis japonica . Researches have shown how intervention involving animal reservoirs can reduce S . japonicum infection in humans [2] , [3] . Simultaneous treatment of water buffaloes and human has proven to be effective as seen in a five-year praziquantel-based intervention study done around the Poyang Lake in Jiangxi Province , China [2] . However , animal surveillance for schistosomiasis has not yet been fully developed . In China , a nationwide schistosomiasis survey in 1995 established the high prevalence of S . japonicum in water buffalo ( 9 . 6% ) and cattle ( 7 . 2% ) [4] , showing how important these animals are as reservoir hosts . In Indonesia , domestic animals such as water buffaloes and wild animals were found to be infected with schistosomes ( 10% ) [5] . In the Philippines , a variety of animal reservoir hosts such as rats , cats , dogs , pigs , cattle and water buffaloes were found to be potential hosts for schistosomiasis using different parasitological and immunological assays [6]–[8] . Among these hosts , water buffaloes had the lowest prevalence of infection [8] and showed no significant role in the S . japonicum transmission to humans according to the mathematical modeling done on these prevalence data [9] . A recent study however in one endemic area in Leyte showed prevalence in water buffaloes as high as 51 . 5% using the highly validated real-time polymerase chain reaction [10] . This may prove that water buffaloes have a major contribution to the transmission of schistosomiasis in the Philippines . Animal schistosome infection has been usually diagnosed through direct parasitological techniques including Kato-Katz technique and miracidial hatching . The quantitative Kato-Katz fecal smear is simple , practical and useful in quantifying eggs [11] , [12] and is considered by the World Health Organization as the gold standard method for diagnosing schistosomiasis [13] . However , this method is labor-intensive , requires skilled personnel , has low sensitivity in low prevalence endemic areas [14] , [15] and seven repeated Kato-Katz examinations coupled with miracidial hatching are needed to reach its maximal sensitivity [16] . On the other hand , molecular detections such as polymerase chain reaction ( PCR ) are highly sensitive and specific , but they are costly and require expensive equipment . Furthermore , current serological tests utilizing crude antigens like soluble egg antigen-enzyme-linked immunosorbent assay ( SEA-ELISA ) and circum-oval precipitin test ( COPT ) cause cross-reactions leading to misdiagnosis . Hence there is a need for the development of an easier , more sensitive and specific test for schistosomiasis . In a previous study , thioredoxin peroxidase-1 ( SjTPx-1 , GeneDB accession no . Sjp_0095720 . 1 ) and four tandem repeat proteins ( TRP ) namely Sj1TR , Sj2TR , Sj4TR and Sj7TR ( GeneDB accession nos . Sjp_0099630 , Sjp_0086200 , Sjp_0059850 , Sjp_0110390 respectively ) were evaluated against human sera [17] . SjTPx-1 and Sj7TR both showed high sensitivity and specificity making them promising diagnostic antigens for human schistosomiasis . Using ELISA , these recombinant proteins were tested on water buffaloes and the results were compared with stool PCR assay and the conventional SEA-ELISA and COPT . This study therefore examined the immunodiagnostic potential of the recombinant antigens in water buffaloes which might lead to the development of a more reliable and accurate diagnostic test for animal schistosomiasis . Strengthening the diagnostic test is crucial in both the human and animal schistosome infection surveillance in areas where elimination is in sight and might be vital in the prevention of emergence and re-emergence of schistosomiasis japonica leading to the possible control of this neglected parasitic disease .
Serum and stool samples were taken from 50 water buffaloes in Gonzaga , Cagayan , the Philippines . Stool samples collected by intrarectal means from water buffaloes were placed in code-labeled cups and stored with 10% neutralized formalin until processing . None of the stools were found positive for S . japonicum eggs using the formalin-ether concentration technique ( FECT ) . Non-endemic negative control sera were taken from 18 water buffaloes in Nueva Ecija and Batangas in the Philippines . All the owners of the water buffaloes were informed about the study and gave consent to use their water buffaloes in this study . Sera positive for Fasciola hepatica were collected from experimentally infected goats ( N = 3 ) . They were diagnosed through the detection of the parasite in the stool . This study was done according to ethical guidelines for the use of animal samples permitted by Animal Care and Use Committee , Dokkyo Medical University ( Permit No . 0029 ) in accordance with the Guidelines for the Care and Use of Laboratory Animals , Dokkyo Medical University , The Law Concerning Kind Treatment and Management of Animals ( Law No . 221 ) and Japanese Government Notification on Feeding and Safe-keeping of Laboratory Animals ( No . 6 ) , as well as by Obihiro University of Agriculture and Veterinary Medicine ( Permit No . 23–153 ) . FECT was done prior to DNA extraction to maximize the quantity of schistosome eggs in the collected stool if positive and to remove fecal debris . Although formaldehyde is known to degrade DNA , DNA extraction was not deterred since neutral-buffered formaldehyde was used [18] , [19] and the PCR target is less than 400 bp [20] . DNA extraction from stool samples was done using QIAamp DNA Stool Mini Kit ( QIAGEN Inc . , Valencia , CA ) according to the manufacturer's protocol and stored at −20°C until use . DNA was also extracted from cattle stool in non-endemic area ( Obihiro , Hokkaido , Japan ) to serve as the negative control . PCR was done on the stool samples collected from 50 water buffaloes targeting the 82 bp mitochondrial NADH dehydrogenase I gene ( SjND1 ) [21] . The primer set SjND1 forward 5′-TGR TTT AGA TGA TTT GGG TGT GC3′ and reverse 5′ AAC CCC CAC AGT CAC AGT CAC TAG CAT AA3′ was used according to a previous research [22] . Twenty microliters of reaction mixture contained 2 µl of buffer , 0 . 6 µl of 1 . 5 mM MgCl2 , 1 . 6 µl of 2 . 5 mM dNTP , 0 . 4 µl of each 20 pmol/µl primer , 0 . 2 µl of 5 U/µl Taq DNA polymerase ( Takara , Otsu , Japan ) and 1 µl of template . The conditions for PCR were as follows: 95°C for 10 mins , followed by 40 cycles of 95°C for 15 secs , 60°C for 1 min , 72°C for 1 min , and a final extension of 72°C for 10 min . The PCR was performed using Veriti 96 Well Thermal Cycler ( Applied Biosystems , Carlsbad , CA ) . The PCR products were separated by electrophoresis in 2 . 5% agarose gel and visualized by ethidium bromide staining . PCR reactions were done in triplicates for every stool sample and a sample is regarded as positive when at least one reaction was positive . Recombinant molecules of SjTPx-1 and the four TRPs from S . japonicum used in this study were prepared as previously described [17] . In brief , SjTPx-1 was cloned using PCR from S . japonicum Yamanashi strain adult worm cDNA while the nucleotides coding a partial tandem repeat domain of the 4 TRPs were synthesized by GenScript USA Inc . ( Piscataway , NJ ) . The genes were then digested with their respective restriction enzymes , inserted into the pET28 vector ( EMD Biosciences , San Diego , CA ) and transfected into Escherichia coli BL21 grown in SOB medium ( Sigma-Aldrich , St . Louis , MO ) . The recombinant proteins were recovered using the Ni-NTA agarose ( Qiagen Inc . , Valencia , CA ) , dialyzed and eluted with 20 mM Tris , pH 8 . 0 . The integrity and purity of the proteins were evaluated by 15% polyacrylamide gel electrophoresis ( SDS-PAGE ) under denaturing conditions and subsequent Coomassie Brilliant Blue staining . The concentration of each expressed protein was measured using the BCA Protein Assay ( Thermo Scientific , Rockford , IL ) . The validity of the ELISA assays using the recombinant proteins was estimated by the sensitivity , specificity and predictive values using the stool PCR as the reference standard . Kappa value was used to estimate the agreement between the antigens [25] . To test for the statistical significance of the difference between the mean OD values of the PCR positive and PCR negative samples on the ELISA using the crude and recombinant antigens , two-tailed p-value was calculated using unpaired t test with 95% confidence interval .
Stool PCR was performed to serve as the standard test by determining the positives for S . japonicum infection . Stool DNA from a non-endemic cattle served as the negative control and S . japonicum DNA template served as the positive control . Of the 50 water buffalo samples , 23 were positive . As seen on Figure 1 , a band having approximately 82 bp was found in the positive samples while none on the negative samples . The band was also seen in the positive control but not in the negative control . COPT was done initially on the 50 water buffalo samples for the purpose of comparing it with the ELISA using the recombinant proteins . Seventeen samples turned out to be positive as shown by bleb or segment formation after 48 h incubation . All of the samples positive for COPT were also PCR positive . The ELISA was performed using sera from 50 water buffaloes from an endemic area in the Philippines . Cut-off values were calculated using 18 water buffalo serum samples from non-endemic areas in the Philippines . Twenty samples were positive for both SjTPx-1 and Sj1TR , 18 for SEA and 14 samples for Sj2TR , Sj4TR and Sj7TR . As shown on Table 1 , 16 out of the 18 SEA positive , 19 out of the 20 SjTPx-1 positive and 18 out of the 20 Sj1TR positive samples were also PCR positive . There were 2 PCR negative samples detected only by SEA and Sj1TR , and of which , 1 was also detected by SjTPx-1 . Furthermore , there were 4 samples detected only by PCR and negative for all the recombinant proteins and SEA . The mean OD values for PCR negative were lower than that of the PCR positive samples ( Figure 2 ) for the crude and the recombinant antigens . The p-values obtained to show the significance of the difference between the mean OD values of PCR positive and PCR negative samples were all less than 0 . 05 and were considered statistically significant ( data not shown ) . To test for specificity , 3 serum samples from goats experimentally infected with Fasciola hepatica were also used . Only SEA showed cross-reaction with 2 samples having high OD values ( data not shown ) . Based on the statistical analysis , SjTPx-1 and Sj1TR showed high agreement with the stool PCR done on the samples based on the kappa values as seen in Table 2 . The specificity and the positive predictive values of these 2 recombinant proteins were higher than those of SEA .
The lack of importance given to the role of animal hosts in the transmission of S . japonicum has turned into a loophole in the control efforts for schistosomiasis . Efficient and highly sensitive diagnostic tools for animal surveillance should be employed as a strong support in ensuring control of the parasitic infection among the reservoir hosts . This study aims to determine the possible use of the recombinant antigens in the diagnosis of schistosomiasis among the water buffaloes . Results of the study are expected to contribute to clearer insights in the role of this animal in the transmission of the disease . SjTPx-1 , which has a sensitivity of 85 . 71% for humans [17] and 84 . 0% for cattle [26] in previous studies , showed a comparable 82 . 61% sensitivity among the water buffaloes . However , it should be noted that the infection standard between these studies are different , with stool PCR confirmed samples used in this study and microscopy confirmed samples in the previous studies . But despite this difference , SjTPx-1 showed good immunodiagnostic potential in all these studies and therefore might be an effective diagnostic antigen candidate for both humans and animals . Furthermore , Sj1TR performed better in water buffaloes ( 78 . 26% ) than in humans ( 68 . 57% ) while Sj7TR did not show good antigenicity in water buffaloes ( 60 . 87% ) as it did in humans ( 85 . 71% ) . These differences in antigenicity can be explained by the differences in immune responses among various host species . On the other hand , results showed that SEA has lower sensitivity than SjTPx-1 and Sj1TR , and causes cross-reaction with F . hepatica positive samples . Both the conventional SEA-ELISA and COPT therefore are not adequate enough to properly diagnose cases of schistosomiasis . Given this and the difficulty in scaling up production of SEA for mass screening , the use of recombinant proteins has proven to be a good alternative for schistosomiasis diagnosis . In this study , we used stool PCR as the standard test instead of stool microscopy . Coprological methods such as Kato-Katz technique have been the commonly accepted gold standard in schistosomiasis diagnosis . However , it will be difficult to detect schistosome eggs in the stool of large animals due to the size of their excreta which might affect the sensitivity of the test . The adequacy of the stool PCR in diagnosing true positives has been already validated by a previous study done among water buffaloes in the Philippines [10] . Their results showed a marked difference in the number of positive water buffaloes with 51 . 5% prevalence for stool PCR as against to the 3 . 7% prevalence using the coprological tests including DBL and Kato-Katz technique . Stool PCR has been tested also in other helminthic [27] and protozoan [28] infection and was found to possess higher sensitivity and specificity as compared to stool microscopy . Furthermore , samples which were PCR negative and positive for the recombinant protein-based ELISA should be investigated further . It was shown in the initial assessment of the stool PCR that its sensitivity can be affected by the degree of infection [22] . For example , in an infection higher than 10 schistosome eggs per gram ( epg ) of stool , the sensitivity can go as high as 95 to 100% . The sensitivity goes down to 78 to 85% when the infection is less than 10 epg . It is therefore very important to adjust the diagnostic capabilities of the recombinant proteins to detect cases even in very low infections which are undetectable even with molecular techniques such as PCR . Furthermore , the extent of time that the antibodies against these recombinant proteins will be present in the blood circulation should also be analyzed . It was widely known that one of the limitations of antibody-based serological tests is that it cannot distinguish past and present infection . In addition , the infection in water buffaloes is self-limiting [29] which further complicates the possible diagnosis of active infection . In the previous paper using these recombinant proteins [17] however , serum samples from human individuals collected one year after treatment with praziquantel tested negative for the recombinant antigens . This somehow suggests that the recombinant antigens might be used to detect current infection in humans . However , it was not yet studied in animals and it will be very useful if these recombinant antigens can also be used to diagnose present animal infection as well . As this study proved the serological applicability of SjTPx-1 and Sj1TR in water buffaloes , this might be also used in the development of rapid immunochromatographic tests that can detect animal schistosome infection in the field . Although the possible reservoir animal hosts in endemic areas can also undergo mass drug administration as previously done in China , serological tests utilizing these recombinant proteins will be useful in epidemiological studies and surveillance of animal infection in areas that have reached elimination level . It was reported that inappropriate surveillance system was one of the factors attributed to the re-emergence of schistosomiasis in one province in China [30] . Infection rate among the cattle in that province was reported to have reached as high as 22 . 3% . Mammalian reservoir hosts might serve as the sentinel population in schistosomiasis transmission as they have the potential to be the key source of S . japonicum infection in re-emerging regions [31] . The World Health Organization noted that case detection will be a problem when elimination of the disease is at hand [32] . Environmental monitoring was said to be important in knowing the scale of the transmission mechanism in such low transmission environment [33] . Strengthening therefore the diagnostic capabilities of serological tests might be one of the vital keys in the possible prevention of such re-emergence of the disease . On the other hand , the emergence of schistosomiasis in new endemic foci is also a threat to the possible elimination of the disease . The site used in this study , Cagayan Valley , was not known to be endemic of schistosomiasis until 2002 [34] . Based on the results of this study , schistosome infection among water buffaloes has a positivity rate ranging from 24% to 46% using ELISA and stool PCR respectively . Animal infection might play a big role in the transmission of the disease in that area . A more specific and sensitive animal surveillance is therefore also needed to prevent spreading of the disease in other areas . Furthermore , the recombinant antigens used in this study should also be tested against other animals like dogs , pigs and rats as previous studies showed that they are also important reservoir hosts for S . japonicum [9] , [35] . This will also provide a more realistic epidemiological picture of the disease which is very important in the control program . In the future studies , the use of these recombinant antigens should be also validated in areas with different levels of endemicity both for humans and animals . This stage is relevant as the challenge now is to optimize the diagnostic use of these recombinant antigens to the different stages of active control . Appropriate diagnostic tools were strongly needed to evaluate effectiveness of community interventions , verify local disease elimination and detect resurgence of the disease at the earliest time possible [36] . | Schistosomiasis remains to be a public health problem in 76 endemic countries in spite of control efforts that have been done . Among the major causative agents of schistosomiasis , only Schistosoma japonicum is known to be zoonotic . However , the role of animal reservoir hosts has not been given much importance which might be the main hindrance in the possible elimination of the disease . In addition , animal surveillance is not part of the current schistosomiasis control program in most of the endemic countries . This study reports the use of recombinant proteins in ELISA for detecting the infection in water buffaloes . These antigens were previously used against humans and showed that SjTPx-1 and Sj7TR can be a good diagnostic antigen . Using the stool PCR as the standard test , SjTPx-1 and Sj1TR were shown to work on the water buffaloes better than the conventional SEA . These antigens can both be useful in the development of intensified animal surveillance for schistosomiasis . | [
"Abstract",
"Introduction",
"Materials",
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"Discussion"
] | [
"medicine",
"veterinary",
"science"
] | 2012 | Utilization of ELISA Using Thioredoxin Peroxidase-1 and Tandem Repeat Proteins for Diagnosis of Schistosoma japonicum Infection among Water Buffaloes |
Appropriate health and nutrition interventions to prevent long-term adverse effects in children are necessary before two years of age . One such intervention may include population-based deworming , recommended as of 12 months of age by the World Health Organization in soil-transmitted helminth ( STH ) -endemic areas; however , the benefit of deworming has been understudied in early preschool-age children . A randomized , double-blind , placebo-controlled trial was conducted to determine the effect of deworming ( 500 mg single-dose crushed mebendazole tablet ) on growth in one-year-old children in Iquitos , Peru . Children were enrolled during their routine 12-month growth and development clinic visit and followed up at their 18 and 24-month visits . Children were randomly allocated to: Group 1: deworming at 12 months and placebo at 18 months; Group 2: placebo at 12 months and deworming at 18 months; Group 3: deworming at both 12 and 18 months; or Group 4: placebo at both 12 and 18 months ( i . e . control group ) . The primary outcome was weight gain at the 24-month visit . An intention-to-treat approach was used . A total of 1760 children were enrolled between September 2011 and June 2012 . Follow-up of 1563 children ( 88 . 8% ) was completed by July 2013 . STH infection was of low prevalence and predominantly light intensity in the study population . All groups gained between 1 . 93 and 2 . 05 kg on average over 12 months; the average difference in weight gain ( kg ) compared to placebo was: 0 . 05 ( 95% CI: -0 . 05 , 0 . 17 ) in Group 1; -0 . 07 ( 95%CI: -0 . 17 , 0 . 04 ) in Group 2; and 0 . 04 ( 95%CI: -0 . 06 , 0 . 14 ) in Group 3 . There was no statistically significant difference in weight gain in any of the deworming intervention groups compared to the control group . Overall , with one year of follow-up , no effect of deworming on growth could be detected in this population of preschool-age children . Low baseline STH prevalence and intensity and/or access to deworming drugs outside of the trial may have diluted the potential effect of the intervention . Additional research is required to overcome these challenges and to contribute to strengthening the evidence base on deworming . ClinicalTrials . gov ( NCT01314937 )
The soil-transmitted helminth ( STH ) disease cluster includes ascariasis , trichuriasis and hookworm disease . It is considered to be one of the most common Neglected Tropical Diseases ( NTD ) , affecting an estimated 1 . 45 billion people worldwide [1] . STHs are transmitted in contaminated food , water and the environment in areas of poverty in low- and middle-income countries . These intestinal parasites have a direct and indirect adverse impact on nutritional status by disrupting normal nutrient intake , excretion and utilization in their hosts and by causing blood loss and loss of appetite [2 , 3] . WHO recommends large-scale preventive chemotherapy programs , using anthelminthic treatment ( i . e . deworming ) , for the high-risk groups of women of reproductive age , especially pregnant women , school-age children ( i . e . 5 to 14 years of age ) , and preschool-age children ( i . e . 1 to 4 years of age ) in STH-endemic areas [4 , 5] . Adverse effects from deworming are infrequent , and when reported , are mild and transitory , including gastrointestinal upset and diarrhea [6] . Deworming interventions are often school-based in order to reach school-age children . In preschool-age children , deworming is often piggybacked onto vaccination or supplementation programs , child health days , or programs for the elimination of lymphatic filariasis [7] . However , preschool-age children lag behind their school-age counterparts as scaling-up of school-based programs continues while that of preschool programs remains a challenge [7] . The global proportion of at-risk preschool-age children receiving deworming in 2012 was estimated to be on the order of 25% [7] . This coverage has decreased since previous reports [8] . Prior to 2002 , children under two years of age had been excluded from deworming interventions as the burden of STH infection was perceived to be low in this age group and the safety profile of available anthelminthics was not well established . In 2002 , WHO convened an informal consultation of experts , and subsequently recommended the inclusion of children between 12 and 24 months of age in deworming activities using single-dose albendazole ( in a reduced dose of 200 mg ) or mebendazole ( in the usual dose of 500 mg ) [9] . These recommendations were based on animal studies , toxicity data and other safety data [10] . Despite the WHO recommendations and increasing evidence of the occurrence of STH infection in early preschool-age children [10–15] , many countries still exclude children under 24 months of age from their national deworming programs . Providing evidence on the potential benefits of deworming in the younger age group between one and two years of age is essential . A study reviewing data from 54 countries confirmed that preventive interventions must occur during the first two years of life to prevent growth deficits , such as stunting and underweight [16] . Interventions at this time are essential to prevent both short- and longer-term adverse health effects [17] . The evidence-base on including deworming as one of the essential early childhood interventions in this critical window is , however , limited . Randomized controlled trials conducted exclusively in school-age children or in both preschool-age and school-age children have provided mixed evidence on deworming benefits on growth and development [6 , 18 , 19] . Few studies have focused exclusively on the preschool-age population [12 , 20–22] . There is some evidence that adverse consequences of even low prevalence and intensity STH infection may be more pronounced in children during this critical time period [11] . Considering the unique nutritional demands and growth patterns of younger children , aggregated results from older children do not provide a clear indication of the potential benefit of deworming on growth and nutrition in younger age groups . To fill this research gap , we therefore conducted a randomized controlled trial on the effect , and optimal timing and frequency , of a deworming intervention incorporated into routine child health services at one year of age . Our objective was to determine whether deworming would improve growth by two years of age .
This study received ethics approval in Peru from the Comité Institucional de Ética of the Universidad Peruana Cayetano Heredia and the Instituto Nacional de Salud , in Lima , and the local Ministry of Health office ( Dirección Regional de Salud ( DIRESA ) Loreto ) in Iquitos ( S1 Text ) . Ethics approval was obtained in Canada from the Research Ethics Board of the Research Institute of the McGill University Health Centre in Montréal , Québec ( S1 Text ) . An independent Data Safety and Monitoring Committee ( DSMC ) was established with three members , from Canada , the U . S . , and Peru , to review all adverse events and approve continuation of the trial at three time points . At baseline , eligibility was assessed , and an informed consent form was signed by both parents or guardians of the child . In the case of a single parent ( e . g . due to death , separation or divorce ) , only one signature was required . The trial was registered with ClinicalTrials . gov ( NCT01314937 ) . The CONSORT checklist is described in S1 Checklist and the trial protocol is described in S2 Text . We conducted a randomized , double-blind , parallel , placebo-controlled trial of a deworming intervention incorporated into routine growth and development ( ‘Crecimiento y Desarrollo” or CRED ) visits in Iquitos , an STH-endemic area of the Peruvian Amazon . Details on baseline enrolment methodology and the study population have been described elsewhere [14] . Briefly , children were enrolled into the trial in their homes or participating health centres . Inclusion criteria were: 1 ) children attending any one of the 12 participating health centres for their 12-month CRED visit; and 2 ) children living in Belén , Iquitos , Punchana or San Juan districts . Exclusion criteria were: 1 ) children attending the health centre for suspected STH infection; 2 ) children who had received deworming treatment in the six months prior to the trial; 3 ) children whose families planned to move outside of the study area within the next 12 months; 4 ) children under 12 months of age or 14 months of age or older; and 5 ) children with any serious congenital or chronic medical condition . Any child who was excluded for medical reasons , and who was not already receiving regular health care , was referred to the health centre for follow-up by appropriate health personnel . A baseline socio-demographic and epidemiological questionnaire ( including family and child health and nutrition information ) was administered in the home or health centre to the primary caregiver of the child . Baseline outcome measurements , including weight , length and the provision of a stool specimen , were ascertained in a subsequent visit in the health centre . All procedures were performed by dedicated , trained research assistants . Following confirmation of eligibility , informed consent and all baseline outcome assessments in the health centres , children were randomized into one of four intervention groups: Deworming consisted of a single-dose mebendazole tablet ( 500 mg ) ( manufactured by Janssen Pharmaceuticals Inc . ; donated by INMED Peru ) . The placebo was identical to the deworming tablet in terms of size , colour and markings ( manufactured and purchased from Laboratorios Hersil , Peru ) . Tablets were crushed and mixed with juice for ease of administration and safety [23] . The crushed tablet was administered by research assistants at the end of each visit after all outcome assessments had been completed . All children received deworming at the 24-month visit according to Peruvian Ministry of Health guidelines [24] . Children received usual care interventions and services from health centre personnel [24] . This included the administration of measles , mumps and rubella ( MMR ) vaccination at the 12-month visit , and diphtheria , pertussis and tetanus ( DPT ) vaccine booster at the 18-month visit . Sample size calculations were based on detecting the smallest meaningful difference among intervention groups in mean weight gain over 12 months , and took into account potential effect dilution from treating infected and non-infected children . From previous research in the study area , STH prevalence was expected to be 25% at 12 months of age [13] . To estimate expected growth , longitudinal growth data was collected from health centre registries in the study area in 2011 . Mean weight gain ± standard deviation between 12 and 24 months in 100 untreated children was calculated to be 2 . 0 kg ± 0 . 8 kg . The sample size was calculated a priori such that comparisons could be made between all four groups to look at the overall effect of deworming , as well as the effect of timing and frequency of deworming . In order to have 80% power to detect a minimum difference of 0 . 20 kg in mean weight gain among intervention groups , assuming a common standard deviation of 0 . 8 and a significance level of 0 . 05 , and using a one-way ANOVA which accounts for pair-wise multiple comparisons between all groups ( i . e . 6 comparisons ) using the Tukey correction , the estimated sample size per group was 366 children . The required sample size was increased to 440 children per group ( 1760 in total ) , to take into account potential loss-to-follow-up of 20% after 12 months ( based on attrition rates from previous studies in the area by the research team [25 , 26] ) ( MC4G Software© , GP Brooks , Ohio University , 2008 ) . Computer-generated randomly ordered blocks of eight and twelve were used to randomly assign children to each intervention group in a 1:1:1:1 allocation ratio . Blocking ensured that the number of children assigned to each group would be balanced and reduced the potential for bias and confounding [27] . The random allocation sequence was generated by a biostatistician who was not otherwise involved in the trial . Research personnel not directly involved in the trial prepared small envelopes containing the randomly assigned intervention for each visit . These were numbered from 1 to 1760 , with each number corresponding to one of the four intervention groups . Envelopes were stored in a temperature-regulated pharmacy at the research facility , and distributed by the Project Director ( SAJ ) or the local Study Coordinator ( LP ) in sequential order to research assistants until the sample size was achieved . Appropriate allocation concealment and randomly ordered block sizes ensured that the randomization sequence would not be predictable [27] . All health centre and research personnel , and parents of participants were blinded to intervention status . Children were followed-up at their 18 and 24-month visit in the health centre , at which time all outcome ascertainments were repeated . At the 18-month visit the second randomly assigned intervention was administered . Each visit was scheduled six months after the previous visit . In the case that a participant did not attend their 18-month visit , children remained eligible for the 24-month visit , which was scheduled 12 months after initial enrolment . If participants were not located prior to the day of their anticipated follow-up visit , or a scheduled date was missed , a minimum of four additional attempts were made to locate them . The original end dates of the 18-month follow-up and 24-month follow-up ( i . e . trial completion ) were each extended by one month ( i . e . seven months and 13 months after the end of enrolment , respectively ) to maximize follow-up rates . A monetary reimbursement was provided to cover travel costs for each visit . The pre-specified primary outcome measure was weight gain between the 12 and 24-month visit . Pre-specified secondary outcome measures were weight-for-age z-score , length gain , length-for-age z-score , change in STH infection prevalence and intensity , and change in development ( i . e . cognitive , language and fine motor skills ) between the 12 and 24-month visit . The development outcomes are reported separately . Prior to commencing recruitment , in-depth practical training of the research assistants took place according to WHO guidelines [28 , 29] to ensure accurate outcome assessment and standardization . Inter and intra-rater reliability of over 95% was achieved for weight and length assessments , which are considered acceptable levels for anthropometric measurements [28 , 30] . Methods used for outcome measurements are described elsewhere [14] . Briefly , weight was measured using a portable electronic scale , accurate to the nearest 0 . 01 kg ( Seca 334 , Seca Corp . , Baltimore , MD , USA ) . Length ( i . e . the recommended measurement of height in children less than two years of age ) was measured in duplicate as recumbent crown-heel length on a flat surface using a stadiometer ( Seca 210 , Seca Corp . , Baltimore , MD , USA ) , accurate to the nearest millimetre . One stool specimen per child was collected to assess STH ( e . g . Ascaris , Trichuris and hookworm ) infection prevalence and intensity . For ethical reasons , only specimens from children receiving deworming treatment were immediately examined by trained laboratory technologists at the local research facility using the Kato-Katz method ( single slide ) for the presence and intensity ( i . e . eggs per gram of feces ) of STH infection [31] . At each time point , specimens from those children receiving placebo were stored at room temperature in 10% formalin and analyzed by the direct method for the presence of STH infection upon trial completion ( Table 1 ) . This approach ensured that children found to be infected were treated . This approach also aimed to minimize effect dilution which would have occurred if treatment had been provided to those found to be STH positive , but randomized to receive placebo . The Kato-Katz method is the recommended technique for assessment of the prevalence and intensity of intestinal parasitic infection in fresh stool [31] . For a one-stool specimen , sensitivity and specificity are over 96% for Ascaris and over 91% for Trichuris [32] . There is lower sensitivity and specificity for hookworm; however , hookworm infection is generally uncommon in very young children in this study area [13] . Additional details on the collection of stool specimens , including the ethical rationale for using two methods of analysis and how blinding was maintained , are published elsewhere [14] . Lower sensitivity to detect STH infection from storage and later analysis of specimens by the direct method was also anticipated [14] . A socio-demographic and epidemiological questionnaire was administered at each visit . At the follow-up visits , this included a question on whether deworming had been received between study visits ( i . e . outside of the trial ) . Information on minor and severe adverse events was obtained through passive reporting at follow-up visits or in between visits . Severe adverse events were based on WHO definitions and included: 1 ) death; 2 ) life-threatening conditions; 3 ) in-patient hospitalization or prolongation of an existing hospitalization; 4 ) persistent or significant disability/incapacity; 5 ) cancer; or 6 ) overdose ( accidental or intentional ) [5] . All reported illnesses that did not meet the definition of a serious adverse event were considered to be minor adverse events . All adverse events were reported to ethics committees . Summary reports of adverse events were also provided to the DSMC . Data collection activities during fieldwork were regularly supervised by the Project Director ( SAJ ) and local Project Coordinator ( LP ) . The consistency of egg count assessments was evaluated among the laboratory technologists using standard quality control methods [31] . The laboratory supervisor read 10% of the slides of the laboratory technologists without prior knowledge of the result to ensure quality control . Weight-for-age z scores ( WAZ ) and length-for-age z scores ( LAZ ) were calculated using WHO Anthro software ( Version 3 , 2011 ) . WHO categories were used to classify STH intensity according to species-specific counts of eggs per gram of feces ( epg ) [33] . Both arithmetic and geometric mean epg were calculated . The primary outcome of the trial was mean weight gain in kilograms ( kg ) between the baseline 12-month visit and the 24-month follow-up visit ( i . e . after 12 months ) . Mean weight gain ( kg ) was compared between the four intervention groups using unadjusted one-way ANOVA procedure . Secondary analyses which were specified a priori were conducted to examine differences between intervention groups in terms of change in derived weight indices ( i . e . mean WAZ change ) and length and derived length indices ( mean length gain and mean LAZ change ) . Multivariable linear regression was also conducted adjusting for age , sex , socioeconomic status ( based on an asset-based proxy index ) [34 , 35] and continued breastfeeding at 12 months of age . All analyses were first expressed using an intention-to-treat ( ITT ) approach such that participants were analyzed according to their assigned intervention group . Multiple imputation , using a Markov Chain Monte Carlo ( MCMC ) model with five imputations , was used for those who did not attend the 24-month follow-up visit . Variables related to the outcome , and hypothesized to be related to missing the follow-up visit ( s ) were used to impute missing weight and length measurements . These variables were baseline weight , length , socioeconomic status , continued breastfeeding at 12 months , sex , and age . Imputation was done separately by randomly assigned treatment group . Additional analyses were specified a posteriori , including: 1 ) using a complete case approach on all participants who had attended the final follow-up visit , 2 ) using a per-protocol approach excluding those participants who did not attend all three visits and/or who reported having received deworming outside of the trial between baseline and the final follow-up visit and 3 ) restricted to children positive for STH infection at baseline . These analyses were conducted for the following reasons: 1 ) complete case analyses were conducted for comparison purposes with intention-to-treat analyses with imputed data; 2 ) per-protocol analyses were conducted to account for higher than anticipated non-compliance to the assigned intervention; and 3 ) subgroup analysis in STH-infected children were conducted to account for the lower than anticipated baseline STH infection prevalence . The primary research question on the effect of deworming was determined by comparing growth outcomes between each intervention group and the control group . To explore the secondary research question on the effect of the timing of deworming ( i . e . at the 12-month visit or at the 18-month visit ) , growth outcomes in Group 1 were compared to Group 2 . To explore the secondary research question on the effect of the frequency of deworming ( i . e . provided once or twice ) , growth outcomes in Group 1 and Group 2 were each compared to Group 3 . All three research questions were specified a priori . The effect of deworming on STH indicators at 24 months was also examined using a generalized linear model with a log link , a Poisson distribution , and a robust variance estimator to estimate the risk ratio for the dichotomous outcomes of any STH infection , Ascaris infection , Trichuris infection and hookworm infection , where no infection ( i . e . no STH infection , no Ascaris infection , no Trichuris infection and no hookworm infection , respectively ) comprised the reference group . All statistical analyses were performed using the Statistical Analysis Systems statistical software package version 9 . 3 ( SAS Institute , Cary , NC , USA ) .
Between September 2011 and June 2012 , the parents of 2297 children were approached to participate in the trial . Five-hundred and thirty-seven children were excluded as they did not meet the inclusion criteria ( n = 385 ) , declined to participate ( n = 126 ) , or were approached but not enrolled once the sample size was reached ( n = 26 ) . A total of 1760 children were randomized to the four groups ( Fig 1 ) . All children received the assigned intervention at baseline . A total of 1606 children ( 91 . 2% ) attended their first follow-up at the 18-month visit between March 2012 and January 2013 . Due to parental refusal , three children did not receive their randomly allocated intervention . The average time between the baseline and first follow-up visit was 6 . 3 months ( ± 0 . 41 ) and between the first follow-up visit and the second follow-up visit was 6 . 3 months ( ± 0 . 47 ) . The average time between the baseline and second follow-up visit was 12 . 6 months ( ± 0 . 67 ) . Time between visits was equivalent among intervention groups . The second follow-up visit was completed between September 2012 and July 2013 . A total of 1517 children ( 86 . 2% ) attended all three visits . Of those who did not attend all three visits , 108 ( 6 . 1% ) attended the first visit only , 89 children ( 5 . 1% ) attended the first and second visits and 46 children ( 2 . 6% ) attended the first and last visits . The proportion of children reported to have received deworming outside of the trial during the study period was 25 . 7% in Group 1; 26 . 8% in Group 2; 26 . 3% in Group 3; and 30 . 3% in Group 4 . These differences were not statistically significant ( p = 0 . 49 ) . Baseline characteristics of the study population by intervention group are found in Table 2 . Groups were similar in terms of baseline weight ( kg ) and length ( cm ) , age ( months ) , birth weight ( kg ) and length ( cm ) , continued breastfeeding , up-to-date vaccinations and hospitalizations since birth . There were small differences in the proportion of girls in each group and vitamin A supplementation in the previous year . In terms of maternal and household characteristics , groups were similar in the proportion of mothers who were married or common-law , the level of maternal education , and access to potable water in the home . Small differences were found in maternal employment outside of the home and area of residence . Baseline characteristics were similar between children who attended the final follow-up visit and those who missed their final visit ( S1 Table ) . At baseline , the prevalence of any STH infection was 14 . 5% in the two groups whose specimens were analyzed by the Kato-Katz method ( i . e . 13 . 6% in MBD/PBO and 15 . 2% in MBD/MBD ) ( Table 3 ) . At the 18-month visit , any STH prevalence was 28 . 5% ( i . e . 30 . 7% in PBO/MBD and 26 . 4% in MBD/MBD ) . As expected due to lower sensitivity , STH prevalence in children whose stool specimens were analyzed by the direct method at 12 and 18 months was moderately lower ( i . e . 10 . 5% and 24 . 5% , respectively ) . Certain sensitivity analyses were therefore conducted in subgroups of children found to be STH-positive 1 ) by both the direct and Kato-Katz methods and 2 ) only by the Kato-Katz method . Despite potential misclassification of STH infection status in children whose specimens were analyzed by the direct method , this strategy allowed for maximum comparison among all groups . Infection was predominantly low intensity for Trichuris and hookworm infection at all three time points; however , moderate and heavy intensity Ascaris infection increased over the one-year follow-up period ( Table 3 ) . At the 24-month visit , at which time all specimens were analyzed by the Kato-Katz method , the overall prevalence of any STH increased to 42 . 6% . Prevalence of Ascaris , Trichuris and any STH infection was moderately lower in the groups which received deworming at the 18-month visit . Hookworm infection remained negligible . No statistically significant difference in any STH prevalence or Ascaris or hookworm prevalence was observed in any of the deworming intervention groups compared to the control group; however , a statistically significantly lower prevalence of Trichuris infection was observed in Group 3 , which received mebendazole at both the 12 and 18-month visits , compared to the control group ( RR = 0 . 69; 95% CI: 0 . 52 , 0 . 90 ) ( S2 Table ) . All groups gained between 1 . 93 and 2 . 05 kg in weight and between 9 . 61 and 9 . 84 cm in length , on average over 12 months . The greatest changes in all growth outcomes between the 12- and 24-month visits were seen in Group 1 ( Table 4 ) . The average difference in weight gain ( kg ) compared to placebo was: 0 . 05 ( 95% CI: -0 . 05 , 0 . 17 ) in Group 1; -0 . 07 ( 95%CI: -0 . 17 , 0 . 04 ) in Group 2; and 0 . 04 ( 95%CI: -0 . 06 , 0 . 14 ) in Group 3 . When comparing the outcomes in each of the deworming intervention groups to the control group , however , no statistically significant effect was detected in unadjusted or adjusted ITT analysis ( Table 4 ) . No statistically significant difference in any intervention group compared to the control group was seen in per-protocol analysis ( S3 Table ) , complete case analysis ( S4 Table ) or in analysis restricted to only those children who were positive for STH infection at baseline ( S5 Table ) . In examining the effect of the timing at which deworming was administered , a statistically significant improvement was seen in Group 1 compared to Group 2 , in terms of weight gain ( unadjusted difference 0 . 12 kg; 95% CI: 0 . 01; 0 . 23 ) , length gain ( unadjusted difference 0 . 31 cm; 95% CI: 0 . 04 , 0 . 58 ) , WAZ change ( unadjusted difference 0 . 13; 95% CI: 0 . 03 , 0 . 23 ) , and LAZ change ( unadjusted difference: 0 . 12; 95% CI: 0 . 03 , 0 . 21 ) between baseline and the final follow-up visit in unadjusted analyses ( S6 Table ) . These results remained significant in adjusted analyses ( S6 Table ) per-protocol analysis ( S7 Table ) , complete case analysis ( S8 Table ) . In subgroup analyses restricted to children positive for STH infection at baseline , no significant differences were observed between groups ( S9 Table ) . In comparing the difference in anthropometric outcomes between Group 1 , receiving deworming once yearly , and Group 3 , receiving deworming twice yearly , no additional benefit on weight or length was apparent for twice-yearly deworming in unadjusted or adjusted analyses ( S10 Table ) . Results remained consistent in per-protocol analysis ( S11 Table ) , complete case analysis ( S12 Table ) and in restricted analyses to children infected with STH at baseline ( S13 Table ) . A statistically significant benefit , however , was observed in Group 3 compared to Group 2 , in terms of weight gain and WAZ change . These results remained significant for both weight gain and WAZ change when adjusting for baseline characteristics , in per-protocol and complete case analyses . From baseline until the end of follow-up , 38 minor adverse events were reported and were similarly distributed among groups ( i . e . Group 1: 7; Group 2: 10; Group 3: 12; and Group 4: 9 ) . There were 18 serious adverse events reported: Group 1: 5 deaths and 2 hospitalizations; Group 2: 1 death and 0 hospitalizations; Group 3: 3 deaths and 2 hospitalizations; and Group 4: 2 deaths and 3 hospitalizations . Ten serious adverse events occurred after administration of mebendazole ( i . e . 7 deaths and 3 hospitalizations ) and eight serious adverse events occurred after administration of placebo ( i . e . 4 deaths and 4 hospitalizations ) . The range of time between administration of the randomized intervention and occurrence of the serious adverse event was 6 days to 6 months for hospitalizations and 18 days and 7 months for deaths . None of these serious adverse events were deemed to be related to the deworming intervention by the DSMC , Research Ethics Committees in Canada and Peru , or the trial investigators .
We were not able to demonstrate an overall benefit on the primary research question of deworming on growth between any of the intervention groups compared to the control group after one year of follow-up in intention-to-treat analysis or in further sensitivity analyses . Our results are consistent with a recent cluster-randomized trial of albendazole ( administered every six months to children from six months to six years of age ) conducted in north India where light intensity STH infection was also predominant [21] . It is also consistent with a study in Uganda in children aged 15 months to 5 years who received quarterly albendazole [22] . Our findings do , however , contradict other trials in preschool-age children that found a positive effect of deworming on growth indicators [12 , 20] . The lack of benefit in our study compared to these other studies could reflect a true lack of effect of deworming on growth in the time period and/or study population . This population of children has a high level of malnutrition that may not be able to be treated solely by one or two doses of deworming in a one-year time period . In addition , we were not able to demonstrate a statistically significant reduction in any STH or species-specific prevalence with any of the deworming interventions , except for a reduction in Trichuris infection with twice-yearly deworming . The poor effect of the deworming intervention on STH prevalence measured after 6 and 12 months was almost certainly influenced by the dynamics of re-infection and new infection occurring between study time points . Future studies , which are adequately powered to detect changes in STH infection over time , are needed to confirm these findings . If there were , however , a true effect that was not observed , the short follow-up time may have limited the potential to detect this benefit . It is likely that , in the one year period of our study , a steady state has not yet been achieved , in terms of either STH infection ( e . g . as evidenced by the over threefold increase in STH prevalence from 12 to 24 months of age ) or growth ( e . g . as evidenced by a negative deviation of WAZ and LAZ compared to the international WHO growth standard over 12 months ) . Benefits of the deworming intervention may be apparent only with a longer follow-up time . The low prevalence and intensity of infection , in particular , may have limited the impact of deworming , which reduces morbidity primarily through a reduction in moderate and heavy intensity infection . In deworming interventions , nutritional improvements are not a direct consequence of drug administration but a result of the elimination of parasites that are competing for nutrients . When the intervention is administered to a population with low prevalence and/or intensity , the short-term benefits could be difficult to measure . WHO recommends the periodic ( once or twice-yearly ) administration of antihelminthics as a means of controlling morbidity from STH infection . The nutritional benefits are a consequence of the maintenance of very a low level of these infections in childhood . The baseline prevalence of STH infection in the study population was lower than had been anticipated based on a study conducted in the area just three to four years prior ( i . e . in 2007 and 2008 ) [13] . The number of children who could have potentially benefited from deworming in the trial was therefore reduced , resulting in a reduction in power to detect an effect of the expected size . Results from a previous trial suggested that deworming could improve growth in young children , even with low intensity infection [12]; however , we did not observe this in our trial . Preventive chemotherapy programs include treatment of both infected and uninfected children; nonetheless , our results suggest that research studies should be conducted in areas of high STH prevalence to ensure as little effect dilution as possible . With increasing implementation of deworming programs , a rapid assessment in the age group and study area to determine baseline prevalence and intensity may be warranted before beginning any research study . Our trial was unique in using a multiple group design to look additionally at the secondary research questions of differences in the timing and frequency among the groups that received deworming . Such considerations are important in operationalizing deworming interventions in this age group . Our results suggest that , if deworming is provided between one and two years of age , there is a significant benefit of providing it earlier rather than later . Our results also demonstrate that there was no added benefit from an additional dose provided at 18 months of age ( over and above that at 12 months of age ) . These results were consistent in unadjusted and adjusted analysis , as well as in sensitivity analyses , for multiple growth indicators . A true benefit of earlier deworming compared to later deworming is biologically plausible , as suggested in nutritional research showing the importance of incorporating interventions as early as possible to prevent adverse health and nutritional consequences [16] . However , in light of the lack of benefit of any of the deworming interventions compared to the control group and the low STH prevalence and intensity at baseline , these results should be interpreted with caution . The difference in growth between Groups 1 and 2 may be due to a shorter follow-up from the time of intervention to the time of outcome measurement ( i . e . 12 months in Group 1 vs . 6 months in Group 2 ) , or a chance finding of lower average weight gain in Group 2 compared to all three other groups . Although the number of statistically significant findings was more than due to chance alone , and all comparisons , except for sensitivity analyses ( i . e . complete case , per-protocol , and subgroup analyses in STH-infected individuals ) , were specified a priori , we cannot rule out the possibility that this difference could be a spurious finding due to chance alone . One issue that arose after beginning the study was difficulty with compliance , as over 25% of children received deworming at least once outside of the assigned intervention group . A higher proportion of children in the control group had been reported to have received deworming outside of the trial , but this proportion was not statistically significantly different from the other groups . Even after taking non-compliance into account by conducting a per-protocol analysis , excluding those who took deworming outside of the trial and/or who did not attend all three study visits , no statistically significant difference in growth outcomes was observed . The ease of access to deworming was an unexpected result as deworming is not routinely provided to children under two years of age in the study area; however , this level of access to deworming outside of the study protocol has been observed in other trials in preschool-age children [22] . With the growing presence of deworming campaigns and availability of antihelminthics without a prescription in many countries , this finding of non-compliance will likely become increasingly common . Although for ethical and logistical purposes we could not restrict access to antihelminthics outside of the trial , it is imperative that compliance is measured in all deworming research studies and taken into account in the analysis of results . For ethical and scientific reasons , we did not immediately analyze stool specimens from children randomized to placebo at the 12 or 18-month visits . This meant that accurate STH prevalence and intensity were not available for those receiving placebo ( i . e . Group 2 ( PBO/PBO ) and Group 3 ( MBD/MBD ) at 12 months , and Group 1 ( MBD/PBO ) and Group 3 ( MBD/MBD ) at 18 months ) , and that results from the Kato-Katz and direct methods were not easily comparable ( Table 1 ) . Examining a single stool specimen with a single technique ( i . e . Kato-Katz ) may have also decreased the sensitivity to detect STH infection , particularly in those with low intensity infection [37]; however , considering the sample size and age group of children in the study , the collection of multiple specimens was not considered to be feasible . Our strategy had the advantage of providing accurate overall baseline STH prevalence of the study population ( as all groups would be expected to have similar baseline prevalences due to randomization ) , and accurate final STH prevalences at the 24-month visit ( at which time all groups were analyzed by the Kato-Katz method ) . As STH infection status was a secondary outcome ( weight gain being the primary outcome ) , misclassification of infection status would not have affected the analyses on growth or on the effect of the deworming intervention on STH infection at the 24-month visit . Misclassification might have affected only the secondary subgroup analyses restricted to the STH-infected population . Despite the limitations to the strategy we employed , we consider ours to have methodological advantages to other strategies which have been used , such as: 1 ) not collecting stool specimens , which provides no information on baseline or follow-up STH infection[20] , or; 2 ) analyzing all stool specimens immediately , and treating those found to be infected , regardless of allocated intervention group , which would dilute the effect size by providing treatment to those randomized to placebo if found to be STH-positive [22] . Overall , this is the first trial to provide evidence on the effect of deworming , including optimal timing and frequency , on growth exclusively in children in the critical time window between one and two years of age . This trial demonstrated the feasibility of incorporating deworming into routine growth and development health clinics along with other essential early childhood interventions . We were also able to demonstrate safety of the deworming intervention in this age group , with similar numbers of serious adverse events occurring after mebendazole and placebo administration . This is consistent with results from previous studies [22 , 23 , 38] . Continued observational follow-up of the trial cohort is currently taking place , and will provide evidence on the longer-term effects of deworming up to five years of age . Future studies looking at the benefit of deworming on growth in this age group should include study areas of higher STH prevalence and/or intensity , higher potential compliance to the assigned intervention ( i . e . lower availability of anthelminthics in the community ) and longer follow-up time . Further studies should include other factors that are important to consider for scaling up deworming interventions in this age group . This includes: 1 ) cost-effectiveness of preventive chemotherapy vs . analyzing and treating only infected individuals; 2 ) feasibility and cost-effectiveness of integrating deworming with other health , nutritional and environmental interventions , particularly health education and micronutrient supplementation; 3 ) health and nutritional consequences of low intensity infection in younger age groups; and 4 ) inclusion of high-risk children living in more remote areas and/or those who do not regularly attend health services . This type of research is essential to contribute to strengthening the evidence base on deworming . | The World Health Organization recommends starting population-based deworming interventions as of 12 months of age where intestinal worm infection is common; however , little is known about the benefits in early preschool-age children . We conducted a clinical trial to determine the effect of deworming on growth in one-year-old children in Peru . Participating children were randomly assigned to: 1 ) deworming at 12 months of age; 2 ) deworming at 18 months of age; 3 ) deworming at 12 and 18 months of age; or 4 ) no deworming ( i . e . control group ) . A total of 1760 children were enrolled between September 2011 and June 2012 , and followed up for one year . Overall , with one year of follow-up , no effect of deworming on growth could be detected in this population of preschool-age children . The potential benefit of the intervention may have been affected by low baseline infection prevalence and/or low compliance to the randomly assigned intervention . Additional research is required to overcome these challenges and to contribute to strengthening the evidence base on deworming . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | The Effect of Deworming on Growth in One-Year-Old Children Living in a Soil-Transmitted Helminth-Endemic Area of Peru: A Randomized Controlled Trial |
Co-expression networks are routinely used to study human diseases like obesity and diabetes . Systematic comparison of these networks between species has the potential to elucidate common mechanisms that are conserved between human and rodent species , as well as those that are species-specific characterizing evolutionary plasticity . We developed a semi-parametric meta-analysis approach for combining gene-gene co-expression relationships across expression profile datasets from multiple species . The simulation results showed that the semi-parametric method is robust against noise . When applied to human , mouse , and rat liver co-expression networks , our method out-performed existing methods in identifying gene pairs with coherent biological functions . We identified a network conserved across species that highlighted cell-cell signaling , cell-adhesion and sterol biosynthesis as main biological processes represented in genome-wide association study candidate gene sets for blood lipid levels . We further developed a heterogeneity statistic to test for network differences among multiple datasets , and demonstrated that genes with species-specific interactions tend to be under positive selection throughout evolution . Finally , we identified a human-specific sub-network regulated by RXRG , which has been validated to play a different role in hyperlipidemia and Type 2 diabetes between human and mouse . Taken together , our approach represents a novel step forward in integrating gene co-expression networks from multiple large scale datasets to leverage not only common information but also differences that are dataset-specific .
The advent of expression profiling and other high throughput technologies has enabled us to systematically study complex human diseases by simultaneously measuring tens of thousands of molecular species in any given cell-based system [1] . It is now routine to organize such large-scale gene expression data into co-expression networks to shed light on the functional relationships among genes , and between genes and disease traits [2] , [3] , [4] , [5] . Analysis of co-expression networks can be used to study any tissue or organ ( such as liver , which plays a key role in the metabolism of glucose , lipids and toxic compounds ) , as long as the samples from such organs are collected in a population setting . Given that mouse and rat populations are commonly used to study human diseases in this manner , it is important to understand the mechanisms that are conserved between human and the rodent species , especially as we seek better predictions of the efficacy of drug targets identified from mouse or rat in human populations . In addition , identifying mechanisms that differ between humans and rodents can help to improve the design and interpretation of toxicity studies that involve rodent models . Meta-analysis is the statistical synthesis of data by aggregating results from a set of comparable studies [6] . It can be used to systematically examine similarities and differences between molecular profiling studies carried out in populations from different species [7] . In a gene co-expression network , relationship between gene pairs is usually measured by correlation coefficients of different forms , such as Pearson correlation , Spearman correlation , or Mutual Information . Therefore , the problem of combining or comparing co-expression relationships across multiple datasets can be framed in the context of a meta-analysis of correlation coefficients , for which various methods have already been introduced . One method is Fisher's Inverse test , which computes a combined statistic ( S ) from the p-values of the correlation coefficients obtained from ( k ) individual datasets as , . Under fairly general conditions this statistic follows a distribution with degrees of freedom under the joint null hypothesis of no correlation , making it possible to compute p-values of the combined statistic . Another widely used meta-analysis method involves computing a weighted average of a common metric ( i . e . effect size ) derived from correlation coefficients in the individual datasets . Such statistic can then be used to test for homogeneity over the individual measures and for statistical significance . Datasets in this type of meta-analysis are typically weighted by the accuracy of the effect size they provide , which is a function of the individual sample sizes . Once the mean effect size is calculated , its statistical significance can be assessed by estimating the pooled variance of the mean effect size . In defining the effect size , Hedges and Olkin [8] and Rosenthal and Rubin [9] both advocated converting the correlation coefficient into a standard normal metric using Fisher's Z-transformation and then calculating a weighted average of these transformed scores . Depending on whether the effect sizes are assumed to be equal or not in the multiple datasets , fixed effect as well as random effect models can be employed . In the fixed effect models , the effect size in the population is a fixed but unknown constant and therefore is assumed to be the same for all datasets included in the meta-analysis . For random effect models , effect sizes may vary from dataset to dataset , and are assumed to be a random sample of all population effect sizes . Hunter and Schmidt [10] introduced a single random-effects method based on untransformed correlation coefficients . One important feature of this type of method is that heterogeneity of the effect sizes can be estimated , which provides a way to assess the difference in correlation coefficients across multiple datasets . Schulze [11] provided a thorough review of these meta-analysis methods and their applications . For a meta-analysis of co-expression networks from diverse datasets , such as those constructed from different species , one central issue is that it is often unreasonable to assume that every gene pair has a unique , true effect size across evolutionarily diverse species . Although random effect models provide a more realistic way to accommodate cross species variation , it still assumes a parametric distribution on the population effect sizes . To circumvent this problem , a non-parametric meta-analysis method was introduced for the identification of conserved co-expression modules from human , fly , worm and yeast [7] . In this method , Pearson correlation coefficients of expression profiles between every gene pair were computed in each organism and then rank-transformed according to their correlations with all other genes . A probabilistic test based on order statistics was then applied to evaluate the probability of observing a particular configuration of ranks across the different organisms by chance . The advantage of this method is two-fold: 1 ) because the method is based on non-parametric statistics , it makes no assumption on the underlying distribution of correlation coefficients across multiple datasets; and 2 ) the effect size ( i . e . the rank ratio statistic for every gene pair ) is defined in a gene-centric fashion such that for any given gene , correlations with all other genes are considered . However , the method also has several limitations including 1 ) the loss of power in general given the non-parametric formulization [12] , [13] , and 2 ) the meta-analysis results cannot be represented in the same format as the individual datasets given there is no concept of a mean effect size . The details of individual methods are presented in the Methods section . Their pros and cons are summarized in Supplementary Table S1 . In this paper , we develop a method for the meta-analysis of diverse datasets generated across multiple species . Our method is semi-parametric in nature , requiring fewer assumptions on the distribution of the effect size than a purely parametric approach while retaining better statistical power than a fully non-parametric method . It also 1 ) defines an effect size that is gene centric , 2 ) allows for the computation of a mean effect size , and 3 ) leads to a heterogeneity statistic to test for differences in correlation structures among distinct datasets . Unlike most network alignment algorithms [14] , [15] , [16] , [17] , [18] ( with the exception of [19] ) or connectivity-based approaches [20] , our method does not rely on the networks inferred a-priori from individual datasets , but instead focuses on the development of rigorous statistics to test directly the relationship between every gene pair . The simulation results showed that our method is robust against noises . When applied to a human , mouse and rat cross species meta-analysis of liver co-expression networks , we demonstrate that our method out-performs existing methods in identifying functionally coherent gene pairs that are conserved among the three species . Our method also leads to the identification of modules of co-expressed genes that represent core functions of the liver that have been conserved throughout evolution . Both highly replicated and less confident genome-wide association study ( GWAS ) candidate genes for blood lipid levels are found to be enriched in the conserved modules , providing a systematic way to elucidate the mechanisms affecting blood lipid levels . Application of our test for homogeneity leads to the identification of a single sub-network driven by ApoE that distinguishes two nearly identical experimental cross populations whose genetic backgrounds only vary with respect to the gene ApoE . We further demonstrate that genes involved in human- or rodent- specific liver interactions tend to be under positive selection throughout evolution . Finally , we identified a human-specific sub-network regulated by RXRG , which has been validated to play a different role in hyperlipidemia and Type 2 diabetes between human and mouse . Taken together , our approach represents a novel step forward in integrating gene co-expression networks from multiple large scale datasets to leverage not only conserved information but also differences that are dataset-specific .
The intuition behind our meta-analysis approach in the cross-species setting is that , instead of directly comparing the correlation coefficients of a gene pair as an absolute measure of co-expression , which depends on many features such as sample size , expression dynamics , measurement noise , and confounding factors that are usually not well-controlled among the individual datasets , we measure the co-expression relationship as a relative distance with respect to each gene's total relationship to all other genes in each dataset . When the correlation coefficients between a given gene and all other genes were rank-transformed into a uniform distribution , the inter-relationships among the correlations were destroyed . Unlike the previous method [7] we assume the distribution of correlation coefficients of one gene to all other genes follows a normal distribution under the condition that the numbers of samples and genes are large ( see Materials and Methods section for details ) . In fact , for roughly 70–90% of the expression traits in our datasets , the distributions of their correlation coefficients to all other expression traits are well supported as being normal by the Kolmogorov-Smirnov test ( Figure S1 ) . Based on this assumption , we define for gene pair ( i , j ) in dataset , the effect size of its co-expression according to Glass's d score definition [21] as:where is the correlation coefficient between the expression profiles of ( i , j ) in dataset , and and are the mean and standard deviation of the null distribution , respectively , of the correlation coefficients between gene and all other genes . Essentially , by this definition we transform the correlation measure into a relative distance to the gene-centric mean in terms of standard deviation units . This transformation not only normalizes all effect sizes , but also takes into account the context of each gene in individual datasets . It is of further note that our effect size definition is directional , i . e . is usually different from due to differences in the neighborhoods of gene and . For simplicity , we drop the superscript so that represents the effect size for any gene pair in dataset . Using a meta-analysis procedure for d score that developed by Hedges and Olkin [8] , we can compute the mean effect size as:and the standard deviation of the mean effect size as:The statistical significance of the mean effect size can then be assessed by forming the Z-score statistic: In addition , heterogeneity of the effect sizes across the datasets can be estimated by the statisticwhich follows a distribution with degree of freedom under the null hypothesis of homogeneous effect sizes . Given the mean effect size and heterogeneity statistic , a flowchart of our method is summarized in Figure 1 . Briefly , the first step begins by computing correlation coefficients for all gene pairs in every dataset . Correlation can be measured by the Pearson or Spearman correlation , depending on the properties of the datasets being analyzed . The method then proceeds by iterating through all gene-pairs one at a time , computing the heterogeneity statistic for every gene-pair . If homogeneity is not rejected at a pre-specified significance level , the mean effect size for the gene-pair is computed and tested for deviation from zero . A statistically significant mean effect size is then considered as a conserved co-expression relationship among the datasets being compared . On the other hand , if the homogeneity of the effect sizes is rejected , the gene-pair is considered as a candidate for change in co-expression relationships , termed differential interactions hereafter , between the datasets . In this case , the direction of change can be determined by examining the actual effect sizes in single datasets . To compare the performance of our semi-parametric method with the existing parametric and non-parametric methods , we ran several simulations . In each simulation , 3 independent data sets were generated assuming the underlie structure is modular as shown in Figure S2 ( see Materials and Methods section for details ) . There were 150 samples and 2000 genes in each data set . The signal strength is measured by the correlation between the latent regulators and their downstream genes . The signal strengths were different for the 3 simulated data sets , shown in Figure 2A . When there was no systematic noise , the parametric methods ( FEM Fisher-Z and combine p-value ) performed better than non-parametric method , shown in Figure 2B . It is consistent with other studies' results that there are power losses in general for non-parametric methods [12] , [13] . The performance of our semi-parametric method was between the parametric methods and the non-parametric method . It is consistent with the nature that our semi-parametric is a hybrid of parametric and non-parametric methods . It is worth to note that the random effect model ( REM Fisher-Z ) performed worst among methods tested even though the effect sizes were different as shown in Figure 2A . When the systematic noises were moderate ( measured by the correlation between genes and systematic noises ) as shown in Figure 2C , the performances of our semi-parametric method and the parametric methods were similar , shown in Figure 2D . When the systematic noises were stronger ( shown in Figure 2E ) , the performances of parametric methods decreased significantly , and our semi-parametric and non-parametric methods were robust against systematic noises ( shown in Figure 2F ) . Under all conditions , our semi-parametric method performed better than the non-parametric method . We applied our method to identify conserved co-expression interactions among 6 , 455 orthologous genes in human , mouse and rat ( see Materials and Methods for details about the data , data preparation and orthologous gene identification . The 6 , 455 genes are listed in Table S2 . The 2-D hierarchical clustering views of individual data sets are shown in Figure S3 , and ordered sample and gene annotations are listed in Table S3 , S4 , S5 , S6 , S7 , S8 ) . We used the absolute Spearman correlation coefficient between the expression profiles of a gene pair as the measure of co-expression interaction . By doing this we considered only the magnitude of gene-gene correlation , but not its direction , since the same gene-gene relationship may manifest as either a positively or negatively correlated expression profile due to feedback control [4] . Specifically , our method inferred 20 , 230 conserved co-expression interactions , covering 4 , 885 genes , at a p-value cutoff of , corresponding to a Bonferroni corrected false positive rate of 5% ( i . e . ) for both effect size and the heterogeneity . The false discovery rate ( FDR ) of this result is estimated to be based on a permutation test procedure where sample labels were randomly shuffled for each gene independently in every dataset ( see Materials and Methods for details ) . These conserved interactions represent approximately 2 . 4–15 . 2% of the co-expression interactions obtained using single species data , given there were 828 , 031 , 334 , 721 and 132 , 884 interactions in human , mouse and rat , respectively , at the same statistical significance p-value threshold . We benchmarked the performance of our method against existing meta-analysis methods in the literature , as well as against the interactions previously reported for single species co-expression networks [22] . The number of predictions ( i . e . conserved interactions ) inferred by our method lies in between the numbers predicted by existing parametric and non-parametric meta-analysis methods at a common FDR threshold , shown in Table S9 , consistent with the semi-parametric nature of our approach . When only considering the same number of top confident predicted pairs , the qualities of the semi-parametric method were better than other methods in terms of coherences with both Gene Ontology ( GO ) biological processes and curated KEGG pathways ( shown in Table S10 ) . To test the full range of predictions , we generated precision vs . coverage curves for each method by varying the statistical significance thresholds and computing 1 ) the percent of inferred gene pairs that share a common GO biological process annotation , and 2 ) the percent of inferred gene pairs that share a common curated KEGG pathway ( Figure 3 ) . Two conclusions stand out from these results . First , all meta-analysis methods outperform the inference based only on single species datasets , likely due to the increased precision achieved by incorporating evolutionary information and the added power achieved by integrating multiple datasets . Second , our method clearly outperformed all existing meta-analysis methods across the full spectrum of coverage , but most significantly at the stringent p-values . This demonstrates the added value of combining the advantages of existing methods . We next performed spectral clustering of the orthologous genes based on their interconnectivity in the conserved co-expression network and identified co-expressed gene modules , shown in Figure S4 ( see Materials and Methods for the spectral clustering method ) . Table 1 summarizes the top 13 modules comprised of greater than 20 genes and their enrichment for GO biological process terms . Almost all of the modules are observed to be coherent with respect to some biological processes and many of the indicated processes represent core biological processes in the liver , including immune response ( p<2 . 70×10−43 ) , carboxylic acid metabolic process ( p<6 . 6×10−16 ) , and sterol biosynthetic process ( p<1 . 9×10−27 ) . It is of particular note that these modules differ from modules identified in single species datasets in that the genes in modules of the conserved co-expression network are functionally related based on evolutionary conservation , rather than on correlated gene expression alone . Recent human genome-wide association studies have identified many candidate genes affecting blood lipid concentrations . However , the mechanisms by which many of these candidate genes contribute to blood lipid concentration remains unclear [23] . In addition , there are potentially many SNPs with weaker associations to lipid concentration that are difficult or impossible to detect or replicate given the lack of power in current GWAS [24] . Therefore , an open question is whether there are many more genes harboring common variation that affect the polygenetic nature of lipid concentration regulation . Because liver is a key tissue for lipid metabolism , we can use the liver networks to interpret the GWAS results and generate hypothesis regarding the mechanisms of the candidate genes . Toward this end , we selected 30 recently identified lipid-associating loci [25] and assessed the ability of our conserved modules to annotate the 45 candidate causal genes nominated from these 30 loci . Of the 45 candidate genes , 26 have orthologs in human , mouse and rat and were therefore included in our study . Nineteen of these genes reside in human , mouse and rat conserved modules ( Table 2 ) , where the putative mechanisms with respect to lipid regulation can be annotated based on the module functions . The results suggest that cellular processes such as sterol biosynthetic process and cell-cell communication are involved in regulating blood lipid concentration . Of particular note is SORT1 , a gene that resides at the locus most significantly associated with LDL cholesterol [25] . Based on the conserved modules , SORT1 belongs to module 1 , a module enriched for genes involved in cell-cell signaling ( p-value<6 . 51×10−23 ) . Other candidate genes at lipid associated loci , such as GALNT2 and NCAN , also reside in module 1 , suggesting that cell-cell signaling is important for blood lipid regulation . PCSK9 is clearly annotated as being involved in the sterol biosynthetic process along with FADS1 , FADS2 , HMGCR and MVK . In contrast , only 14 of 26 candidate genes can be annotated based on modules derived from the human co-expression networks alone ( Table 2 ) . The annotations of these genes based on the conserved modules are closer to their known functions than ones based on the human modules ( shown in Table S11 ) . For example , MAFB is annotated as “transcription regulation” based on the conserved modules , but as “carboxylic acid metabolic” based on the human-only modules , whereas its annotation in GO is “positive regulation of transcription from RNA polymerase II promoter” . These examples illustrate how the conserved human , mouse and rat modules can enhance the interpretation of GWAS and the annotation of candidate genes identified from these studies . Blood lipid concentration regulation is a complex process , involving many different cellular pathways . We have recently demonstrated that common variation of complex traits is caused by networks of genes as opposed to single genes [4] . To assess whether GWAS results associate with entire networks of genes , we overlapped blood lipid concentration results from the Framingham heart study [26] and the Broad Institute lipid study [27] with the human , mouse and rat conserved liver network . In this analysis , we consider a gene as associated with the blood lipid trait if any SNP associated with the trait in these studies lies within 50Kb of the gene . Then , at a p-value threshold of 0 . 001 , 22 . 2% of the genes with human , mouse and rat orthologs are associated with blood lipid concentration in either study . At the same p-value cutoff , 19 . 7% of all human genes in our dataset were associated with blood lipid concentration , suggesting that the lipid concentration regulation mechanism is conserved globally ( ∼1 . 13 fold enrichment , Fisher's Exact Test ( FET ) p-value = 5 . 38×10−11 , permutation adjusted p-value<0 . 001 , Figure S5A ) . The distribution of genes associated with blood lipid concentration among the modules is shown in Figure 4A . Seven of the 13 modules were observed to have a higher concentration of genes associated with blood lipids than the background . Modules 1 , 7 and 11 were significantly enriched for genes associated with blood lipid levels ( 1 . 14 , 1 . 41 and 1 . 55 fold enrichment with FET p-values of 1 . 7×10−3 , 6 . 6×10−3 , and 7 . 4×10−3 , respectively ) . These results suggest that cell-cell signaling , cell-adhesion and sterol biosynthesis pathways are associated with variation in blood lipid concentration regulation in the human population . In contrast , a similar test was applied to modules identified from human expression profile data alone . The module with the highest overlap with genes associated with blood lipid traits was not enriched for a coherent biological process and the module enriched for carboxylic acid metabolism were not significantly enriched for genes associated with blood lipid traits ( Figure 4B ) . We have further showed that these results are not sensitive to the window size around the lipid-associating loci for selecting lipid-associating genes . The trends of the global conservation of lipid-associating genes and results in Figure 4 hold true also for window size of 10K , 20K , 30K and 40K ( Table S12 and Figure S6 ) . Genetic loci associating with blood lipid traits from both Framingham and Broad studies may harbor many genes in each of these regions . Dissecting the true causal genes from those irrelevant ones remains a significant challenge . We have previously shown that cis eSNPs – SNPs that are associated with the mRNA levels of genes residing in the same genomic regions – are enriched for functionally relevant genes associating with the trait of interest [28] . In addition to the cis eSNPs , functionally coherent gene modules , representing the cellular processes associated with the trait of interest , can also help pinpoint the true causal genes . By filtering the Framingham and Broad candidate lipid-associating genes with genes that either 1 ) harbor a cis eSNP in its vicinity , or 2 ) belongs to any of the three conserved co-expression modules enriched in lipid-associating genes , the overlap between the two studies becomes more significant than the un-filtered sets , demonstrating the utilities of cis eSNP and conserved co-expression modules in teasing out irrelevant candidate genes ( shown in Table 3; in this case , the cis eSNP genes we previously identified from a liver expression study were used [28] ) . There were 395 genes ( Table S13 ) that are associated with a cis eSNP in the human liver , and are also in the three conserved co-expression modules we identified as associated with the blood lipid trait . These genes represent the most likely causal genes controlling the blood lipid concentration by integrating GWAS candidate loci , human cis eSNP genes and conserved co-expression modules between human and rodent species . Among these genes , four of them , SORT1 , FADS1 , FADS2 and GALNT2 , are recently reported as candidate genes at highly replicated genetic loci contributing to polygenic dyslipidemia [25] . This result is statistically significant given there are only 26 such candidate genes in our initial set of 6455 orthologous genes between human and rodents ( a 2 . 51-fold enrichment , FET p-value<0 . 0189 , permutation adjusted p-value<0 . 015 , Figure S5B ) . These results demonstrate that the combination of multiple types of information can provide an objective way to infer causal genes under the loci of interest . Many factors contribute to the identification of differential interactions between human , mouse and rat , such as evolution differences , genetic background differences , and perturbation differences in the data sets ( such as genetic diversity in human liver data vs . diverse compound treatments in rat liver data ) , to name just a few . As a proof of concept , we applied our meta-analysis approach to identify differential interactions between the liver co-expression networks from two previously reported F2 intercrosses . The first F2 intercross was constructed between C57BL/6J ApoE null ( B6 . ApoE−/− ) mice and C3H/HeJ ApoE null ( C3H . ApoE−/− ) mice ( referred as BXH/apoe−/− ) [29] . The second F2 intercross was constructed between C57BL/6J ( B6 ) wild type mice and C3H/HeJ ( C3H ) wild type mice ( referred as BXH/wt ) [30] . These two crosses are essentially identical from the standpoint of genetic background , diet , and rearing , except that in one of the crosses the ApoE gene is knocked out . Given this single gene difference between the crosses , we hypothesized that differentially connected genes would be enriched for genes associated with ApoE related pathways . Our method identified 500 differentially connected genes involving 1 , 023 differential interactions between the BXH/wt and BXH/apoe−/− crosses . GO enrichment analysis for this set of genes revealed that the only over represented biological process were those involving ApoE [31] , albeit these processes are highly overlapping , including the cholesterol metabolic process ( 4 . 5% vs . 0 . 7% background , p<5 . 6×10−6 ) , the sterol metabolic process ( 4 . 5% vs . 0 . 9% background , p<1 . 2×10−4 ) and the lipid metabolic process ( 15 . 2% vs . 7 . 2% background , p<3 . 3×10−4 ) . Interestingly , no core biological processes in liver that do not involve ApoE ( e . g . , immune response ) were enriched , which serves as a negative control for our results . To test whether these differential interactions were mainly driven by expression dynamic changes as the result of the ApoE gene knockout , we selected a set of 500 genes with the largest difference in expression variation between the two crosses . GO enrichment analysis revealed no coherent biological functions represented in this set , indicating that the observed network changes could not be explained simply by dynamic differences in gene expression . We further examined the mouse protein-protein and protein-DNA interaction networks curated from interaction databases and literature , including Ingenuity , GeneGO and HPRD , around the ApoE gene . Of the 22 genes in the immediate neighborhood of ApoE , including ApoE itself , 4 ( 18 . 2% ) were inferred as differentially connected between the wild type and ApoE−/− crosses , and this proportion was highly significant ( ∼8 . 1 fold enrichment , FET p-value<1 . 1×10−4 , permutation adjusted p-value<0 . 001 ) ( Figure 5 and Figure S5C ) . Taken together , these results demonstrate the ability of our meta-analysis procedure to dissect differentially regulated pathways around specific molecular perturbations . Although our method is purely expression profile based , it can also recapitulate known physical interactions in the region of the source perturbation , which further supports the validity of our approach . Differential interactions among diverse organisms can result from true evolutionary differences or from incomplete perturbations in the datasets we examined , leading to reduced expression dynamics in one or both of the interacting genes . Here we assumed that the gene expression system in each species we examined was extensively perturbed , either directly or indirectly ( via second or higher order effects ) . The human samples were collected from more than 400 unrelated individuals , making up an out-bred population comprised of 400 diverse genetic backgrounds . The F2 mice obtained from the BXH crosses represent an in-bred population in which differences in the genetic background of the parental strains are randomly shuffled in each of the individual mice . The rat expression profiles were generated by treating rats with a compendium of drug compounds with various mechanisms of action . Therefore , although liver gene expression in each species is measured under different sets of perturbations , the extensiveness of these diverse perturbations was likely to render that most pathways were perturbed given there are a finite number of pathways . We carried out the cross-species meta-analysis in a pair-wise fashion to produce human vs . mouse and human vs . rat comparisons . For the human vs . mouse comparison our method identified 8 , 706 conserved interactions involving 3 , 205 genes , in addition to 613 differential interactions involving 547 genes . For the human vs . rat comparison , we identified 10 , 809 conserved interactions among 3 , 310 genes , as well as 447 differential interactions among 420 genes . All results were obtained using a p-value cutoff of . We further characterized each orthologous gene considered in the comparisons by classifying each gene's involvement in 1 ) only conserved interactions , 2 ) at least one differential interaction . Since it has been shown that genes differentially connected in the co-expression and physical interaction networks tend to evolve at different rate [32] , [33] , we also attempted to characterize the evolutionary rate for each group by measuring the ratio between the rate of non-synonymous to synonymous substitution ( Ka/Ks ) [34] in the protein coding regions of the respective genes . Interestingly , for both comparisons we found that genes involved in a larger number of differential connections tend to have a higher Ka/Ks ratio ( Figure S7 ) . These results suggest that stronger positive selection ( or relative weaker negative selection ) may lead to new advantages for a given gene by increasing or decreasing the number of its co-expression partners . To further illustrate this point , we expanded our analysis to include genes that are non-orthologous between human and rodents , and tested whether genes that were differentially connected among orthologous genes also tended to have more interactions with non-orthologous genes in a given species , compared to genes involved in only conserved interactions . This was indeed the case when we looked at the ratio of interactions to human-specific genes vs . human-rodent orthologs in the liver co-expression network built from human expression profiles ( Figure S8 ) . Taken together , these results demonstrate that positive selection may render a gene the ability to rewire its co-expression connections with evolutionarily conserved partners as well as to add new partners that emerge through speciation . One important aspect of understanding the difference in gene expression regulation between human and rodent species is that rodent species ( mouse in particular ) are frequently used to elucidate the complexity of human diseases . However , there is no guarantee that discoveries made in mouse regarding causes of disease will translate into human systems , so such results can be misleading [35] . In addition to mice being used as a model for human diseases , rats have been established as a critically important model for human drug metabolism and toxicity trials . However , the extent to which toxicity results in rat are faithfully reproduced in humans has not been well characterized [36] . Among the many species-specific variations between human and rodents that may cause such barriers , differential rewiring of the co-expression networks can be an important contributing factor . Understanding species-specific interactions , especially human-specific interactions , is a necessary step to develop relevant animal models for human diseases . Again using the same p-value threshold described above , 1 , 171 differential interactions were identified among the human , mouse and rat liver co-expression networks . An interaction between two genes is considered human-specific if 1 ) the co-expression relationship between the two genes is significantly different between human and the rodent species based on the heterogeneity test , 2 ) the correlation p-value of the two genes in human is smaller than , and 3 ) the correlation p-values for the two genes in both mouse and rat are larger than . Of the 1 , 171 differential interactions identified , 163 were human-specific . The top 20 genes with most human-specific interactions are listed in Table S14 . These genes are inter-connected to form three sub-networks ( Figure 6 ) . The largest sub-network consists of 11 genes , three of which ( PIP5K1B , RXRG and ACSBG1 ) are well known to be involved in lipid metabolism . RXRG ( retinoid X receptor gamma ) emerges as a key regulator of this human-specific sub-network . It is one of the genes with the most predicted human-specific interactions , and 7 out of 8 of its interactions involve other genes also with the most human-specific interactions ( PIP5K1B , TFAP2E , SLC22A13 , DAPK3 , RPS27 , FAT2 and ACSBG1 ) . RXRG homozygous mutant mice are normal [37] , suggesting that it may not exert any essential function in mouse . However , there are many evidences suggesting that RXRG variations in humans are associated with lipid metabolism [38] , as well as with glucose and Type 2 diabetes [39] . RXRG mutations are the most frequent variations in familial combined hyperlipidemia and are associated with triglycerides and HDL cholesterol [40] . These differences in RXRG's role between human and mouse are consistent with our prediction that there are differences between human and rodents networks around RXRG . In addition to RXRG's 8 predicted human-specific interactions with genes having a rodent ortholog , it is also known to be an upstream regulator of CETP [41] which has no corresponding ortholog in either mouse or rat . CETP encodes a cholesteryl ester transfer protein that plays a key role in regulating HDL cholesterol . Thus it may partially explain RXRG's contribution to lipid metabolism in humans . These results suggest that attention should be paid to retinoid X receptor activities when CETP transgenic rodent models are studied .
There are a number of systematic efforts for studying complex human diseases using human samples or animal models . Co-expression networks represent a powerful system-level tool for dissecting the architecture of gene expression , and the complex relationships between genes and disease associated traits . Combining co-expression networks across multiple datasets , especially those measured in common tissues from evolutionarily distant species , has the potential to greatly enhance the power to distinguish true associations among gene expression traits from those spurious interactions picked up by guilt-by-association techniques in single datasets . We presented a novel semi-parametric meta-analysis method to combine multiple high dimensional datasets from different species . When applied to the human , mouse , and rat liver co-expression networks , our method out-performed all existing methods with respect to the degree of biological coherence reflected by the identified gene pairs . Using the co-expression network conserved across human , mouse and rat , we identified cell-cell signaling , cell-adhesion and sterol biosynthesis processes as the primary mechanisms represented by GWAS gene candidates associated with blood lipid levels . In comparing human and rodent co-expression networks we found that ∼10% of the gene-gene co-expression relationships were conserved , in accordance with a recently published comparative analysis of human and mouse gene expression patterns [42] . The conserved interactions could be organized into gene modules that corresponded to core pathways that are critical to normal cellular functions , and therefore are likely to lead to disease if disrupted . Knowledge of the conserved interactions between human and rodent species has the potential to facilitate studies of human disease using rodent models . When we combined conserved liver modules with cis-eSNP information and GWAS results , we identified a list of 395 candidate genes regulating blood lipid levels . Six of these genes ( MTHFR , PEX5L , CPE , LIPA , UCP3 and PLIN ) have previously been shown to have mutant phenotypes in mouse that involve abnormal lipid levels . Systematic testing of the genes in this set using experimental techniques such as siRNA in cell-based systems could provide further confirmation of their involvement in regulating blood lipid concentrations . Under a unified framework , our method also allows the identification of gene-gene relationships that differ significantly between datasets . The sensitivity of our method to identify dataset-specific biological perturbations was well highlighted by the identification of a single sub-network driven by ApoE that was able to distinguish two nearly identical experimental cross populations whose genetic backgrounds were identical with the exception of ApoE ( knocked out in one of the crosses ) . This type of network comparisons can help characterize network plasticity due to evolution . We have shown that genes involved in such differential interactions between human and rodents are likely to be under positive selection for gaining or losing co-expression partners . Given that only ∼10% of gene-gene relationships are conserved between these diverse species , divergence in gene expression are likely to be more extensive than genome sequences . It has shown through a chip-chip study that the overlap of transcription factor binding sites is only about 20% across 3 different yeast species where sequence differences are about 0 . 05% [43] . In some cases , the promoter regions are identical across genomes of 3 yeast species , transcription factors only bound in one species but not others . Thus , variation in transcription regulation is much larger than sequence variation . There could be other factors affecting conversation of pairwise relationship in different data sets , such as 1 ) inadequate expression dynamics in those parts of the system that lack targeted perturbations , and 2 ) experimental and technological noise that subdue the real changes in co-expression . In addition to the meta-analysis methods we compared , there are graphic model-based meta-analysis or Bayesian meta-analysis methods which have been applied to gene expression data in several studies [44] , [45] . The performance of Bayesian meta-analysis depends on priors tuning . If noninformative priors are used , then the Bayesian meta-analysis is close to the random effect model . Even through effect sizes are clearly different in our simulated data and empirical data , the mixed effect model performed worse than the fixed effect model . On the other hand , our meta-analysis method is robust across multiple conditions without any tuning of parameters . In addition , the Bayesian meta-analysis is away more computation intensive than the method we proposed so that we did not include it in our comparison . Meta analysis of co-expression networks we proposed here allow us to compare co-expression networks constructed from data sets of heterogeneous experimental settings . If experimental settings are similar , then direct comparison of signature sets can also provide insights of conserved mechanisms at system levels . For example , a set of periodically expressed genes in H . sapiens , S . ceravisiae , S . pombe and A . thaliana was defined and then orthologs of these genes were compared to see whether they peaked during the same phase of cell cycle [46] . However , in our datasets , experimental conditions were different - the variances of human and mouse liver expression data were due to naturally occurred genetic variation , whereas those in the rat liver expression data were due to diverse compound treatment . Therefore , there is no common way to define gene signatures across different data sets that can be compared directly . Gene expression is one type of high throughput data that can be leveraged to systematically study human diseases . There are many other types of high-dimensional data to which our method could be applied , including protein-protein interaction , protein expression , metabolite expression , and Chip-on-chip data . Further developments are needed to combine these different types of data across different species . Nevertheless , even at its current stage , our method has been successful in identifying mechanisms that are common between and distinct to human and rodent species , which provides the potential to aid in the drug development process .
We profiled 423 human liver samples [28] , 382 liver samples of rats treated with different classes of drugs [47] , 300 mouse liver samples from an F2 murine intercross between C57BL/6J ApoE null ( B6 . ApoE−/− ) and C3H/HeJ ApoE null ( C3H . Apo E−/− ) ( referred as BXH/apoe−/− ) [29] , and 321 mouse liver samples from an F2 intercross between C57BL/6J ( B6 ) wild type mice and C3H/HeJ ( C3H ) wild type mice ( referred as BXH/wt ) [30] . For every gene in each expression dataset , the expression values were mean-subtracted and then divided by the standard deviation . Missing values were imputed by the robust regression based the expression of the gene most correlated to the query gene expression . Orthologous gene pairs between human , mouse and rat represented on microarrays were identified by taking the reciprocal best hit using BLASTN with an E-value cutoff of . This resulted in 8 , 767 orthologous pairs identified between human and mouse , 6 , 934 between human and rat , and 10 , 185 between mouse and rat . There were 6 , 455 orthologous genes common to all three species , which were selected for subsequent analysis ( Table S2 ) . To estimate the significance of a correlation coefficient , we generally convert to which follows a student t-distribution with . When the sample size is large enough , is approximately normally distributed [11] . However , convergence of the distribution is very slow and it is said to be unwise to assume its normality for n<500 [48] . The assumption for estimating the Pearson correlation coefficient distribution is that all vector pairs are independently and identically distributed . However , this may not hold true in practice such as microarray experiments due to the facts that 1 ) probes for two genes on the same chip may be correlated because that are subjected to many common noises and biases , and 2 ) two unrelated genes in a biological network are still remotely connected so that they can not be completely independent . As a result , not all gene pairs are independent , thus their expected correlation coefficient is not necessary zero . In this case , an empirical null distribution is needed . We note that empirical null distributions are different for each gene/probe so that there are 6455 null distributions instead of one global null distribution . We assume the empirical null distribution of all pair-wise correlation coefficients as a normal distribution based on the central limit theorem , which states that the mean of sufficiently large number of independent random variables will be approximately normally distributed [49] . In summary , we assume the empirical null distribution of pair-wise correlation coefficients as a normal distribution under two conditions: ( 1 ) the sample size is large so that the variation of is small; ( 2 ) the number of genes under study is large so that the central limit theorem can be applied . We note that our sample sizes are in the range of 300–500 , which are out of the recommended range for normal assumption . However , our normal assumption for correlation distributions of our data is supported by the Kolmogorov-Smirnov test of normality . The sample sizes of the data sets we simulated are 150 . We checked the distributions of correlation coefficients of each individual gene , and found that correlation coefficients for over 98% of genes are normally distributed . For the empirical data sets , correlation coefficients for over 70% of genes are normally distributed , which is shown in Figure S1 . We assume the underlie system consists of 2000 genes which are divided into10 functional modules and 1 null module , as shown in Figure S2 . Each functional module consists of 100 genes and the null module consists of 1000 . For simplicity , we assume each gene in a functional module is linearly related to a latent regulator and is simulated as , where is a vector ( , the sample size ) representing the expression of gene ( which belongs to the functional module ) in data set . is a vector for the latent variable in data set . is a vector representing systematic noise in the data set . is the random noise . Genes in the null model are not related and are simulated as , where is a random vector . and are regression coefficients representing the strengths of the signal and the systematic noise , respectively . The latent variables , the random signals , systematic noise and random noise are all assumed to be normally distributed with mean and different variances . The coefficients in are constrained by the strength of correlation . The sign of the coefficient was randomly assigned . It is similar for . We assume are jointly normally distributed , we can write their covariance as , where is of size , is of size , is of size , and is of size . The regression coefficients , and are then given by , and the error term , , is normally distributed with mean 0 and variance . To assess the goodness of the reconstructed coexpression networks derived from different meta-analysis methods , they were compared to the true network , which was formed by linking all genes in the same functional module as defined in the simulation process . We define the “goodness” of the reconstructed network in terms of its accuracy , which is measured by two parameters . The first parameter is defined as the precision of the network: , which is the proportion of detected interactions that actually exist in the true network . Precision corresponds to specificity and is equal to one minus the false positive rate ( ) . The second parameter is defined as the recall of the network: , which is the proportion of total interactions in the true network that are detected in the reconstructed network . Recall corresponds to sensitivity and is equal to one minus the false negative rate ( ) , which is also known as the true positive rate ( ) . The recall and precision for a perfectly reconstructed network are equal to 1 . The central figure of merit used to evaluate and compare the coexpression networks derived from different meta-analysis methods ( with respect to the true network ) is the recall vs . precision curve , which can be considered as a variation of the traditional Receiver Operator Characteristic ( ROC ) curve . ROC curves are generated by plotting the true positive rate ( TPR ) against the false positive rate ( FPR ) . The area under the ROC curve ( AUC ) is then a measure of how the constructed network compares to the true network . The larger the AUC , the better the constructed network compares to the true network , where the maximum AUC is 1 , indicating that the constructed network perfectly matches the true network . Qualitatively the recall vs . precision curve is equivalent to the ROC curve in that if the AUC for one network is greater than ( or less than ) the AUC of a second network with respect to one of plot types , that same relationship will hold for the other plot type . We opted to use the recall vs . precision plots over the ROC plots as the figure of merit because recall and precision are the more standard measures used in the network reconstruction community . The false discovery rate ( FDR ) of our meta-analysis results was estimated using permutation test procedures . For the conserved interactions , null datasets were created by randomly and independently shuffling the expression values of all genes in each dataset , thus breaking the inter-gene relationships while keeping intact the expression mean and standard deviation of the genes in every dataset . For the differential interaction , we generated the null datasets by shuffling the dataset membership of the samples , so that the permuted datasets are essentially random subsets of the total original samples . The same meta-analysis procedure was applied to both the original datasets as well as the permuted ones . The FDR was then computed as the ratio between the number of inferences made from the permuted datasets ( i . e . false discoveries ) over the number of inferences made from the original datasets ( i . e . total predictions ) . Only GO biological process categories with fewer than 1 , 500 genes ( according to human annotations ) were included for analysis , precluding non-specific categories , such as metabolic process , from entering the analysis . All GO enrichment analyses were performed using the Fisher's exact test , with all 6 , 455 orthologous genes forming the background gene set for the human , mouse and rat comparisons . For the BXH/wt vs . BXH/apoe−/− analysis , the background set was comprised of all genes represented on the microarray used in the study . To partition the network of genes obtained from our procedures into modules of genes , we employed the divide-and-merge methodology of clustering [27] , where a top-down divide phase based on a theoretical spectral algorithm [50] was used to obtain a clustering tree , and a bottom-up merge phase was used to parse the clustering tree to obtain a partition of the genes ( gene modules ) that optimized a certain objective function . We used the modularity function [51] to identify modules in the human-mouse-rat conserved network . The definition of modularity from the cited references is provided here for completeness . Let be a partition of the genes in a network into clusters . Then , where is the number of edges between two genes that both belong to , is the sum of the number of neighbors of all genes in , and m is the number of edges in the whole network . | Two important aspects of drug development are drug target identification and biomarker discovery for early disease detection , disease progression , drug efficacy and drug toxicity , etc . Recently , many single nucleotide polymorphisms ( SNPs ) associated with human diseases are discovered through large genome-wide association studies ( GWAS ) . However , it is still largely unclear how these candidate SNPs may cause human diseases . The ultimate aim of this paper is to put these GWAS candidate SNPs and their associated genes into a network context to understand their mechanism of action in human diseases . In addition to large-scale human data sets that are often heterogeneous in terms of genetic and environmental factors , many high quality data sets in rodents exist and are frequently used to model human diseases . To leverage such information , we developed a method for combining and contrasting gene networks between human and rodents , specifically to elucidate how GWAS candidate SNPs may contribute to human diseases . By identifying mechanisms that are conserved or divergent between human and rodents , we can also predict which disease causal genes can be studied using rodent models and which ones may not . | [
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] | 2009 | Meta-analysis of Inter-species Liver Co-expression Networks Elucidates Traits Associated with Common Human Diseases |
Bartonella bacilliformis is a pathogenic bacterium transmitted to humans presumably by bites of phlebotomine sand flies , infection with which results in a bi-phasic syndrome termed Carrión’s disease . After constructing a low-passage GFP-labeled strain of B . bacilliformis , we artificially infected Lutzomyia verrucarum and L . longipalpis populations , and subsequently monitored colonization of sand flies by fluorescence microscopy . Initially , colonization of the two fly species was indistinguishable , with bacteria exhibiting a high degree of motility , yet still confined to the abdominal midgut . After 48h , B . bacilliformis transitioned from bacillus-shape to a non-motile , small coccoid form and appeared to be digested along with the blood meal in both fly species . Differences in colonization patterns became evident at 72h when B . bacilliformis was observed at relatively high density outside the peritrophic membrane in the lumen of the midgut in L . verrucarum , but colonization of L . longipalpis was limited to the blood meal within the intra-peritrophic space of the abdominal midgut , and the majority of bacteria were digested along with the blood meal by day 7 . The viability of B . bacilliformis in L . longipalpis was assessed by artificially infecting , homogenizing , and plating for determination of colony-forming units in individual flies over a 13-d time course . Bacteria remained viable at relatively high density for approximately seven days , suggesting that L . longipalpis could potentially serve as a vector . The capacity of L . longipalpis to transmit viable B . bacilliformis from infected to uninfected meals was analyzed via interrupted feeds . No viable bacteria were retrieved from uninfected blood meals in these experiments . This study provides significant information toward understanding colonization of sand flies by B . bacilliformis and also demonstrates the utility of L . longipalpis as a user-friendly , live-vector model system for studying this severely neglected tropical disease .
Bartonella bacilliformis , the focus of this study , is the bacterial agent of a potentially life-threatening bi-phasic disease referred to as Carrión’s disease . Known since pre-Incan time , estimates of human fatalities caused by B . bacilliformis are >100 , 000 [1] . Since the first known epidemics in 1871 , this neglected tropical disease continues to impose significant morbidity and mortality on South Americans; approximately 1 . 7 million of which are currently estimated to be at risk [2] . The B . bacilliformis ecotype [3 , 4] is limited to South American humans and select species of sand flies living in the Andes Mountains of Peru , Ecuador and Colombia . Because a nonhuman reservoir has not been identified , our current understanding is that B . bacilliformis is transmitted among humans by phlebotomine sand flies . Bartonella bacilliformis proliferates in the human bloodstream by invading erythrocytes , and 2–8 weeks following the bite of an infected sand fly , resulting in an illness characterized by hemolytic anemia and concurrent Oroya Fever with fatality rates as high as 90% [1] . If an individual survives this primary anemic phase , verruga peruana ( VP ) or Peruvian warts may develop . These verrucous skin eruptions result from B . bacilliformis’ capacity to invade vascular endothelial cells and stimulate angiogenesis [5 , 6] [7] . VP are hemangioma-like cutaneous microcolonies , histologically indistinguishable from bacillary angiomatosis ( BA ) lesions of individuals infected with Bartonella henselae or Bartonella quintana [8] . Phlebotomine sand flies are responsible for transmitting a number of pathogens causing diseases in humans , including leishmaniasis , viral encephalitis and Carrión’s disease . “Sand fly” is a term derived from the tan or sandy color of the wings of many of the >800 species described to date [9] . In general , the sand fly life cycle is rather long , requiring 45–80 d to complete all developmental stages , including egg , four larval instars , pupa and adult . Plant nectar likely provides a significant source of nutrients and water for both male and female flies , but only females are hematophagous [10] . It is well established that L . longipalpis transmits Leishmania to humans , yet for unknown reasons this species does not seem capable of serving as a vector of B . bacilliformis . This hypothesis is based largely on the apparent lack of B . bacilliformis in naturally-occurring populations of L . longipalpis or any other arthropod sampled in endemic areas . For example , Noguchi’s group ( late 1920’s ) tested a large number of arthropods from endemic ‘verruga zones’ by homogenization , injection into animals , and subsequent isolation . Largely based on these results , L . verrucarum , Lutzomyia peruensis and Lutzomyia noguchi are considered competent sand fly species associated with B . bacilliformis transmission to humans [11] . “Vector specificity” is a term used to describe the phenomenon where one species of arthropod such as L . verrucarum is capable of transmitting a particular pathogen , like B . bacilliformis , and another closely-related species inhabiting the same ecotype such as L . longipalpis is not [12] . Thus , we asked if the apparent vector specificity of B . bacilliformis exhibited between L . verrucarum ( competent ) and L . longipalpis ( non-competent ) was the result of each fly’s blood meal preference or whether a developmental relationship between pathogen and competent vector has evolved . In this study , we analyzed several aspects of B . bacilliformis’ interactions with L . verrucarum and L . longipalpis sand fly species . First , we transformed a low-passage B . bacilliformis isolate to synthesize green fluorescent protein ( GFP ) . Second , we used this GFP+ strain to artificially infect L . verrucarum and L . longipalpis sand flies in order to observe bacterial colonization of both species over a 14-d time course by fluorescence microscopy . Third , we artificially infected L . longipalpis and assessed the viability of B . bacilliformis in adult flies , eggs , feces and diuretic fluid . Finally , we assessed the capacity of L . longipalpis to transmit B . bacilliformis between “infected” and “un-infected” artificial blood feeders . This is the first report analyzing B . bacilliformis colonization of L . longipalpis , subsequent viability and potential for transmission by this sand fly species .
All experiments involving animals were approved by the University of Montana Institutional Animal Care and Use Committee under protocol number 063-12-MMDBS-010213 . The specific regulations to which this animal care and use protocol adhered was The National Resource Council’s Guide for Care and Use of Laboratory Animals ( 8th edition ) . The University of Montana is also accredited by AAALAC with PHS assurance and is currently registered with the USDA . Bartonella bacilliformis was cultivated as previously described for B . quintana except the incubation temperature was 30°C and 5% CO2 was omitted [13] . Briefly , heart infusion broth + sheep blood ( HIB-B ) plates consisted of heart infusion broth containing 4% sheep blood , 2% sheep serum and 1 . 5% agar . When necessary , HIB-B was supplemented with 25 μg/ml kanamycin ( HIB-B+K ) . Bartonella bacilliformis was routinely cultured 5–7 d , harvested using a flat razor , washed ( 3 times with PBS , pH7 . 4 ) and added to freshly prepared human blood ( below ) . Bartonella bacilliformis [strain San Pedro] was transformed by electroporation with pJMB-GFP as previously described [14] . The resulting low-passage GFP+ strain of B . bacilliformis was used for all experiments in the study , and for simplicity will be referred to as B . bacilliformis throughout the text . All manipulations were performed in a dedicated sand fly insectary equipped with an air pressurized vestibule entry room ( University of Montana ) . The L . longipalpis strain , which originated from Jacobina , Brazil , was obtained from an existing colony maintained at the Walter Reed Army Institute of Research ( WRAIR , BEI Resources , Catalog No . NR-44001 ) . One of the authors ( PGL ) initially isolated the L . verrucarum strain used in the study from case sites in the vicinity of Caraz , Ancash Department , Peru , and subsequently established a colony at WRAIR . To our knowledge , this is the only laboratory colony of L . verrucarum currently in existence . Lutzomyia longipalpis is much more amenable to mass rearing and manipulation in the laboratory than is L . verrucarrum , which requires a lower rearing temperature , has a longer generation time and lower fecundity and productivity . For this reason , L . longipalpis was used for the majority of this study as we did not want to risk sacrificing the rare L . verrucarum laboratory colony . Flies were maintained according to standard mass-rearing procedures developed and implemented at the WRAIR [15] [16] . Further details regarding maintenance and mass rearing of flies can be found in supplementary information ( S1 Data ) . Fresh human blood ( Type O+ ) was collected from one of the authors ( JMB ) using anticoagulant acid-citrate-dextrose solution B ( BD Vacutainer 364816; Becton Dickenson , Franklin Lakes , NJ ) , and immediately transferred to 40°C . Cells were separated from serum by centrifugation ( 1000xg , 5min , 4°C ) washed three times ( PBS , pH7 . 4 , 4°C ) and reconstituted with heat-inactivated serum ( 56°C , 1h ) from a Type O+ blood donor . Bartonella bacilliformis ( strain San Pedro ) harboring pJMB-GFP was cultured on HIB-B+K for 5–7 d . Bacteria and erythrocytes were enumerated by light microscopy , combined with heat-inactivated serum prepared as above ( at an MOI of ~10 bacteria per RBC ) and co-cultured at 30°C for either 3 h or 16 h . Five-d-old frozen chicks ( Layne Laboratories , Arroyo Grande , CA ) were thawed in PBS and skins carefully removed . Skins were sterilized by soaking for 1 min in 70% EtOH , rinsed three times in sterile PBS and mounted on custom-made glass membrane feeders ( Lillie Glassblowers; Atlanta GA; autoclaved ) with orthodontic rubber bands and dental wax . After testing the membrane for leaks using PBS , the blood meal was loaded into the glass feeder , and the feeder sealed with parafilm , warmed with a circulating water bath ( 39°C ) and offered to flies housed in 473-ml experimental feeding cups [Neptune paper cans ( WL Enterprises Inc , Fort Lee , NJ ) ] modified in two ways . Feeding cups were prepared as follows: First , a 2 . 45-cm diameter aspirator access door was covered with a double-layer of dental dam ( Coltene Whaledent; Cuyahoga Falls , OH ) and was taped to the cup with strapping tape . Second , cup lids were replaced with CM and held in place with strapping tape allowing flies access through the fabric to soaked cotton balls as well as artificial blood meals . Flies in experimental cups were stored in polypropylene boxes as described above for larval pots ( 25°C; 80–90% RH ) . Starvation of flies for 24 h increased feeding activity and sucrose-soaked cotton was replaced with water-soaked cotton 16h prior to ( and during ) artificial blood meals . After feeding on artificial blood meals , flies were fed a 10% sucrose solution supplemented with 40 mg/ml kanamycin ( by cotton ) and stored as above ( 25°C; 80–90% RH ) . At 16–32h following the blood meal , flies that had obviously imbibed blood ( distinguished by dark color of abdomen; typically ~33–50% of the population ) were transferred to new experimental cups for further analysis . As an additional safety measure , cups containing infected flies were stored in polypropylene boxes and sealed in air-tight clear polyethylene bags ( Ziploc; C . Johnson & Son , Racine , WI ) . Infected blood meals were prepared and individually fed to small populations ( ~150 females ) of L . verrucarum and L . longipalpis . At 1 , 2 , 3 , 5 , 7 , and 14 d ( L . verrucarum only at 14d ) following ingestion of infected blood meals , three individual flies of each species were dissected and analyzed for the presence of B . bacilliformis using UV-fluorescence microscopy . In addition to the digestive tract , all other tissues and organs of the flies were examined , including hemolymph , salivary glands , mouthparts and crop . Eggs , feces , and diuretic fluid generated by the infected flies were also analyzed for B . bacilliformis by fluorescence microscopy . Blood-fed females were transferred by mouth aspirator ( JW Hock; Gainesville , FL ) from experimental cups to sterile , 15-ml screw-cap conical tubes containing water and one drop of liquid dish soap . Flies were gently puffed from the aspirator into the tube and were immobilized in the bubbles . Tubes containing infected flies were capped , gently inverted and poured onto a 10-cm2 cloth mesh ( CM ) suspended over a standard Petri dish . Soap was rinsed away by moving the CM to Petri plates containing water or 1X PBS . As an additional safety precaution , all manipulations of living , infected flies were performed in a custom-made glove box within the insectary . Subsequent manipulation and dissection were accomplished using a stereoscopic microscope ( StereoZoom; Bausch & Lomb ) and wooden applicator sticks ( 15 cm long , 0 . 32 cm dia . ) tipped with stainless steel entomology needles ( Minuten Nadeln , BioQuip , Rancho Dominguez , CA ) . After transferring individual flies to a 50-μl drop of PBS on a microscope slide ( Fig 1C ) , one needle was used to hold the fly ( by piercing thorax ) , and the other was used to remove the head and pull the entire alimentary tract and attached organs away from the exoskeleton . This structure was subsequently transferred to a 20-μl drop of PBS on a separate microscope slide and then covered with a coverslip . The gut contents were examined using a microscope ( BX51; Olympus , Center Valley PA ) equipped with a fluorescence illuminator ( X-Cite 120Q; Excelitas Technologies , Waltham MA ) and a cooled digital color camera ( DP72; Olympus ) with accompanying acquisition software ( DP2-BSW; Olympus ) . Four groups of 200 female flies each were transferred from adult cages to experimental cups using a custom , vacuum-assisted transfer device . Cups contained flies of known ages; two cups contained ‘young’ flies ( 2-10-d-old ) and the other two contained ‘old’ flies ( 11-25-d-old ) . An infected blood meal was prepared as described above ( i . e . , bacteria and blood co-cultured for 16h at 30°C ) , loaded into four individual glass feeders fitted with sterilized chick-skin membranes and warmed with circulating water ( 15 min at 39°C ) . Cups containing flies were placed under each of the four identical feeders loaded with infected blood meals and flies were allowed to feed ( 3 h , 80–90% RH ) . Cups containing infected flies were then stored ( 32 h , 25°C; 80–90% RH ) to allow for diuresis and hardening of peritrophic membranes , after which flies from each group that had imbibed blood were transferred to new experimental cups ( one cup per group ) and stored for further examination . At days 3 , 5 , 7 , 9 , 11 , and 13 following ingestion of an infected blood meal , individual flies from ‘old’ and ‘young’ populations were examined for viable B . bacilliformis colony forming units ( CFUs ) . At each time point , five flies were transferred from each age group to two tubes containing PBS ( with dish soap as above ) . Each tube of infected flies was gently inverted and contents poured into a sterile cell strainer ( 70 μM , #352350; BD Falcon , Bedford , MA ) and placed in a sterile Petri plate . In general , surface sterilization of the infected flies was accomplished as previously described [17] . Cell strainers containing flies were then transferred to 15-ml sterilization solution ( 70% ethanol , 0 . 05% sodium hypochlorite ) swirled in a Petri dish on a level bench top ( 45 s ) , washed ( 5 ml PBS; 3 times for 1 min each ) , and plated as follows: At each time point , eight surface-sterilized flies ( four from each age group ) were individually transferred to 1 . 5-ml microcentrifuge tubes ( each containing 25 μl PBS ) , homogenized with sterile pestles ( Argos Technologies; Elgin , IL ) , serially diluted in heart infusion broth and plated on HIB-B+K . Eggs , feces , and diuretic fluids generated by infected flies were also examined for B . bacilliformis . At 8-10d following infection , eggs , feces and diuretic fluids were collected from experimental cups , serially diluted and plated onto HIB-B+K . Specifically , a micropipettor and 10-μl aliquots of PBS were used to randomly collect four groups of five eggs each that were surface-sterilized , washed , homogenized , diluted and plated as above for adult females . Diuretic fluid and feces generated by L . longipalpis following the blood meal were also sampled for viable bacteria . Four batches of five piles each of randomly collected feces and four batches of five dried diuresis droplets each were independently collected using a micropipettor and aliquots of PBS . These samples were serially diluted ( HIB ) and plated ( HIB-B+K ) . Following a 30-d incubation at 30°C , B . bacilliformis CFUs were counted . Two groups of female flies were transferred from adult cages to experimental cups using a vacuum aspirator . Two to five-d-old flies were used in this transmission study , and cups contained different numbers , either 50 or 150 female L . longipalpis . These two groups of flies were starved overnight and then individually assessed for their capacity to transmit viable B . bacilliformis from an infected human blood meal to an uninfected meal in the membrane feeder . Three blood meals were prepared as above except only one contained B . bacilliformis ( ~6x106 bacteria per ml ) . Each group of flies was first offered the infected blood meal and then offered an uninfected blood meal specific to each group . Infected and uninfected artificial meals were offered in three successive trials of 5 , 10 , and 15 min each . For example , the group of 50 flies was offered an infected blood meal for 5 min and then this group of flies was moved to an uninfected meal for 5 min . This same sequential ( infected then uninfected ) method was repeated two additional times for durations of 10 min and then 15 min with the group of 50 flies . The group of 150 flies was also independently fed this same infected meal in an identical manner and a separate , uninfected blood meal was used for this group . Contents of both uninfected feeders were then plated and cultured as above . Following a 30-d incubation at 30°C , B . bacilliformis CFUs were enumerated .
In initial pilot experiments , we observed significant differences in colonization results between flies fed meals that had been co-cultured for 3h or 16h . In both species of sand fly , colonization was significantly lower in flies fed 3-h co-cultures as compared to 16 h . Flies examined by fluorescence microscopy at 3 and 5 d post-blood meal had relatively little GFP signal when fed 3-h co-cultures , whereas flies of both species that had imbibed 16-h co-cultured blood meals had obvious colonization that was of markedly higher density . For this reason , 16-h co-cultures were used for the remainder of the study . Microscopic examination of sand fly tissues included salivary glands , haemocoel , mouth parts , eggs , feces , and diuretic fluids and were performed over a 14-d time course . At 24h post-blood meal , B . bacilliformis was found highly concentrated in the abdominal midgut ( AM ) of both L . verrucarum and L . longipalpis ( Fig 2 ) . Bacteria were confined to this region of the digestive tract by the stomodeal and pyloric valves and were enveloped by the peritrophic membrane ( PM ) , where all bacteria were held in the lumen of the AM . Bacteria were not found in any other tissue of either fly species at this time point . At 24h post-blood meal , bacteria were highly motile and bacillus-shaped , measuring approximately 0 . 5–1 um wide and 1–3 um long . Although bacteria demonstrated a marked degree of motility in the AM of L . verrucarum ( S1 Video ) and L . longipalpis ( S2 Video ) , B . bacilliformis appeared to adhere to the internal surface of the PM of both sand fly species . At 24h following an infected blood meal , differences in bacterial colonization of L . verrucarum and L . longipalpis flies were indistinguishable . At 48h post-blood meal , bacteria appeared more numerous ( as compared to the 24-h time point ) and had changed their morphology to smaller coccoid forms of approximately 1 μm in diameter ( Fig 3 ) . On rare occasions , bacteria were observed on the apparent external side of the PM and one of the L . verrucarum sampled at this time point showed a small number of bacteria in the esophagus . In addition to changing shape , motility was significantly decreased and bacteria appeared adherent to the inner wall of the PM ( S3 Video ) . At 48h , colonization of both fly species by B . bacilliformis appeared the same . At 72h post-blood meal , digestion of erythrocytes in the gut was evident and appeared to coincide with bacterial transition into coccoid forms . At this time point , a significant reduction in the overall density of B . bacilliformis per fly was observed , concurrent with erythrocyte digestion by both L . verrucarum and L . longipalpis . Although colonization of both sand fly species exhibited reduction in bacterial density at the 72-h time point , B . bacilliformis appeared at relatively high densities in locations beyond the abdominal midgut in L . verrucarum such as the thoracic midgut and ileum ( Fig 4A–4D ) . In general , this was not the case for L . longipalpis at 72 h post-blood meal ( Fig 4E–4H ) . Although bacteria were occasionally observed beyond the AM , densities were relatively lower and the decreased size suggested the bacteria may not have been viable . Thus , at 72 h post-blood meal , a difference was observed between the competent and non-competent vectors of B . bacilliformis At 5 d post-blood meal , the L . verrucarum digestive tract continued to be colonized by B . bacilliformis beyond the boundary of the AM ( Fig 5 ) . Colonization of L . longipalpis examined at this time point exhibited a significant decrease in density , where minute GFP signals , and questionably viable bacteria , were observed . One example of a questionable signal , however , was observed in the cibarium of L . longipalpis at 7 d post-blood meal ( Fig 6 ) . Thus , by 7d post-blood meal , B . bacilliformis was apparently cleared from L . longipalpis , whereas infection of L . verrucarum was quite apparent . At 14 d post-blood meal , three L . verrucarum were analyzed and one of the flies had an obvious infection of the anterior and thoracic midgut ( TM; Fig 7A–7D ) . Fly 2 had one GFP+ signal in its ilieum , and the third fly had no signal whatsoever . Overall , microscopic analyses comparing competent and non-competent sand fly vectors suggest that L . longipalpis eliminates B . bacilliformis , whereas in L . verrucarum , bacteria survive blood meal digestion , migrate beyond the AM , and colonize the sand fly’s entire digestive tract . We were unable to observe B . bacillliformis outside the digestive tract lumen . We were also unable to visualize GFP-expressing bacteria in the feces or diuretic fluid of L . longipalpis . Unfortunately , we were unable to corroborate the feces and diuretic fluid data with L . verrucarum due to limited availability of this sand fly species . In this experiment , we analyzed the viability of B . bacilliformis in both young ( 2–10 d ) and old ( 11–25 d ) sand flies and were able to make several observations ( Fig 8 ) . First , B . bacilliformis remained viable in the non-competent vector for up to 11d post-infection . Second , older flies appeared to maintain a higher density of bacteria over time compared to younger flies . Third , by 13 d post-infection , viable B . bacilliformis were no longer found in L . longipalpis . We were curious whether L . longipalpis could transmit viable B . bacilliformis from an artificially-infected blood meal offered through a chick-skin membrane on a glass feeder to an uninfected blood meal offered through a chick-skin membrane on an identical feeder simply by means of contaminated mouthparts . However , groups of 50 and 150 sand flies were unable to transmit bacteria from the infected blood meal to the uninfected blood meal , suggesting L . longipalpis was unable to transmit viable B . bacilliformis by mechanical means . Hertig examined wild-caught L . verrucarum by light microscopy , and cultured B . bacilliformis from proboscis homogenates [18] . In addition , he frequently observed ‘massive infections of the sand fly proboscis with unidentified microorganisms’ , and termed these ‘x-prob’ . He was unable to culture ‘x-prob’ . In our study , we frequently observed something similar to ‘x-prob’ on the mouthparts of laboratory-reared L . verrucarum as well as L . longipalpis ( S1 Fig ) . However , we were unable to observe GFP signal on mouth parts of infected flies at any point over the 14-d time course . This indicates that ‘x-prob’ is distinct from B . bacilliformis . Thus , we were curious if these unknown bodies contained DNA , and were actually microorganisms . We collected 10–12 d-old L . longipalpis that had not been exposed to B . bacilliformis and attempted to stain putative ‘x-prob’ with common nucleic acid stains ( e . g . , ethidium bromide , SYTO9 and propidium iodide ) without success ( S1D and S1E Fig ) . Given the lack of DNA and striking resemblance between these structures and eye pigmentation granules , we hypothesize that they are not bacteria but rather pigment granules from omatidia in the compound eyes .
This study represents the first microscopic characterization of B . bacilliformis colonizing the midgut of Lutzomyia sand flies since 1942 . We used fluorescence microscopy to chronicle colonization of L . verrucarum and L . longipalpis by using a low-passage GFP-expressing B . bacilliformis isolate over a 14-d time course . Our results suggest that B . bacilliformis infects both species of sand fly , and colonization is limited to the lumen of the digestive tract . As infection progresses , bacteria appear to change morphology from a highly motile bacillus into a relatively smaller , non-motile coccoid form . A similar phenomenon was previously described for B . bacilliformis cultured in vitro; wherein the bacterium changed from a motile bacillary form into a non-motile coccoid form upon entering stationary phase [19] . When examined microscopically , differences in the course of colonization between sand fly species were indistinguishable up to 72h following an infected blood meal . At this time point , B . bacilliformis appeared to transform into small coccoid forms , that by 7d post-blood meal were nearly absent from L . longipalpis . However , at 72h post-blood meal , B . bacilliformis appeared at relatively high density in regions of the gastrointestinal track beyond the abdominal midgut and persisted for >14d as small coccoid forms . Although bacteria exhibited a significant degree of motility at 24h post-infection , by 48 h-post infection B . bacilliformis appeared to bind to the peritrophic membrane and were immobile in both fly species . Although a small number of bacteria were observed outside the PM , it is unclear whether this occurred prior to or after formation of the PM . As mentioned , Carrión’s disease is not a zoonosis , but rather an anthroponosis , as a non-human animal reservoir of B . bacilliformis has never been identified . Transovarial transmission is a phenomenon in which a pathogen is maintained in nature by arthropods , where eggs are infected and subsequently maintained in the vector until the adult stage , when transmission to human occurs . Transovarial transmission of Rio Grande Virus [20] is known to occur in Lutzomyia and we were curious if this could occur with B . bacilliformis . We therefore examined eggs ( n >100 ) of both species inside the fly and following oviposition , but were unable to observe GFP+ bacteria in the eggs of either fly species . Furthermore , eggs collected from infected L . longipalpis , were cultured on HIB-B+Kan plates but viable Bartonella CFUs were never isolated . These results suggest that maintenance of B . bacilliformis in nature is not facilitated by transovarial transmission . At dusk , humans enter dwellings to sleep and sand flies become active and seek blood . Humans generally feel pain associated with the ‘bite’ of a sand fly ( which mechanically is a pierce and cut rather than a ‘bite’ ) and attempt to slap the affected area . This reaction either results in the fly’s escape ( to seek more blood ) or its demise at or near the bite region on the surface of skin . A phenomenon referred to as ‘mechanical transmission’ or early phase transmission ( EPT ) [21] is one in which arthropod mouthparts are initially contaminated , by briefly obtaining small amounts of blood from an infected human , and then finishing its meal on another human . Our interrupted blood meal EPT experiments with B . bacilliformis by relatively large populations of L . longipalpis suggest that mechanical transmission by this species does not occur . Whether EPT occurs during a bite with contaminated L . verrucarum is unknown but would likely prove interesting . Noguchi examined a wide variety of arthropods from the verruga zone ( e . g . , ticks , mites , midges , lice , fleas , bedbugs , buffalo gnats , mosquitoes , horse flies , sheep ticks , and three species of sand fly ) that were collected without the use of chemicals and shipped to Rockefeller Institute [11] . Each arthropod was subsequently crushed in saline and injected into rhesus monkeys . The only samples that generated a B . bacilliformis bacteremia were of L . noguchii and L . verrucarum , while samples of L . peruensis or of any of the other arthropods generated no infection . Battistini collected live infected sand flies from the verruga zone that were released into a screened cage containing rhesus monkeys and B . bacilliformis was subsequently isolated from the blood of these animals [22] . This was apparently the first demonstration of B . bacilliformis transmission by sand flies . It is unclear whether transmission occurred from ‘biting’ or some other means . It is well accepted that Bartonella quintana transmission to humans is primarily a result of the infected feces of Pediculus humanus ( human body lice ) being scratched into a cutaneous bite . The resulting infection causes trench fever [23] . In 1937 , Hertig described an experiment wherein wild L . verrucarum were fed on patients that were bacteremic and 75% of 90 flies contained B . bacilliformis in their guts when later examined [24] . Interestingly , fecal matter obtained from these infected sand flies contained a large number of B . bacilliformis , whereas diuretic fluid did not . Unfortunately , we were unable to analyze feces or diuretic fluid of L . verrucarum to any great extent due to limited numbers of this sand fly species . However , samples of L . longipalpis feces and diuretic fluid examined were devoid of any visible GFP signal or viable bacteria . Finally , Hertig attached small cages containing infected sand flies to Rhesus monkeys and , although transmission to animals occurred , he mentioned that it may have been by some other means than direct inoculation via mouthparts [18] . We have demonstrated that large groups of L . longipalpis do not appear to mechanically transfer viable bacteria as a ‘flying needle’ , but because B . bacilliformis remains viable for up to seven days , this species may be capable of transmission by other means . In conclusion , our results characterize colonization of the sand fly midgut by B . bacilliformis as well as demonstrate a distinct pattern regarding colonization of competent and non-competent arthropod species . We have shown L . longipalpis to be a user-friendly , live-vector/host model system , which we currently use to further characterize site-specific mutants of B . bacilliformis in Lutzomyia . Identification of factors used by pathogens to colonize and persist in arthropod vectors may lead to the development of transmission-blocking vaccines [25] [26] for prevention of this severely neglected tropical disease . | In general , Bartonella infections are zoonooses and are transmitted to humans by hematophagous ( blood eating ) arthropods such as fleas , lice , ticks , mites and sand flies . Bartonella bacilliformis is found only in Andean regions of South America and is transmitted to humans by a few select species of sand flies , resulting in Carrión’s disease . Our current understanding is that Carrión’s disease is not a zoonosis , as an animal reservoir has not been identified . In this study , we analyzed B . bacilliformis colonization in two species of sand flies in an attempt to understand why Lutzomyia verrucarum is a competent vector and Lutzomyia longipalpis is incapable of transmitting this pathogen between humans . We found that B . bacilliformis remains in the abdominal midgut of the non-competent vector and is progressively digested until no viable bacteria remain ( 7d ) . Furthermore , large groups of L . longipalpis were used to demonstrate that this non-competent species is incapable of mechanically transmitting viable bacteria between artificial blood meals simply by contaminated mouthparts . In L . verrucarum , B . bacilliformis colonizes the lumen of the digestive tract beyond the intra-peritrophic space in large numbers and persists ( >14d ) . | [
"Abstract",
"Introduction",
"Materials",
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"Results",
"Discussion"
] | [] | 2015 | Colonization of Lutzomyia verrucarum and Lutzomyia longipalpis Sand Flies (Diptera: Psychodidae) by Bartonella bacilliformis, the Etiologic Agent of Carrión’s Disease |
Viroporins like influenza A virus M2 , hepatitis C virus p7 , HIV-1 Vpu and picornavirus 2B associate with host membranes , and create hydrophilic corridors , which are critical for viral entry , replication and egress . The 6K proteins from alphaviruses are conjectured to be viroporins , essential during egress of progeny viruses from host membranes , although the analogue in Chikungunya Virus ( CHIKV ) remains relatively uncharacterized . Using a combination of electrophysiology , confocal and electron microscopy , and molecular dynamics simulations we show for the first time that CHIKV 6K is an ion channel forming protein that primarily associates with endoplasmic reticulum ( ER ) membranes . The ion channel activity of 6K can be inhibited by amantadine , an antiviral developed against the M2 protein of Influenza A virus; and CHIKV infection of cultured cells can be effectively inhibited in presence of this drug . Our study provides crucial mechanistic insights into the functionality of 6K during CHIKV-host interaction and suggests that 6K is a potential therapeutic drug target , with amantadine and its derivatives being strong candidates for further development .
Chikungunya fever is a severe and debilitating illness caused by the mosquito-borne arbovirus , Chikungunya Virus ( CHIKV ) [1–4] . Infections are generally non-fatal , but this virus has been much in the limelight lately , due to its rapid spread and outbreaks worldwide [2–4] . Although , India is endemic for CHIKV; outbreaks do occur , the latest one reported in New Delhi in 2016 [4] . Currently , there are no specific therapeutics against CHIKV infections , the treatment being primarily symptomatic [1] . CHIKV , like other alphaviruses , is an enveloped RNA virus with particle diameter ranging between 65–70 nm [5] . The viral genome is organized into structural and non-structural protein-encoding regions [5] . The structural protein cassette is composed of glycoproteins ( E1-E2-E3 ) , capsid protein ( C ) , and 6K , which was recently shown to have a transframe variant ( TF ) [6 , 7] . 240 copies each of glycoproteins E1 and E2; as well as the capsid protein , are arranged in accordance with T = 4 symmetry [8] . Although the roles of capsid and envelope proteins in the life cycle of CHIKV are fairly well studied [9–14] , reports pertaining to the direct functional characterization of 6K are rare , making it the least understood amongst all CHIKV structural proteins . A recent report suggests that 6K is a prime target for mounting CTL- mediated immune response in the host , indicating its significance as a therapeutic target [15] . One contributing factor for the lack of direct biochemical characterization of CHIKV 6K is its extreme intrinsic hydrophobicity , as well as cytotoxicity [7 , 16] , which makes the production of sufficient quantity of functionally active , recombinant protein fairly challenging . In fact , the majority of the studies on 6K from other alphaviruses—demonstrating the biochemical properties and role in viral life cycle—have been carried out by analysis of mutated virus [17–23] or by RNA expression at the cellular level [7 , 24] , with only a few studies characterizing recombinant 6K [16 , 25] . Further , the molecular details of membrane association by 6K and the exact role of this activity in promoting virus budding remains unknown . Recently , a transframe ( TF ) variant of 6K was identified , which is generated from the 6K gene as a result of a ( -1 ) ribosomal frameshift . TF has the same N-terminal domain as 6K , but a different C-terminus [6 , 7] . 6K and TF are produced during infections by most alphaviruses , and both are thought to be essential for virus budding , although only TF appears to be packaged within virions while 6K is probably retained at the membranes of infected cells [6 , 7] . Additionally , studies with SINV have highlighted the role of palmitoylation for the localization of TF to the plasma membrane [26] . The membrane permeabilization activity of alphavirus 6K places it in the category of viroporins . Nucleation of aqueous passageways by viroporins allows movement of ions and small molecules , which in turn , facilitates virus entry , replication , and egress . Some well-characterized members of the viroporin family include M2 of influenza A virus , Vpu of HIV , p7 of HCV , and 2B of picornaviruses [27] . The role of viroporins in sustaining infections , and their significance as targets for drug development , is illustrated by the antiviral activity of amantadine , which targets the ion-channel forming protein M2 and prevents Influenza A infections [28] . Given the recent scenario of frequent outbreaks of Chikungunya fever in different parts of the world [1–4] , and the role of 6K in supporting alphavirus infections [17–23] , we attempted to functionally characterize CHIKV 6K and assess its potential as a target for drug development . Using a combination of electrophysiology , cryo-electron microscopy , biophysical techniques , and molecular dynamics simulations , we demonstrate that CHIKV 6K interacts with membranes in multifaceted ways , leading to permeability as well as vesicle fusion . We show that CHIKV 6K exists in oligomeric forms , and forms ion channels in membranes . In addition , we demonstrate that virus-like particles ( VLPs ) of CHIKV show a marked deviation from their usual morphology when treated with amantadine; and that the inhibitory activity of amantadine extends to CHIKV replication in cell culture . Thus , it can be strongly emphasized that 6K is critical in CHIKV biology and is a potential therapeutic target for the treatment of Chikungunya fever .
The 6K proteins of alphaviruses are extremely hydrophobic in nature with more than 50% of the residues having a positive value on the hydrophobicity scale ( Fig 1A ) . A GRAVY ( grand average of hydropathy ) value of 1 . 006 also indicates that the protein has an overall hydrophobic nature . This , as well as the presence of transmembrane regions predicted by several servers ( Fig 1B ) , were likely responsible for our inability to generate CHIKV 6K alone , in soluble form , by recombinant expression in bacteria . A fusion of CHIKV 6K with GST ( Glutathione S Transferase ) was successfully expressed and purified from E . coli Rosetta ( pLysS cells ) . We found that IPTG induction and subsequent bacterial growth at a relatively lower temperature of 18°C , is an essential requirement for maximizing expression . GST-6K was initially purified by pulldown with GST beads ( Fig 1C ) , followed by size-exclusion chromatography , which generated two separate peaks corresponding to GST and GST-6K ( Fig 1D ) . The GST-6K peak was fairly broad and the molecular weight corresponding to the primary peak fraction ( Fig 1D , indicated with an arrow ) was calculated to be 190 . 98 kDa , which indicated the formation of a hexamer ( monomer MW = 33 . 5 KDa ) . Since the recombinant protein appeared to have the propensity to break into its constituent parts ( Fig 1D ) , GST-6K was utilized within 24–48 hours post purification for every experiment . The ability of GST-6K to form ion channels in vitro was investigated using an electrophysiology setup as described in Materials and Methods . The ion channel activity of GST-6K was measured as a flow of current across an otherwise intact DPhPC membrane ( Fig 2A and 2B ) . Fig 2A and 2B ( i , iii , v and vii ) represent the current versus time traces of GST-6K incorporated into the lipid bilayer membrane at applied membrane potentials of -100 mV and +100 mV respectively . The corresponding all point histograms are shown in Fig 2A and 2B ( ii , iv , vi and viii ) . Addition of GST-6K to the membrane caused spikes in the current trace , which displayed the typical stepped nature of the opening and closing of an ion channel . This showed that GST-6K successfully incorporated within the bilayer and formed ion-conducting , stable channels , which were functional under the aforementioned membrane potentials , and opened at both negative and positive voltages . Altogether , observations were made at four different protein concentrations of 0 . 2 , 0 . 85 , 1 . 2 and 2 . 6 ng/μl . The lipid ( DPhPC ) concentration remained fixed in all cases , as the formation of bilayer membrane in the BLM cup demands an exact amount of lipid . Increasing concentration of GST-6K coincided with the appearance of multiple open states with higher currents , at both positive and negative membrane potentials . These observations can be best explained as follows . GST-6K forms pore on the membrane by oligomerization ( S1 Fig ) , however , the number of monomers which oligomerize to form a pore is not fixed . Increase in protein concentration may result in the generation of higher-order oligomers , leading to the formation of bigger pores in the membranes and higher currents ( S1 Table ) . Generation of multiple oligomeric states is well evidenced in pore-forming proteins [29] . Similar experiments with only GST as negative control do not result in the formation of ion channels , as seen by us ( S2 Fig ) and others [30] . This indicates that the ion-channel forming activity is exclusively due to 6K . Since GST-6K appeared to be capable of forming ion-channels in membranes , we attempted to determine if it preferentially damages target membranes . Liposomes mimicking the lipid composition of ER and plasma membranes encapsulating the fluorescent dye sulforhodamine B , were generated as described [31 , 32] . The ability of GST-6K , at a concentration ranging from 0 . 1 μM-10 μM , to disrupt these liposomes , was tested using standard methods . A distinct increase in disruption of ER mimicking liposomes was observed with concentrations of 0 . 5 μM ( and higher ) of GST-6K ( Fig 3A ) . Transmission electron micrographs of GST-6K treated , vs non-treated ER-mimicking liposomes , displayed a clear difference in morphology ( Fig 3B and 3C ) , with the latter image ( Fig 3C ) showing liposomes with their surfaces decorated with proteinaceous material . Incubation of plasma membrane mimicking liposomes with GST-6K did not cause any significant release of dye ( Fig 3A ) , or alteration of surface features ( Fig 3D and 3E ) , indicating the inability of GST-6K to effectively rupture these membranes . To further pinpoint the role of specific lipids in GST-6K-mediated membrane damage , similar experiments were conducted with SulfoB-encapsulating liposomes composed of DOPC ( Dipalmitoylphosphatidylcholine ) and cholesterol in a 1:1 molar ratio . GST-6K , even at relatively higher concentrations of 5 and 10 μM , was incapable of disrupting these liposomes ( Fig 3A ) . These observations , taken together , indicated that CHIKV 6K is probably unable to damage membranes rich in cholesterol . Free , purified GST did not display any significant ability to disrupt membranes , indicating that the GST tag did not influence the effect of CHIKV 6K on membranes in any way ( Fig 3A ) . Incubation of ER-mimicking liposomes with GST-6K and subsequent visualization using transmission electron microscopy , revealed that the protein dotted the surface of liposomes ( Fig 3C ) . Manual picking of particles and 2D classification ( Fig 3F ) indicated that the morphology of the structures formed was not uniform . Thus , it appears that GST-6K may induce the formation of a range of oligomeric structural units on the surface of membranes . The inability of CHIKV 6K to cause leakage in plasma membrane specific liposomes was somewhat surprising , as the stipulated role of 6K during virus particle egress implies some association with the plasma membrane [21] . We , therefore , tested the cellular location of 6K tagged with EGFP ( Enhanced Green Fluorescent Protein ) in mammalian epithelial cells using confocal microscopy . Upon transfection of EGFP-6K into HEK ( Human Embryonic Kidney ) 293T cells , the GFP fluorescence showed a very high degree of localization with the ER , partial localization with Golgi membranes , but no localization to mitochondria , nucleus or the plasma membrane ( Fig 4A–4E ) . HEK-293T cells were also co-transfected with pCDNA3 . 1 containing either CHIKV E2 or E1 glycoproteins , along with GFP-6K . The E1 and E2 glycoproteins were expressed in conjunction with an N-terminal myc-tag , which was detected with an anti-rabbit HRP conjugated antibody ( Fig 4F and 4G ) . 6K selectively localized with E2 as opposed to E1 and traversed to the plasma membrane upon simultaneous expression with E2 ( Fig 4H ) . This data , in conjunction with previous instances of E2 trafficking to the plasma membrane by itself [33–35] , suggests that the physiological role of 6K in infected cells is quite possibly dependent on E2 , which might dictate the trafficking of 6K to the plasma membrane . In order to better understand the mode of membrane association and ion channel formation by CHIKV 6K , we carried out microsecond scale ( 1 . 5 μs ) MD simulation studies . In the absence of an experimentally derived tertiary structure for 6K , two different predicted structures - 6k1 and 6k2—were considered ( Fig 5A-i and 5B-i respectively ) . All simulations were carried out for a total of 1 . 5 μs in the presence of 100 mM concentration of NaCl under the influence of an applied electric field . When a single molecule of 6k1 was embedded within a pre-equilibrated POPC ( 1-pamitoyl-2-oleoyl-sn-glycero-3-phosphocholine ) membrane the peptide adopted an overall angular conformation with the central alpha-helical segment ( 14QQPLFWLQALIPLAALIVLCNCLR37 ) attaining an almost 15-degree tilt with respect to its initial conformation . The peptide retained this conformation for the rest of the simulation period . However , embedding 6 molecules of 6k1 in the membrane led to the formation of a small stable channel through which ions were observed to pass through ( Fig 5A-iv ) . Secondary structural analysis of the individual monomers ( S3 Fig ) showed that 6k1 primarily remains helical during simulation time , with the central alpha-helical segment of each monomer spanning through the length of the membrane , thereby providing structural stability to the overall complex . The average size of the channel was found to be ~1 nm , which is sufficient for ions and small molecules like sulforhodamine B to pass through and also corresponds closely to the channel diameter obtained from electrophysiology experiments . We utilized the “Pore Walker” server [36] to detect the residues within the channel and found that the channel lumen was primarily lined by residues Ala1 , Thr2 , Tyr3 , Glu5 , Ile24 , Pro25 , Ala28 , Leu32 and Arg37 from each monomer . Similar simulations studies with the oligomeric form ( Fig 5B-ii ) of 6k2 produced an interesting outcome ( Fig 5B-iii , iv ) . No stable channel for ion passage , unlike that observed for 6k1 oligomers , were detected in these cases ( Fig 5-iv ) . A plausible explanation for this discrepancy was provided by the difference in the orientation of the N-terminal helix in 6k1 and 6k2 conformations ( Fig 5A-i and 5B-i respectively ) . We generated two potential oligomeric arrangements with the best possible spatial placement of 6k1 and 6k2 . The 6k1 oligomer contains the N-terminal helices positioned towards the interior of the assembly ( Fig 5A-ii ) , whereas the 6k2 oligomer has the N-terminal helices projected outwards ( Fig 5B-ii ) . The hydrophobicity plot for 6K ( Fig 1A ) clearly shows that , except the small N-terminal stretch of approximately 15–20 residues , the rest of the sequence is considerably hydrophobic . Thus , as the simulation progressed , the inward orientation of N-terminal helices of 6k1 probably favored the stabilization of the channel , widening it enough for the passage of small molecules; whereas , for 6K2 , the cavity collapsed on itself , thus hindering the formation of a stable channel . Our simulation studies provide indications as to how CHIKV 6K forms ion channels within biological membranes and the dynamics of ion conduction and hints towards the critical role of the N-terminal residues of 6K in the formation and stabilization of the ion channel . During the visualization of GST-6K interaction with ER mimicking liposomes by transmission electron microscopy ( Fig 3C ) , the formation of a population of liposomes with larger diameter was noticed . Further studies clearly showed the occurrence of liposomal fusion ( Fig 6A ) , which was further investigated using dynamic light scattering ( DLS ) ( Fig 6B ) . First , 1 mM calcium chloride was utilized as a positive control , as calcium ions are known to facilitate fusion of artificial vesicles in vitro [37] . Addition of calcium chloride resulted in a size-shift of vesicles to larger liposomes ( Fig 6B ) . Upon exposure of ER-mimicking liposomes to 1 μM GST-6K , a distinct overall increase in the diameter of vesicles was noted , indicating the presence of larger sized structures ( Fig 6B ) . This additional , fusogenic ability of CHIKV 6K appears to be similar to that of the M2 channel protein of Influenza A virus [38] . Given these similarities between Influenza A M2 and CHIKV 6K , we attempted to check whether the membrane fusion events orchestrated by CHIKV 6K could be prevented by amantadine , a well-known inhibitor of M2 [38] . The fusion of ER-mimicking liposomes by GST-6K was entirely abrogated by 1μM amantadine ( Fig 6B ) . Addition of the same amount of amantadine also inhibited the release of fluorescent dye from ER mimicking vesicles by GST-6K to a significant proportion ( Fig 6C ) . The effect of amantadine on the ion channel formation by GST-6K was checked by electrophysiology experiments described previously at both positive and negative membrane potentials ( Fig 6D and 6E ) , addition of 1 μM amantadine to 2 . 6 ng/μl GST-6K on the bilayer ( BLM ) resulted in the formation of only closed states ( Fig 6D and 6E , iii-iv ) , while clear close and open states were detected in the absence of amantadine ( Fig 6D and 6E , i-ii ) . This clearly indicates that amantadine abolishes the ion channel activity of CHIKV 6K . It may be mentioned here that given the concentration of the protein the recordings show that 6K forms multi-channels , which are likely to be due to the formation of different oligomeric states of the protein on the BLM . To understand whether the ability of amantadine to inhibit the membrane activity of 6K translates to any effect on CHIKV particle assembly , we transfected HEK 293T cells with the cDNA encoding the entire structural protein cassette of CHIKV , followed by treatment of transfected cells with 1μM amantadine . Transfection of the structural protein cassette resulted in the formation of CHIKV VLPs , which were purified and observed using cryo-electron microscopy ( Fig 7A ) . While the majority of particles generated from untreated cells ( ~74% ) displayed the usual size and morphology of wild type CHIKV ( Fig 7A ) , particles generated from amantadine-treated cells were relatively smaller , heterogeneous , and in some cases appeared to lack lipid-associated glycoproteins ( Fig 7B ) . Upon detailed visual examination of ~1000 particles from each population , 89 . 29% of particles generated from amantadine treated cells exhibited aberrant morphology , in contrast to 24 . 79% aberrant particles generated from untreated cells ( Fig 7C ) . We postulate that the deviation of CHIKV particles from standard size and morphology is possibly due to the detrimental effect of amantadine on the membrane activity of 6K ( Fig 6D and 6E ) . Taken together , our data highlights the necessity of 6K-mediated membrane interaction for correct virus particle assembly . To check whether the inhibitory activity of amantadine on correct virus assembly extends to the infectivity of virus particles , the ability of CHIKV ( strain S 27 ) to replicate in vero cells was tested in presence and absence of amantadine . First , any cytotoxic effects of amantadine on vero cells was tested with different concentrations of the drug ( 25–200 μM ) for 24 h . MTT assay showed that while ~100% cells were viable upon being treated with 100 μM of amantadine , the cellular viability decreased to 95% upon treatment with 150 μM of the drug , and was further reduced to 88% with 200 μM of the drug ( Fig 8A ) . To check the effect of amantadine on CHIKV replication , a dose kinetics assay was performed ( Fig 8B ) , in which vero cells were either mock infected or infected with CHIKV ( MOI 0 . 1 ) and treated with different concentrations of the drug ( 2 . 5–200 μM ) . Cell culture supernatants were harvested at 18 hpi and plaque assay , as well as qRT-PCR , were carried out to estimate the virus titer . As observed in Fig 8B , around 58% reduction in virus titer was observed in samples treated with 40 μM concentration , while 77% reduction was observed at 100 μM concentration of amantadine in comparison to control . For further confirmation , qRT-PCR for CHIKV E1 gene was carried out . It was observed that the E1 gene expression was decreased significantly with increased concentration of amantadine as evident from the Ct values shown in Table 1 . Additionally , for assessing the effect of amantadine on CHIKV propagation at different time points post-infection , a growth kinetics experiment was performed ( Fig 8C ) . Vero cells infected with CHIKV ( 0 . 1 MOI ) and treated with 60 μM of amantadine , 90 minutes post infection , were harvested at 6 , 12 and 18hpi , and the harvested cell culture supernatants were processed through a plaque assay to estimate the viral titer . It was observed that there was ~38% reduction in viral titer at 6hpi and ~60% reduction at 12hpi and 18hpi as shown in Fig 8C . Taken together , the results indicate that amantadine can inhibit viral infection and shows significant anti-CHIKV effect under in vitro growth conditions .
Although different virus families have distinct mechanisms for host interaction , however , there exist notable similarities in the processes of entry , uncoating , replication , and egress . Some examples are membrane fusion for cellular entry of enveloped viruses , amphipathic peptide-mediated membrane disruption by non-enveloped viruses , and viroporin-mediated membrane alteration/remodeling for facilitating viral propagation [39–41] . These analogous steps in the virus-host interaction pathway [42 , 43] can potentially be targeted for generating broad-spectrum antivirals . This approach may ultimately be more cost-effective than engineering separate therapies against specific viral pathogens . Viroporins constitute a group of virally encoded membrane-interacting proteins that are crucial for initiating and maintaining successful viral infections [41] and consequently are suitable therapeutic targets . The 6K protein from alphaviruses , due to its ion channel forming ability , and its critical role in facilitating virus budding , has been considered a member of the viroporin family [16 , 21 , 24]; although the analogue protein from CHIKV has not been characterized functionally . Here , we show for the first time that the 6K protein from CHIKV associates into oligomers and interacts with membranes–two essential features which categorize virally encoded proteins as viroporins . Interestingly , our data show that CHIKV 6K interacts with membranes in multifaceted ways–it can induce ion channel formation , allow passage of small molecules like fluorescent dyes , and also facilitate fusion of vesicles . As this kind of complex membrane association was earlier identified for the M2 protein of Influenza A Virus , also categorized as a viroporin , we attempted to test whether an existing , FDA-approved and marketed M2 ion channel inhibitor–amantadine- can affect the functionality of CHIKV 6K . Our data showed that indeed , amantadine at a concentration of 1μM [45] was able to inhibit ion channel activity , vesicle fusion and membrane permeabilization properties of 6K in vitro . We further hypothesized that if 6K is required for correct budding of CHIKV particles from cells , the inhibition of 6K functionality by amantadine would lead to the production of virus particles with a significant degree of aberrance in morphology , similar to the phenotype earlier observed upon deletion of 6K in other alphaviruses [20 , 21] . We found that indeed , amantadine at a concentration of 1μM was sufficient to cause structural aberrations in a majority of CHIKV virus-like particles budding out of cells . This effect of amantadine on the activity of CHIKV 6K is surprising , given the lack of primary sequence similarity between 6K and M2 [44] . However , there could be similarities in the tertiary or quaternary folds of M2 and 6K , which might allow amantadine binding and is worthy of further investigation through structural studies . Given the effect of amantadine on particle morphology , we tested the possibility of utilizing amantadine as an inhibitor of CHIKV infection . Vero cells infected with CHIKV virions ( strain S 27 ) , showed a significant decrease in viral titer upon exposure to 5 μM or more amantadine ( Fig 7B ) . This concentration was higher than that required for alteration of particle morphology , which indicates that during infection , there could be other compensatory factors resulting in the generation of infectious particles , or that the morphologically altered particles do retain some infectivity . The IC50 value of amantadine was calculated to be 29 . 51 μM ( Fig 7B ) , which is comparable to that observed for other antiviral drugs [46] . Additionally , the qRT-PCR assay also confirmed that the expression of viral RNA progressively reduced with increase in amantadine concentration . Moreover , through a growth curve analysis , it was observed that the addition of amantadine inhibited CHIKV infection remarkably at later times in vitro . This strongly implies that 6K is indeed a valuable target for the development of pan-alphaviral inhibitors , using amantadine and its derivatives as the starting point . Our work also highlights interesting features of 6K localization and functionality , which are essential for understanding the mechanism of CHIKV assembly . Our liposome assays show that CHIKV 6K preferentially disrupts vesicles that mimic the lipid composition of the endoplasmic reticulum ( ~ 60% disruption ) as opposed to those that mimic the lipid profile of plasma membrane ( ~10% disruption ) . Likewise , recombinant expression of 6K in mammalian cells results in the protein being localized primarily in ER , highlighting a preference shown by the protein for the ER membrane . Indeed , it appears that enhanced cholesterol composition , as present in the plasma membrane , is a deterrent for 6K; however , existing literature conjectures that 6K forms ion channels in the plasma membrane [16] . In order to rationalize these contradictory phenomena , we hypothesized that 6K can probably traffic to the plasma membrane , only if it has a binding partner . Specifically , previous studies showing that the E2 glycoprotein of other alphaviruses interact with 6K , that any detrimental alteration in 6K hinders effective glycoprotein processing [17 , 18 , 19 , 21] , and that E2 is also capable of traversing to the plasma membrane on its own , led us to conjecture that E2 might play a role in conveying 6K to the plasma membrane . To verify this hypothesis , we carried out confocal microscopy studies of EGFP tagged 6K , in presence of E1 and/or E2 glycoproteins; and found that CHIKV 6K strongly colocalizes with E2 as opposed to E1 , and that its primary location is altered to the plasma membrane upon co-expression with E2 . It is possible that the functionality of 6K in altering ionic homeostasis at the plasma membrane is contingent upon its alliance with E2 during virus budding , and that 6K in presence of E2 utilizes the trans-golgi network from ER to reach the plasma membrane . Our data shows that CHIKV 6K has the propensity to form oligomers and can integrate with planar lipid membranes to produce ion channels . However , how these oligomeric associations are stabilized can only be answered from high resolution experimental structural data . All-atom molecular dynamics simulation studies indicate that the diameter of channels formed by 6K oligomers is in the range of 1 . 0–1 . 10 nm , which is sufficient for the passage of small ions and fluorescent dyes . A 2D classification of GST-6K particles dotting the surface of ER liposomes also indicated heterogeneity in the structure of particles formed on these liposome surfaces . A close analysis of amino acids forming the lumen of the channel identified a sizeable proportion of residues from the hydrophilic N-terminal region of individual monomers . The rest of the polypeptide , which is highly hydrophobic , appeared to provide the necessary structural anchorage required to prevent the collapse of the channel . The validity of this arrangement for channel formation is highlighted by attempted simulations with an oligomeric form of 6K , where the N-terminal regions of individual monomers extended outwards from the channel . In this particular arrangement , the central helical transmembrane segments collapsed on one another , disrupting the formation of the central channel . Recent developments in the field have indicated that the transframe or TF variant of 6K may also be involved in membrane penetration and may be packaged in virions [7]; while the fate of 6K is to remain associated with cellular membranes . Since the sequence for ribosomal frameshift is inherent in the 6K cDNA , we expect that a minor proportion of recombinant protein produced by us will also have an altered C-terminus . However , this alteration will consist of incorporation of an expression-vector derived stretch of 7 residues , which is entirely different from the actual C-terminal region of TF derived from the CHIKV genome . This minor population of recombinantly generated variant is therefore not expected to have similar functionality as viral genome generated and virus-incorporated TF . In the absence of detailed structural and functional data on 6K and TF , it is at this point impossible to comment on the possibly separate roles played by these components in the life cycle of alphaviruses . Our work highlights the biophysical characteristics and functionalities of CHIKV 6K that are analogous to those displayed by viroporins and attempts to fundamentally characterize its cellular localization and multifaceted membrane interacting abilities . We hope that given the requirement for 6K for the propagation of the important human pathogen CHIKV , efforts will be made to utilize 6K as a drug target to develop therapeutic strategies in the future .
Hydrophobicity plots corresponding to CHIKV 6K sequence , was generated using the ExPASy tool ProtScale ( https://web . expasy . org/protscale/ ) , and probable transmembrane domains were identified using DAS [47] , TMHMM [48] , TOPCONS [49] and PHOBIUS [50] . The cDNA corresponding to CHIKV 6K was obtained from a mammalian expression cassette encoding all structural proteins corresponding to CHIKV strain 37997 ( CMV/R CHIKVC-E3-E2-6K-E1 ) [51] . This cassette was a kind gift from Dr . John Mascola , NIAID ( National Institute of Allergy and Infectious Diseases , USA ) . The cDNA corresponding to 6K was subcloned into the BamHI and XhoI sites of the bacterial expression vector pGEX-6P2 ( GE Healthcare ) , thus generating a construct with an N-terminal GST-tag . For mammalian expression , 6K cDNA was subcloned into the NheI and HindIII sites of pEGFP-N1 ( Clontech ) , resulting in the insertion of an N-terminal EGFP tag . CHIKV glycoproteins E1 and E2 were subcloned into the BamHI/XbaI and EcoRI/XbaI sites , respectively , of pCDNA 3 . 1 ( + ) ( Invitrogen ) with N-terminal myc-tag . All constructs were confirmed by sequencing . Expression of GST-6K was induced in E . coli Rosetta pLysS cells . Cells were grown at 37°C until OD reached 0 . 8 , when the temperature was reduced to 18°C , followed by addition of 1 mM IPTG . After 3 hours of induction , cells were pelleted and the expression of GST-6K was confirmed by western blotting , using an anti-GST antibody ( Abcam , USA ) . The pelleted cells were resuspended in a lysis buffer containing 50 mM Tris pH 7 . 5 , 100 mM NaCl , 1 mM DTT , 10% Glycerol and 1% CHAPS , lysed by sonication , followed by centrifugation to remove cellular debris , and a two-step purification process . In the first step , the soluble fraction was incubated with 200 μl of GST beads ( Pierce Glutathione Agarose , Thermo Scientific ) for 60 minutes at 4°C , with shaking , to pull down the fusion protein . The bound protein was eluted from beads using 10–20 mM glutathione in lysis buffer . The second step of purification involved size-exclusion chromatography on a Superdex-200 10/300 G/L column , using an ÄKTApurifier 10 ( GE Healthcare ) . A buffer consisting of 50 mM Tris , pH 7 . 5 and 100 mM NaCl , at a flow rate of 0 . 5 ml/min was used for SEC elution . Purified GST-6K was incorporated in the bilayer membrane as described previously [52] . The apparatus for electrophysiology experiments consisted of a polystyrene cuvette ( Warner Instruments ) with a thin wall separating two aqueous compartments . The polystyrene divider had a circular aperture with a diameter of 150μm . Both compartments were filled with 1 ml buffer containing 10 mM HEPES , pH 7 . 4 , 500 mM KCl and 5 mM MgCl2 and connected to an integrating patch amplifier ( Axopatch 200B , Axon Instruments ) through a matched pair of Ag/AgCl electrodes . The cis chamber was connected to the head stage ( CV-203BU ) of the amplifier , while the trans-chamber was held at virtual ground . A solution of DPhPC ( Avanti Polar Lipids , USA ) and cholesterol ( 6:1 ) in n-decane ( 10 μl ) was painted over the aperture to form the membrane . Reconstitution of GST-6K in BLM ( Bilayer Membrane ) was initiated by adding desired concentration ( 0 . 2 , 0 . 85 , 1 . 2 , 2 . 6 ng/μl ) of protein in BLM buffer , followed by mixing using a magnetic stirrer . A sudden shift in the membrane current indicated the incorporation of the channel in BLM . For amantadine inhibition experiments , GST-6K was added to a final concentration of 2 . 6 ng/μl . Amantadine to a final concentration of 1 μM was added to the BLM buffer containing GST-6K only after ensuring that the protein has properly integrated in the membrane . Channel current was recorded using Digidata ( 1440A , Axon Instruments ) , Low pass Bessel filter of 2 KHz and the acquisition software CLAMPEX ( PCLAMP 10 . 2 , Axon Instruments ) . The channel current was recorded at fixed applied membrane potentials in the range of -100 to +100mV at a sampling frequency of 10 KHz ( temperature between 24–25°C ) . Open and closed states of the channel ( s ) were identified as described [52] . Data were analyzed using the software CLAMPFIT ( PCLAMP 10 . 2 , Axon Instruments ) , Origin 5 . 0 ( Originlab Corp . USA ) and Matlab . Generation of liposomes mimicking the membrane composition of the endoplasmic reticulum ( ER ) and plasma membrane , and liposome disruption assays , was carried out as described [31 , 32] . All lipids were procured from Avanti Polar Lipids ( Alabaster , AL , USA ) . For the assay , GST-6K , at a concentration of 0 . 1 μM to 10 μM , was incubated in the presence of dye-encapsulated liposomes for 30 minutes at 25°C , and end-point fluorescence was monitored at 585 nm . Purified GST was used as a control in all experiments . Experiments were carried out in triplicates on a Perkin Elmer fluorescence spectrophotometer using a quartz cuvette . DLS analysis was carried out in a Malvern Zetasizer ( Nano ZS90 ) . Briefly , freshly generated ER mimicking liposomes were incubated for 30 minutes alone , or in presence of different concentrations of GST-6K , calcium chloride or amantadine . All experiments were repeated thrice . HEK 293T cells were cultured in 12-well plates in DMEM , supplemented with 5% FBS and 1% Pen-Strep ( GIBCO ) . Transfection was carried out using Lipofectamine 2000 ( Life Technologies , USA ) according to the manufacturer’s instructions . Fixing and staining of cells for confocal microscopy were carried out as described [32] . Images were captured on a confocal laser scanning microscope ( Leica SP5 Confocal Laser Scanning Microscope ) using a 60X oil-immersion objective . Localization of 6K to cellular organelles or with E1/E2 was estimated by calculating the Pearson’s correlation coefficient , which measures the linear correlation between fluorescent channels . Values greater than 0 . 5 were considered to indicate a high degree of colocalization between fluorescent signals . General processing of images was carried out with the software ImageJ ( http://rsb . info . nih . gov/ij/ ) . All experiments were done in triplicates . For production of VLPs , ~34 . 2 x 106 adherent HEK 293T cells , maintained as above , were transfected with 32 μg purified plasmid DNA ( CMV/R CHIKVC-E3-E2-6K-E1 ) mixed with 60 μl lipofectamine 2000 , and incubated for 72 hours at 37°C , in presence of 5% CO2 . Post incubation , media was harvested and subjected to cushioning on 15% sucrose at 1 , 00 , 000x g for 2 hours at 4°C . The resultant pellet was resuspended in a buffer consisting of 20 mM Tris , pH 7 . 5 and 100 mM NaCl . For negative staining , 4 μl of GST-6K ( 0 . 5 μM ) , mixed with ER liposomes and incubated for 25 minutes , was applied onto glow discharged , 400-mesh carbon-coated copper grids ( Agar Scientific Ltd . , UK ) . The sample was incubated for 4 minutes , and the excess drained off on a Whatman filter paper . 3 μl of 2% uranyl acetate was added for 1 minute , followed by washes ( 3X ) with water , and the grid was air-dried for 1 minute . Micrographs were captured at a magnification of 29000x . For cryo-freezing of CHIKV VLPs , 4 . 5 μl of purified VLPs ( sucrose cushioned@100 , 000xg ) , at a concentration of 0 . 3mg/ml ( absorbance at 280 nm ) , was applied to glow-discharged Quantifoil R2/2 holey carbon grids . Vitrification was carried out using a Vitrobot Mark IV ( FEI/Thermofisher ) , with a blot time of 2 . 5s , by plunging grids into liquid ethane cooled by a surrounding bath of liquid nitrogen . Grids were transferred to a cryo-holder ( Gatan model 626 ) and visualized on a FEI Tecnai F20 G2 FEG Transmission Electron Microscope , operated at 200 kV . Digital images were recorded on a CCD camera using TIA software under low-dose conditions at a magnification of 50 , 000x , with a defocus range of -3 to -5 μm . 2D classification of GST-6K was carried out using EMAN 2 . 2 [53] . Briefly , particles were boxed and extracted from micrographs . A total of 746 particles were selected for generating the 2D classes . All micrographs were CTF corrected and reference-free 2D classification was carried out using standard methods . A total of 6 iterations were done and 16 representative classes were chosen . Vero cells ( African green monkey kidney epithelial cells ) and Chikungunya virus ( CHIKV , S 27 strain , accession no . AF369024 . 2 ) were kindly gifted by Dr . M . M . Parida , DRDE , Gwalior , India . Cells were maintained in Dulbecco’s modified Eagle’s medium ( DMEM; PAN Biotech , Germany ) supplemented with 5% Fetal bovine serum ( FBS; PAN Biotech , USA ) , Gentamycin , and Penicillin-Streptomycin ( Sigma , USA ) . Amantadine was purchased from Sigma Aldrich ( now MERCK ) . Vero cells were seeded in 96 well plates ( Corning , USA ) and after the cells had reached 90% confluence , they were treated with different concentrations of amantadine for 24 hours and incubated at 37°C in 5% CO2 . Cellular cytotoxicity assay was performed according to the protocol described earlier [54] . Cellular cytotoxicity was determined in triplicate and each experiment was repeated three times independently . Vero cells at 90% confluence were grown in 24 well plates in complete DMEM ( Pan Biotech , USA ) . For infection , cells were first washed two times with 1X PBS ( Himedia , USA ) and thereafter infected with CHIKV , at a Multiplicity of Infection ( M . O . I ) of 0 . 1 , in serum-free medium . The infected cells were incubated at 37°C in 5% CO2 for 90 minutes with shaking at every 10 minutes interval . After 90 minutes , cells were washed twice with 1X PBS , and incubated with complete DMEM containing different concentrations of amantadine ( 2 . 5 μM to 200 μM ) . Thereafter , cell culture supernatants from CHIKV infected , drug-treated or untreated cells , were harvested at different hpi according to the experiment for viral titer estimation . Plaque assay was performed according to the procedure mentioned before [55] . Briefly , vero cells seeded onto 6 well plates were infected with different dilutions of the harvested cell culture supernatants mentioned above . After infection , cells were washed twice with 1X PBS and overlaid with semi-solid media ( DMEM containing 10% FBS and 2% methylcellulose ) . The cells were fixed once plaques were visible and countable ( 4 to 5 days post-infection ) . For fixing , the cells were first treated with 8% formaldehyde , followed by staining with 8% Crystal violet solution . Equal volume of samples ( Mock , CHIKV infected , and infected as well as amantadine treated ) was taken for viral RNA isolation using the QIAamp viral RNA isolation kit ( Qiagen , USA ) as per the manufacturer’s instructions . RT reaction was performed using the First Strand cDNA synthesis kit ( Fermentas , USA ) as per the manufacturer instructions . An equal volume of cDNA was used during qRT-PCR for amplifying E1 gene of CHIKV [56] . In the absence of any experimentally determined three-dimensional structure for CHIKV 6K , two different structures predicted by the server Bhageerath-H [57] were utilized for simulation studies . Both structures contain a central alpha-helical segment with two other short helical stretches at the N- and the C-termini . The predicted three-dimensional structures of CHIKV 6K were embedded within a pre-equilibrated 392 POPC lipid bilayer . Peptide molecule ( s ) were placed perpendicular to the plane of the membrane . The entire system was solvated in water and 100 mM NaCl was added . After equilibration for 100 ns , the system was simulated without position restraints for 700 ns . After 700 ns , an electric field corresponding to 90–110 mV was switched on along the z-axis for facilitating ion conduction through the channel . All simulations were carried out for 1 . 5 μs , using the GROMACS package v5 . 1 . 1 [58] . Specific simulation steps followed has been outlined earlier [31 , 59] . Gromacs analysis tools , UCSF Chimera [60] and VMD ( Visual Molecular Dynamics ) [61] were utilized for data analysis and molecular visualization . | Chikungunya fever is a severe crippling illness caused by the arthropod-borne virus CHIKV . Originally from the African subcontinent , the virus has now spread worldwide and is responsible for substantial morbidity and economic loss . The existing treatment against CHIKV is primarily symptomatic , and it is imperative that specific therapeutics be devised . The present study provides detailed insight into the functionality of 6K , an ion channel forming protein of CHIKV . Amantadine , a known antiviral against influenza virus , also inhibits CHIKV replication in cell culture and drastically alters the morphology of virus particles . This work highlights striking parallels among functionalities of virus-encoded membrane-interacting proteins , which may be exploited for developing broad-spectrum antivirals . | [
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] | 2019 | The effect of amantadine on an ion channel protein from Chikungunya virus |
Cancellation of redundant information is a highly desirable feature of sensory systems , since it would potentially lead to a more efficient detection of novel information . However , biologically plausible mechanisms responsible for such selective cancellation , and especially those robust to realistic variations in the intensity of the redundant signals , are mostly unknown . In this work , we study , via in vivo experimental recordings and computational models , the behavior of a cerebellar-like circuit in the weakly electric fish which is known to perform cancellation of redundant stimuli . We experimentally observe contrast invariance in the cancellation of spatially and temporally redundant stimuli in such a system . Our model , which incorporates heterogeneously-delayed feedback , bursting dynamics and burst-induced STDP , is in agreement with our in vivo observations . In addition , the model gives insight on the activity of granule cells and parallel fibers involved in the feedback pathway , and provides a strong prediction on the parallel fiber potentiation time scale . Finally , our model predicts the existence of an optimal learning contrast around 15% contrast levels , which are commonly experienced by interacting fish .
For many neural systems , prediction and cancellation of redundant signals constitutes one of the most convenient features for efficiently processing behaviorally meaningful information . When processing sensory input , for instance , neural circuits must be able to discriminate a novel stimulus from the background of redundant or non-relevant signals . A well-known situation in which such a discrimination may be highly advantageous is the so called “cocktail party problem” , in which a particularly relevant signal is extracted from a mixture containing other unimportant signals [1] , [2] . This is known to be useful , for instance , to identify particular voices or sounds for both human and nonhuman animals [1] , [3] , or find and identify mates among conspecifics and heterospecifics [4] . However , the concrete mechanisms that the brain may employ to discriminate and cancel redundant information are presently unknown . It would be , therefore , convenient to identify and closely study natural systems displaying such a cancellation phenomenon , in order to isolate its fundamental principles . Of special interest might be the mechanisms able to conduct the cancellation process over a wide range of realistic conditions , such as canceling redundant signals of different intensities ( or with time varying intensities due , e . g . , to the relative movement of the receiver and the signal sources ) while keeping novel stimuli intact . The understanding of such a contrast-invariant cancellation mechanism would be beneficial not only for the “cocktail-party problem” in auditory systems , but also for visual neuroscience . Indeed , contrast invariance is a well known and well studied feature of the visual cortex , and particularly of the V1 area [5] , [6] . A number of ingredients are thought to play a role in contrast invariance in V1 , such as inhibition [7] , [8] , gain control [9] , [10] or membrane fluctuations [9] , [11] , to name a few . However , many of the strategies giving rise to contrast invariance in V1 are still highly debated [12] , [13] or simply starting to be uncovered [9] , [14] . Consequently , new findings about how contrast invariance is achieved in other sensory modalities such as the simpler electrosensory system might contribute to understand contrast invariance in V1 and possibly to identify common principles for the corresponding biophysical mechanisms . The contrast-invariant cancellation sketched above stands as an interesting potential example . A system able to perform cancellation of redundant information is known to exist in the electrosensory lateral-line lobe ( ELL ) of the weakly electric fish Apteronotus leptorhynchus [15]–[17] . This fish continuously emits a wave-type , high frequency ( 600∼100Hz ) sinusoidal electric organ discharge ( EOD ) to sense its surroundings and communicate with conspecifics . Small objects such as prey produce spatially localized amplitude modulations ( AMs ) in the EOD . On the other hand , the presence of conspecifics or own-body movements such as tail bending induce spatially global AMs in the EOD . For example , since each fish has a fixed EOD frequency , the proximity of two fish produces an AM in the form of a beat of fixed frequency but time-varying amplitude due to the relative motion of the animals . The depth of these AMs , referred commonly as contrast , may depend on physical quantities such as the distance to conspecifics or the amplitude of the tail movement , in the case of global signals , or the size of ( or distance to ) the prey , in the case of local signals . Both spatially local and global AMs are encoded by electroreceptors ( mainly , P-units ) that densely cover the body of the fish [18] . In particular , AMs in the EOD are reliably encoded with a modulation in the firing rate of the P-units , which provide feedforward input to pyramidal neurons in the ELL . Interestingly , it has been found that a subpopulation of these pyramidal neurons , called superficial pyramidal ( SP ) cells , are able to respond selectively to local stimuli ( i . e . prey ) by removing low-frequency global redundant signals ( i . e . tail bending ) , and thus maximizing the response to novel local stimuli [15] , [19] . In the following , we will denote this pathway from the P-units to the SP cells as the feedforward pathway . This removal of global signals is also present in another family of electric fish , namely the mormyrid weakly electric fish , although the mechanism differs significantly [20] , [21] . These fish emit a pulse-type electric field instead of a wave-type field . The pacemaker generating the EOD also conveys spike discharges internally to ganglion neurons , to which the electroreceptors project . Through the so called anti-Hebbian spike-time-dependent plasticity , these ganglion neurons use this internal timing information ( corollary discharge ) to cancel out the redundant responses from the electroreceptors caused by the fish's own pulses , thus allowing an efficient detection of novel stimuli [22] . For both pulse-type and wave-type fish , the cancellation of global signals is achieved via the activation of a neural circuit denoted , by convention , as the indirect feedback pathway ( it should be noted , however , that it is actually a longer feedforward circuit from the P-units to the SP cells via DP cells , as we will see below ) . Such a circuit , which we will denote here simply as feedback pathway , involves a granule cell population , the eminential granularis posterior ( EGp ) , which projects a massive number of parallel fibers ( PFs ) onto SP cells . In spite of this common architecture , the cancellation mechanism for the wave-type fish A . leptorhynchus is significantly different from the one used by pulse-type fish , not only because of the nature of its EOD ( wave-type ) , but also because the corollary discharge is not present in wave-type fish . For the particular case of wave-type fish , the presence of burst-induced long-term plasticity in the PF-SP cell synapses [23] , together with the segregation of the PFs into frequency-specific channels [16] , [24] , shapes the feedback input to the SP cell into a negative image of the redundant sensory stimulus , causing destructive interference and effectively canceling the global stimulus in the SP cells [16] . Little is known , however , about how different stimulus contrasts are processed in such a circuit . The AM contrast level of a signal is strongly correlated with the spatial proximity of the source , either for local input ( i . e . distance to the prey ) or global input ( i . e . distance to conspecifics ) [25] , and thus constitutes a highly variable feature of the stimulus . Ideally , the fish would be expected to detect the presence of prey ( and properly estimate the corresponding distance ) while in the presence of other conspecifics at different distances from them . This would imply that SP cells display some form of cancellation for global stimuli of different contrasts . Neither the existence of such a cancellation nor its concrete dependence with the stimulus contrast have been experimentally quantified to date . Furthermore , the mechanisms that might lead to this phenomenon are not known . Arguably , a linear system would be expected to maintain the output as a given fraction of the input , regardless of the input strength . The neural circuits of interest here , however , are known to involve nonlinearities , including not only the input-output nonlinearity arising from the spiking threshold , but also the ones due to the presence of bursting and spike-timing dependent plasticity rules . Due to these nonlinearities , the particular configuration of PFs needed to properly cancel signals with a given contrast might be unable to provide consistent cancellation for another contrast . Also , since PFs are already segregated into different frequency-specific channels [16] , it is unlikely that this strategy could be followed again to form contrast-specific channels , due to the limited number of PFs available . Therefore , both novel experimental observations and models are needed to address the question of cancellation of global stimuli with different contrasts . In this work , we tackled this problem by employing a combination of experimental and modeling methods . We performed in vivo extracellular measurements of SP neuron activity for global and local stimuli of different AM frequencies and contrasts . Our measurements clearly show the existence of contrast-invariant cancellation of global stimulus for a wide , behaviorally relevant range of stimulus contrasts . Although a slight decay in cancellation for increasing contrasts is observed , the cancellation level decays only about across all the range of contrasts considered , and thus cancellation remains at all times at values over ( being a perfect cancellation of the global signal ) . In addition , a computational model is fitted to our in vivo data and the in vitro results presented in [23] , and it is employed to explore the origins of this contrast invariance . Our model is based on those from previous studies [16] , [24] , which considered a feedback pathway composed of multiple delayed PFs projecting onto SP cells , with the strength of these PFs determined by a burst-induced long-term plasticity rule . In the model presented here , we also consider several novel mechanisms needed to understand contrast-invariant cancellation , which are: ( a ) the explicit modeling of the P-unit input/output characteristics , which affect both the feedforward and feedback pathways , and ( b ) the presence of saturating effects in the feedback pathway . We have also considered that plasticity does not shape PFs quickly , so that the model will have to deal with contrast levels that it was not explicitly trained to cancel . This last point is extremely important , as a system in which PFs are allowed , via long-term plasticity , to relearn how to cancel every new stimulus would be highly unrealistic . Employing this highly detailed model fitted to our in vivo data , we find that ( i ) in spite of nonlinearities associated with PF plasticity , the level of AM contrast is successfully transmitted through the feedback pathway , matching the contrast arriving at SP cells through the feedforward pathway and explaining the contrast-invariant cancellation found in vivo , ( ii ) the PF weights associated with a given contrast are able to provide good cancellation for other contrast levels , and ( iii ) the minor decay of cancellation with contrast is due to the saturation of activity in the feedback pathway . In addition , our model predicts that , in order to properly cancel global signals at the experimentally observed levels , the average contrast level that must drive the PF learning lies around contrast levels . Interestingly , contrast levels around this one are commonly found within the natural environment for communication signals in the weakly electric fish [25] . This hypothetical link between social interaction and redundancy reduction in neural circuits might be used to uncover neural or synaptic mechanisms which are elusive to standard in vivo or in vitro techniques .
The goal of this study is to understand the mechanism that neurons in the ELL of the weakly electric fish employ to cancel spatially redundant signals with different contrasts . To do that , we first analyze experimental data from in vivo recordings . Fig . 1 shows the extracellularly recorded response of superficial pyramidal ( SP ) neurons in the ELL under different stimuli . As one can see , SP cells respond strongly and in a phase-locked fashion to local stimuli ( Fig . 1A , black ) , whereas the response is much broader in phase , and smaller in amplitude , when the stimulus is global ( Fig . 1A , gray ) . By considering peri-stimulus time histograms ( PSTH ) , we confirm that , within the range of frequencies of the AM considered , the response to global signals is effectively cancelled ( Fig . 1B ) . As previous studies have addressed , the cancellation is most pronounced within this AM frequency range [16] , and it is achieved by the emergence of a negative image of the original signal , generated by the feedback pathway [15] , [16] . More importantly , whereas previous studies [16] , [24] characterized cancellation for a single stimulus strength , in this study we present the stimulus at different contrast levels ( i . e . different strengths ) . We observe that the stimulus is cancelled efficiently for a wide range of input contrast levels , with cancellation values over in all cases . Cancellation was measured by the ratio in gain between the local and global responses ( see Methods for details ) . Furthermore , the level of cancellation appears to be approximately the same for all contrasts , from very low ( ) to very high ( ) values ( Fig . 1C ) , with a minor decay of cancellation levels observed for very high contrasts . Contrasts higher than were not considered in this study , since P-unit electroreceptors encode AMs in a nearly linear manner up to . After that , the activity of P-units saturates and biologically relevant information can not be processed in the same quasi-linear regime [26] . When averaging over all the frequencies considered , we can observe that the degradation of the cancellation process ( defined as the complementary of the frequency-averaged cancellation , i . e . ) is restricted to a range between and , and therefore the cancellation of global signals only varies in about for all the range of biologically relevant contrast levels ( Fig . 1D ) . We first consider the response of the SP neuron to local stimuli . The dynamics of the neuron membrane potential is modeled following a leaky integrate-and-fire ( LIF ) formalism [27] , [28] with an extra burst-inducing mechanism . The subthreshold dynamics of the membrane potential is given by ( 1 ) where denotes rectification of the input , is the input from the P-units encoding the sensory stimulus , is the burst-inducing mechanism needed to reproduce the behavior of in vivo SP cells [29] , [30] , and is a Gaussian low-pass filtered noise of mean and standard deviation to fit the model to baseline ( also referred here as spontaneous ) activity conditions ( i . e . no AM ) . As in the standard LIF formalism , when reaches a certain fixed threshold , a spike is recorded , and after that remains at a certain resting value during the absolute refractory period of the neuron . When stimulated by a sinusoidal input , the model SP neuron responds with a modulation of its firing rate . Fig . 2A shows the maximum firing rate ( solid black line ) as a function of the amplitude of the sinusoidal-like signal entering the SP cell from the P-units . In color lines , we see the maximum firing rates observed experimentally for different contrasts entering the P-units . By looking at the intersections of the black curve with the color lines , we can determine the relationship between input contrast to the P-unit and input modulation to the SP cell ( i . e . the P-unit output ) . The resulting input-output relationship for the P-units is shown in Fig . 2B . As we can see , the P-units display some degree of saturation for high input contrasts ( of about ) . This agrees with previously known results [26] which show that P-units behave as linear encoders for relatively low contrasts ( up to ) , and beyond that point they start to saturate . We can now easily include such a saturation effect into our model ( see section Methods ) . Once this nonlinearity has been considered , the response of our model agrees very well with the experimental observations for local stimuli , as one can see in Fig . 2C , for different AM frequencies and contrasts . We consider now the situation in which we have a spatially global stimulus in the system . The presence of the global stimulus activates the feedback pathway which projects onto the SP cells via the PFs . This implies considering an extra term in the dynamics of the membrane potential of the SP neuron , which is now ( 2 ) In the last term , is the strength of the feedback ( which will depend nonlinearly on the contrast since the feedback pathway is also driven by P-unit activity ) , and the term mimics the effect of disynaptic inhibition driven by the PFs . More precisely , since the reversal potential of the inhibitory synapse ( GABA-A receptors ) is close to the resting potential of the SP cell [31] , inhibition was modelled as an extra shunting conductance [32] . The quantity is the strength of the particular PF which is active ( i . e . which is transmitting a burst arriving from a granule cell ) at the phase segment of the signal cycle . For simplicity , we assume that only one PF is active at a given time ( see section Methods for details ) . At the SP cell , we distinguish between small and large bursts , since the characteristics of the learning rule will be different depending on the burst size [23] . Adopting the burst definitions which were explicitly characterized in [23] , we will consider the 2-spike burst as the typical small burst , and the 4-spike burst as the typical large burst ( see Fig . 3A , and section Methods for further details on burst definition ) . Small and large bursts have different roles in the cancellation process , as it was found in [16]: large bursts cover long periods associated with low-frequency input , while small bursts have a similar role for high frequencies and are also important in the timing of the plasticity for these input frequencies . For burst timing purposes , the temporal location of a given burst is identified as the temporal location of its first spike . The burst-STDP learning rule employed , based on in vitro experimental recordings [23] , is shown in Fig . 3B . Every time a pair of presynaptic-postsynaptic bursts occur , each PF weight is updated according to the following rule ( 3 ) where and are used if the burst of the SP cell is a small burst , and and are used if it is a large burst . The presynaptic burst is assumed to match the burst type occurring at the SP cell . Once again , symbolizes rectification , which means this rule is applied to all weights whose phase segment began at a time as long as . Beyond this range , the weights are unchanged . Note that the burst-induced depression found in vitro is purely depressing and would eventually decrease all PF weights to zero . To avoid that , we include a non-associative potentiating rule so that the weights slowly relax back to with a time constant of according to ( 4 ) This rule maintains the independence of synaptic weights and is biologically plausible , since Lewis and Maler [33] demonstrated a presynaptic form of synaptic enhancement in PFs . This enhancement was elicited when PF discharge occurred , without the need of a concomitant pyramidal cell burst response . Such a form of potentiation lasted for many minutes , and a weak potentiation with this or larger would have been difficult to detect experimentally . Furthermore , similar potentiation rules have been experimentally observed in mormyrid fish , and it has been shown to play an important role in cancellation in these fish [34] , in which there is a corollary discharge . Below we will see that the homeostatic time constant may play an interesting role in the learning dynamics . The response of the SP neuron model was always recorded after a certain learning period , during which all PF weights reached their equilibrium state . One of the main points to take into account is that the strength of the PF synapses will depend on the stimulus contrast employed during the learning period ( we denote such contrast level as learning contrast , ) . This is due to the fact that the occurrence of SP bursts , which are the driving events of PF plasticity , depends strongly on the stimulus contrast . The effect of the contrast on PF weights is shown in Fig . 3C , where one can see that the distribution of PF strengths is similar for different learning contrasts , although not exactly the same . In all cases , the sinusoidal stimulus shapes the PF weights to form a negative image of the signal , which constitutes the basis of the cancellation phenomenon . The weights for different learning contrasts are almost identical around the peak of the stimulus ( corresponding here to a phase of ) , where the SP neuron is mainly driven by the stimulus ( and noise plays a relatively minor role ) , and bursts are more likely to occur . The variability of PF weights with the learning contrast is higher for the signal trough ( around phase of ) , where bursts occur scarcely and are not able to efficiently shape the weight distribution . As we can see , PF weights around the signal trough are higher for high learning contrasts . This is due to the fact that a high-contrast stimulus induces a strong hyperpolarization in the SP membrane potential at the stimulus trough , lowering the chances of bursting for that stimulus phase and preventing the depressing LTP rule to decrease the weights . Interestingly , even though both the feedforward and the feedback inputs are sinusoidally driven , the distribution of PF weights significantly deviates from a sinusoidal function , as one may clearly observe from Fig . 3C . Such a deviation has its origin in the highly nonlinear nature of burst dynamics in SP cells . Indeed , it is known that bursting rate displays a highly nonlinear , exponential-like relationship with input ( see Fig . 5B in [24] ) . Since bursts are the main events driving the PF learning , the nonlinear input-burst relationship is translated into a nonlinearity in the PF weight distribution emerging from learning . This , successively , turns the feedback input to the SP cell into a highly nonlinear contribution that prevents treatment of the cancellation phenomenon as a trivial linear summation of sine waves that are out of phase with one another . In Fig . 3D , two examples of cancellation of a global signal of a frequency of , a learning contrast of , and different stimulus contrasts are shown ( in each panel , the corresponding SP neuron response to same-frequency , same-contrast local signals is displayed in gray for comparison purposes ) . As we can see , the cancellation is very good in both cases , although the model overestimates the degree of cancellation for the contrast case ( violet line in right panel ) . In both panels , experimental data are plotted with points and model results are displayed with lines . The PF weights are modified via long-term plasticity mechanisms , which operate in the order of minutes to hours . Since changes in stimulus contrast associated with behavior ( i . e . tail bending becoming narrower ) may occur on the scale of milliseconds to seconds , one can not expect that the weights will be able to adapt fast enough to new presented contrasts in realistic situations . More likely , PF weights will reach some stationary level ( as a result of some time-averaged contrast level provided by day-to-day natural stimuli ) , and then such an equilibrium level will be used to cancel any particular contrast level that the fish receives . In such a situation , we could expect certain differences in the quality of the cancellation depending on the learning contrast assumed in the simulations . The model prediction of the cancellation level for different frequencies and contrasts , and assuming different learning contrasts , is shown in Fig . 4 . In all cases , the cancellation levels are maintained on values over for different contrast levels , as in the experimental observations ( see Fig . 1C ) , and thus indicate the emergence of contrast invariance in the cancellation of global stimuli in the model . It is particularly interesting to note , from the model results , that the specific learning contrast chosen has little impact on the results , contrary to what was expected , and that the levels of cancellation are broadly the same for all AM frequencies ( with high frequencies having slightly lower values , as seen also in the experimental data ) . Note that , in addition to this counterintuitive lack of impact of the learning contrast in the cancellation , some qualitative differences appear with respect to the experimental data . Concretely , for all frequencies and learning contrasts , the level of cancellation slightly increases with the stimulus contrast according to the model predictions , while in the experiments it slightly decreases . The origins of such model/experiment discrepancy may be diverse , but it is reasonable to assume that they could be mainly due to the lack of a key ingredient in the model's feedback pathway , since the response of the model and experiment for the local signal were in good agreement both qualitatively and quantitatively ( see Fig . 2 ) . Since the discrepancy is mostly evident for large contrast values , one might want to consider , as a first approach , possible features of the real system that may be particularly relevant at those conditions . A relevant factor to consider here is the existence of saturation effects along the feedback pathway . Saturation is inherent to all spiking neurons , and high contrast input to granule cells might cause saturation in two , not mutually exclusive ways: higher contrasts might evoke discharge in a greater number of granule cells and/or it might evoke a higher frequency discharge in granule cells . The first situation was examined by Berman and Maler who used stronger PF stimulation to activate greater numbers of PFs; clear saturation of the PF response was observed ( Fig . 5B in [32] ) . The second scenario was studied by Lewis and Maler [35]; this study demonstrated a saturating SP response to increasing PF stimulation frequency ( Fig . 5 in [35] ) . The presence of saturation in the PF-SP synapses imposes a limit in the feedback strength for increasing stimulus contrasts , which would naturally lead to a devaluation of the cancellation quality for high contrasts as observed in vivo . Furthermore , it is reasonable to think that , in addition to this PF saturation , the bursting activity of granule cells might as well saturate for high enough contrast values ( since only a limited number of granule cell bursts can be generated within one stimulus cycle ) , adding an extra layer of saturation to the feedback pathway . To take into account this saturating behavior in the model , we consider a small correction in the feedback gain for high contrast values by introducing a factor in the last term of Eq . 2 . This extra factor will be one for low contrasts ( i . e . , and ) and less than one of higher values ( we chose for contrast and for contrast , although other values are possible without qualitatively varying our conclusions ) . The cancellation levels with this new assumption are shown in Fig . 5A , for different frequencies and learning contrasts . As we can see now , the slight decrease in cancellation levels with increasing contrasts is present for all learning contrasts , in agreement with experimental data ( shown as a gray line in panels of Fig . 5A for a direct visual comparison ) . Therefore , we have identified , via a computational model , the saturation of the feedback pathway as a plausible origin of the slight cancellation decrease with contrast . Our model also provides some insight into the plausible saturating dynamics of these granule cells , which have not been recorded in vivo to date . A second conclusion that we can make from our modeling results concerns the level of cancellation for different learning contrasts . As we can see in Fig . 5A , considering different learning contrasts has now a clear effect on the cancellation properties , as opposed to the case in which saturation of the feedback pathway was not considered . Indeed , high learning contrasts shift the cancellation curves to higher values , leading to higher cancellation levels for all contrasts and frequencies considered . The closest agreement with the experimental data is obtained with a learning contrast of , as we can see in Fig . 5A and more clearly in Fig . 5B . Lower learning contrasts lead to low values of overall cancellation , mainly because PF weights are tuned to cancel only weak modulations and are not able to overcome a large-amplitude signal completely . On the other hand , higher learning contrasts produce an over-cancellation at low input contrasts ( that is , the SP cells have a peak of firing rate where the stimulus displays a trough , and vice versa ) which is not observed experimentally . Due to the saturation of the feedback pathway with contrast , the PFs have to span a wider range of weight values in order to obtain a proper cancellation , and this has a negative effect when trying to cancel low contrast signals . Therefore , the optimal learning contrasts are those situated just below the appearance of a strong saturation in the feedback pathway , but strong enough to allow cancellation for the whole regime of linear encoding of P-units , around . For such an optimal learning contrast , individual firing rate responses are also shown in Fig . 6 as a function of the global stimulus phase for different contrasts and frequencies . The figure also shows the corresponding experimental SP response to local stimulation for comparison purposes . It might be argued that is only optimal when compared to the few other values of the learning contrast considered here . To better characterize the optimal learning contrast and its robustness , we have employed our model to extend our study and to consider other learning contrasts . We consider now a range of possible learning contrasts ( around eleven values from to ) and we compute , for each one of them , the degradation of cancellation as a function of the stimulus contrast , as in Fig . 5B . For learning contrasts not considered in the experiments , such as , values for the P-unit adaptation and feedback saturation in the model were obtained by linear interpolation between known values . We also define a function which quantifies the discrepancy between the model prediction for a given and the experimental data . The error function is given by ( 5 ) where runs over all stimulus contrasts considered ( up to for an easier comparison with experimental data ) , and are , respectively , the model and experimental degradation for the stimulus contrast . As we can see in Fig . 7A , the error function is minimal for . Considering surrounding contrast levels with similar error function values would give us a range of optimal learning contrast of contrast levels , which correspond to reasonably low error levels in the figure . Interestingly , contrast levels around this range are commonly found within the natural environment of the weakly electric fish ( Yu et al . , personal communication ) . In particular , it has been experimentally shown that the presence of free-swimming conspecifics induces a certain range of contrast levels in the electric fish , being these levels centered and more common around ( see Fig . 3B in [25] ) , in strong agreement with the optimal contrast level predicted by our model . In addition to identifying an optimal learning contrast around , our model gives us insight into the dynamics of weak PF potentiation . As it has been argued , the potentiation rule is hard to find experimentally , since it is expected to work at very long time scales and would therefore have a hardly appreciable effect during in vitro recordings [23] ( associative potentiation rules have been , however , found in mormyrid fish [34] ) . To study the potentiation time scale in detail , we evaluate the error measurement defined above as a function of the learning contrast , for different values of the potentiation time constant . As Fig . 7B shows , different values lead to different error curves . Time constants of or above yield mainly the same results , i . e . the optimal learning contrast lies around . Smaller time constants significantly deviate from this value , which is to be expected since a small time constant would lead to more rapid forgetting of the phase-specific synaptic strength and thus significantly modify the learning rule and the PF weight distributions . The model learning rule would thus not fit the experimental data anymore . For time constants of or lower , the optimal learning contrast is found to be higher , which makes intuitive sense because the system forgets faster , but the minimal error reached in these cases is substantially larger than for larger time constants . This can be clearly seen in Fig . 7C: the minimal error decreases as increases , until a global minimum is reached for ( corresponding to an optimal learning contrast of about ) . This value for the potentiation time constant is therefore a strong prediction of our model . In vitro experiments that pharmacologically eliminate the confounding effects of postsynaptic depression [23] and disynaptic inhibition [35] should be able to precisely estimate and therefore test our prediction .
Removal of redundant information is a key task to accomplish for an optimal detection of novel stimuli in sensory systems . Unfortunately , not many neural mechanisms are known to provide such a filtering under realistic conditions . In this work , we have analyzed one of the few neural circuits clearly identified as a system able to cancel redundant information , which involves the indirect feedback pathway to the ELL of the weakly electric fish Apteronotus leptorhynchus . Our results , obtained from a combination of in vivo extracellular recordings and detailed computational modeling , reveal a plausible framework which explains the cancellation of redundant information observed in the electric fish [15] , [16] . They further reveal that this cancellation displays contrast invariance over the entire range of behaviorally relevant contrast levels [26] . The key ingredients for this contrast-invariant cancellation can be summarized in ( i ) the efficient transmission of the contrast level through the ( nonlinear ) feedback pathway , resulting in a match of the feedback input to the feedforward signal in the SP , and ( ii ) the fact that , when the PFs adjust their synaptic weights to cancel a given contrast , they provide a good cancellation input for other contrasts as well . Due to these two features , the model is able to explain the high levels of cancellation ( over at all times ) observed in experiments for a broad range of AM frequencies and contrasts , thus providing a theoretical framework for the contrast invariance in cancellation . This theoretical framework may be helpful to understand other neural systems where cancellation of redundant signals occurs ( such as auditory systems [36] or other neural circuits confronting “cocktail party” problems [1] , [2] ) and may also provide novel useful points of view to understand contrast invariance in visual systems [6] , [9] . In addition , our finding might be seen as a very simple form of context-specific adaptation [37] , since the adaptation mechanism ( the feedback input into SP cells ) would make the SP response different depending on the behavioral context ( e . g . prey vs conspecific ) . In order to achieve the good agreement of this model with the experimental data , an additional ingredient has been necessary to explain the minor decay of cancellation levels with contrast found in vivo . According to our modeling results , a weak saturation in the feedback pathway is a sufficient condition to explain the decay in cancellation for high stimulus contrasts , and this saturation may have different sources . It is known , for instance , that a strong PF stimulation activating a large number of PFs induces a prominent saturation in the PF transmission [32] . Such a saturation phenomenon in PFs would be enough to provide the weak level of saturation needed to explain our experimental findings . Furthermore , PFs are also known to saturate for increasing frequency [35] , and other factors such as a possible saturation in granule cell firing or the presence of short-term plasticity mechanisms found in PF synapses [38] may also contribute to the saturation of the feedback pathway . In particular , short-term synaptic depression could be a plausible candidate to induce feedback saturation , as it can induce synaptic fatigue causing nonlinear gain control [39] and , when interacting with short-term facilitation , can produce important effects in the dynamics of recurrent neural circuits [35] , [40] , [41] . While we have assumed in this work that granule cells in the EGp fire in a bursty fashion and phase-lock to the periodic stimulus , there is an ongoing debate concerning the propensity of the granule cells to fire in bursts [42]–[46] . Importantly , we are dealing here with a specialized group of cerebellar cells , the Zebrin-2 negative cells [47] , whose firing patterns are not known to date . However , the presence of granule cells with a strong bursting behavior is not required for our conclusions to hold . Since the global stimulus is of a periodic nature , it will likely induce the clustering of granule cell spikes around a certain range of stimulus phases , even if the granule cells do not have a tendency to burst . A simple scaling of the granule cell response with the stimulus , as it occurs for SP cells , is therefore the only essential requirement of our model . Similarly , the concrete input/output characteristics of granule cells do not have a strong impact on our results , as saturation for large contrasts has already been found experimentally in parallel fibers [32] , and therefore it is not necessary to impose this condition to granule cells . The possible role of different types of inhibition in the cancellation of global signals has been experimentally addressed previously . For instance , Maler et al . [48] presented morphological evidence suggesting the presence of lateral inhibition ( but not recurrent inhibition ) , as the one we are considering in our model . Bastian et al . [49] demonstrated the existence of inhibitory surrounds for superficial pyramidal cells , but it was later observed [15] that the cancellation could be completely prevented by blocking the EGp feedback , and thus suggesting that these inhibitory surrounds do not have an important role for cancellation . Therefore , the role of other types of inhibition can not be completely ruled out , but their effects on cancellation have been found to be much less important than the PF-triggered feedback inhibition that we are considering in our study . On the other hand , local input activates feedforward inhibition , but this type of input does not trigger cancellation as we also illustrate in Fig . 1B . It is also important to mention that , due to the fact that the same stimulus is driving both the SP cells ( via the feedforward pathway ) and the EGp ( via the feedback pathway ) , parallel fibers will be naturally time-locked to the stimulus . As a consequence , any initial phase displacement in the stimulus ( with respect to previous stimuli ) will affect both the SP cells and the EGp in the same way and will not affect the cancellation . Sudden and fast phase shifts like the ones associated with communication signals ( i . e . small chirps [50] ) , however , will not be predicted and cancelled by the present mechanism , and they will be treated as novel stimuli since they may carry useful behavioral information . In addition to different stimulus contrasts , we have considered signals with different AM frequencies in our study . Local signals ( such as prey ) , which should not be cancelled , usually fall into the range of frequencies considered here ( from to ) . Such a range of frequencies lies within the band of good cancellation observed experimentally and , consequently , we do not observe major differences among AM frequencies in the cancellation level [16] . A slight decrement in cancellation is however observed for , which is indeed to be expected since cancellation starts to decay around and is practically inexistent at [16] , [24] . On the other hand , while frequency-specific channels have been identified in this system [16] , the width of a given frequency channel is not known to date . In our model , we have assumed that frequencies as close as and are canceled via different frequency-specific channels , but it might be possible that both frequencies fall into the cancellation domain of a single channel . However , the detailed mechanisms that such a broadband channel could employ to cancel close ( but different ) frequencies are unknown and they fall out of the scope of our study . Therefore we assumed here that each frequency was processed by a specific channel . The good agreement between our experimental observations and model predictions suggests that our approach may be indeed adequate . It is also worth mentioning that the specific definition of burst does not have a fundamental importance in our model . In particular , the one used here ( i . e . the occurrence of a number of spikes within a fixed time window ) has been chosen for being computationally adequate , but also for being consistent from a biophysical point of view . This is explained by the following two factors: ( i ) the ISI distribution of SP cells is bimodal [16] , with a clear frequency-independent peak at small ISI values which highlights the existence of bursting [51] , and ( ii ) in our system , plasticity is not triggered by single-pulse paired stimulation [23] . The combination of these two factors suggests that SP bursts are structured and well located in time , and highlights the importance of bursts as functionally meaningful events which clearly differ from single spikes . The PF plasticity rule , as presented in [23] , would constantly weaken PF strength until all synapses would reach zero strength . To avoid that , a weak , phase-independent potentiation mechanism was considered here following previous studies [16] . This potentiation rule might have to be extremely slow such that its effect was not detected in standard in vitro protocols . Such a plasticity mechanism would therefore be hard to measure for most direct methods . Aided by our model and experimental findings , we were able to predict a reasonable value for the potentiation time constant , of about . This estimation constitutes a strong prediction of our model , and further modeling and experimental studies will be necessary to corroborate and extend this prediction . Our model indicates the existence of a certain optimal learning contrast , which presumably resembles the time-averaged contrast that should drive the PF plasticity to obtain the cancellation values observed experimentally . We have found that this optimal learning contrast lies around contrast levels ( or , considering a small range of contrast levels , around ) . Interestingly , contrast levels around this value are commonly found within the natural environment for communication signals in the weakly electric fish , such as , for instance , when surrounded by free-swimming conspecifics [25] . Indeed , assuming that a global AM of contrast level is due to the presence of a conspecific , this would correspond roughly to a distance of cm between both fish [52] . In addition , this indicates that a close experimental measurement of the common contrast levels found in the fish's natural environment ( which has been the goal of recent studies [25] ) might provide a good indirect confirmation of the existence of weak potentiation rules which are hard to find in in vitro conditions . Further experimental and modeling work is needed , however , to clarify these possible links , as well as the impact of other realistic assumptions in our circuit , such as considering heterogeneous populations of superficial neurons [53] , [54] . Finally , the study of cancellation of global signals needs to be extended to situations in which more realistic stimuli are considered . Although of remarkable usefulness , the assumption of global sinusoidal signals would correspond only to the case of a perfectly periodic tail movement , or to the presence of a static conspecific at a certain fixed distance . However , tail bending is commonly an aperiodic movement in real conditions and , in addition , the distance between two electric fish would constantly vary as they swim . This implies that the stimulus contrast will vary in time , as for instance following an Ornstein-Uhlenbeck process as shown by Yu et al . [25] , and such variations are likely to be relevant in the cancellation process . This constitutes a much more complex situation than the one studied here ( in which each of the contrast levels we have investigated is constant in time ) , although preliminary work suggests that the present framework may be extended to explain the cancellation for those complex situations as well . For instance , in a complex global signal constituted by several coexisting frequency components , each one of them could be cancelled independently by frequency-specific channels present in the feedback circuit [16] . Furthermore , slow variations in the contrast level for any given frequency might activate other adaptation mechanisms that could aid in the cancellation , as we are currently investigating .
All experimental procedures were approved by the University of Ottawa Animal Care Committee . Experimental recordings were performed as in [16] . Briefly , craniotomy is performed under general anesthesia . During the experiment the fish is awake but paralyzed with curare and locally anesthetized . Single-unit extracellular recordings from superficial pyramidal E cells of the centro-lateral segment of the electrosensory lateral line lobe were performed . These cells can be easily identified based to their location ( depth and centro-medial position ) , their receptive field , their baseline firing rate and response properties . Stimuli consisted of amplitude modulations of the fish's own electric field . The stimulus was delivered through two large global electrodes placed on each side of the fish thereby achieving a global stimulation . For local stimulation , a small dipole was placed in the center of the cell's receptive field; the distance between the dipole and the skin was adjusted to maximally stimulate the whole receptive field of the cell while avoiding stimulation of receptors outside the classical receptive field . To understand the biological mechanisms responsible for contrast-invariant cancellation of global stimuli , we consider a simplified model of the ELL and the indirect feedback pathway ( see Fig . 8 ) . AMs of the sensory input are encoded in firing rate modulations of the P-units . If the stimulus is spatially local , P-units transmit these modulations to the SP neuron , which projects to other higher brain regions . If the stimulus is spatially global , the feedback pathway is also activated ( in addition to the feedforward pathway sketched above ) and a population of EEL neurons called deep pyramidal ( DP ) cells transmit the signal from the P-units to a granule cell population , the eminential granularis posterior ( EGp ) , via the Nucleus praeminentialis ( nP ) . Each granule cell projects through parallel fibers onto the SP neuron , closing the feedback loop . In the real system , the cerebellar feedback pathway to the ELL is bilateral [18] , [55] and can take several routes before returning to the ELL . Furthermore , DP cells , which constitute the origin of the feedback pathway , display a variety of phase relationships with the stimulus depending on its location ( i . e . , the side of the body ) and the specific cell type ( E-cells , which fire at the signal peak , or I-cells , firing at the trough ) [56] , [57] . Finally , each granule cell will be located at a certain position in space and therefore it will be characterized by a particular distance to the target SP cell . All these elements together produce a wide range of feedback temporal delays , suggesting that the PFs can provide feedback to SP neurons at all possible phases of the global periodic stimulus . In addition to this , granule cells have been reported to phase-lock to periodic signals and to burst to sensory stimuli and be silent elsewhere [43] , [45] , [58] , [59] . To include these features in our simplified model , we assume that ( 1 ) the array of PFs provide feedback to SP neurons at all possible phases of the sensory stimulus , and ( 2 ) granule cells in the feedback pathway respond in a bursty fashion , phase-locked to the AM frequency signal . We also take into account in the model that PFs also synapse onto inhibitory interneurons , which provide some level of inhibition to the SP neuron . It has also been shown that certain long-term plasticity rules may adjust the weights of the parallel fibers . According to recent in vitro experiments , PFs projecting onto SP cells display a long-term depression ( LTD ) rule that depends on the timing of presynaptic and postsynaptic bursts [23] . Such a burst-driven learning rule , combined with the presence of PFs displaying a wide set of temporal delays , is thought to be responsible for the generation of a negative image of the input AMs , providing the substrate for signal cancellation [16] . For local stimulation , the SP neuron is modelled following a leaky integrate-and-fire ( LIF ) formalism ( Eq . 1 ) with an extra term accounting for the bursting dynamics ( DAP ) . Noise was introduced in the system via a low-pass filtered ( with cut-off frequency ) Gaussian noise of zero mean and variance one . The variance is later adjusted via the parameter , and a constant bias is introduced to fit the experimental firing rate in spontaneous ( i . e . no AMs ) conditions . As in the standard LIF model , a spike is recorded when reaches the threshold , and after that remains at a certain resting value during the absolute refractory period of the neuron . The EOD signal arriving at electroreceptors can be described , as a first approach , as a sinewave of amplitude and high frequency ( ) . The presence of stimuli induces EOD amplitude modulations of frequency and contrast , so that the EOD amplitude is also a sinewave given by . Since electroreceptors encode AMs by modulating their firing rate , the AM frequency and the contrast are enough to characterize their behavior . The output firing rate of the P-unit population , which is driven by this input , is given by ( 6 ) P-units are known to display some level of saturation for high contrasts [26] ( this effect is denoted in the above equation as ) , and we observe such saturation in our in vivo recordings via the nonlinear response of the SP cell to different stimulus contrasts . By fitting the model SP cell response to the in vivo measured SP cell response , we determine the input-output amplitude relationship of the P-units ( see Fig . 2A and B ) . The corresponding values are and . When using values other than these ones , linear interpolation was employed to estimate the new values of . To incorporate the effect of P-unit adaptation at low frequencies ( as in [16] ) , we multiply the signal by a small constant factor of for . In addition , as electroreceptor input is strictly excitatory , the input to the SP neuron is rectified and in Eq . 1 symbolizes rectification ( that is , if , and otherwise ) . This aided us in incorporating the rectified nature of the pyramidal cell activity into the model . Values for these parameters are displayed in Table 1 . Superficial cell bursting drives the long-term plasticity rules operating in the PFs of the feedback pathway . The term in Eq . 1 models the depolarizing after-potential ( DAP ) , an injection of current into the soma of the neuron after an action potential is fired due to presence of active channels in the cell's dendrites . This effect has been modeled previously in superficial cells [29] , [30] , and we adapt the parameter values used in these works to match the bursting rate of the model to our experimental observations . The mechanism responsible for the generation of bursting is the following: after the cell fires ( ) at time , it will receive a DAP ( i . e . a small current injection ) a short time later , as long as the previous time the cell fired is not too recent . This additional stimulation is modeled as a difference in alpha functions ( Eq . 8 ) , one generated by the soma voltage , and the other by some mean dendrite voltage . However , if the time interval between this spike time and the previous spike time is less than the refractory period of the dendrite , , then the DAP is inactive for the current spike . The refractory period is modeled as a dynamic variable that changes according to a secondary variable , , which is updated for each spike . The time just after the most recent spike was fired is referred as . The equations governing the DAP [30] are ( 7 ) ( 8 ) ( 9 ) ( 10 ) The parameters used in the above equations are listed in Table 2 . The feedback pathway is initiated by DP cells , which do not exhibit global cancellation behavior since they do not receive feedback , and these cells project onto the nucleus praeminentialis ( nP ) which in turn projects onto EGp granule cells . Finally , granule cells project massive numbers of excitatory PFs to the ELL , where they provide input to SP cells as well as local interneurons ( stellate cells ) . The stellate cells in turn inhibit the SP cells via shunting GABA-A receptor channels . Due to difficulties in recording them , the firing activity of EGp granule cells in the electric fish is not known . Similar granule cells in mammals , however , have been shown to respond to sensory input [43] , [58] and to phase-lock their bursting to sinusoidal input [45] . We assume here , therefore , that the activity of each PF is one burst per stimulus period . Considering the natural distribution of temporal PF delays ( see details on network architecture above ) , the total bursting PF input was assumed to be continuous in time . In the model , the feedback cycle associated with the stimulus cycle was discretized into segments of each . This implies that the number of segments changes with the AM frequency considered ( for instance , we would have feedback segments for a stimulus , and segments for an stimulus ) . Each segment , labeled , becomes active at time , has a global strength ( common for all PFs ) and a synaptic weight ( particular for each PF , see Eq . 2 ) , and then becomes inactive at . Each segment is associated with the activity of a given PF for simplicity , although it could be associated with the activity of a certain set of coincident PFs as well . The total excitatory feedback input is therefore a step-wise continuous and periodic signal given by , for each segment as time moves from segment to segment during a period . Disynaptic inhibition , which is also modulated by PF activity , is modeled as an extra shunting conductance . The global strength of the feedback is driven by DP cells , which in turn are driven by P-units . This implies a dependence of the feedback strength on the P-unit response which is modeled as a dependence of with the contrast : ( 11 ) with being the input/output relationship of the P-units . The parameter is set to for the global stimulus ( in order to fit the mean firing rate measured experimentally at global stimulation of and contrast ) , and to zero for the local stimulus ( since this type of stimulus does not activate the feedback pathway ) . The saturation of the feedback pathway is being taken into account in the parameter , which will be one for low contrasts ( i . e . , and ) and less than one of higher values ( we chose for contrast and for contrast ) . When feedback saturation is not being considered in the model , we just set for all contrasts . Again , since P-units drive feedback , the P-unit adaptation at low AM frequencies discussed above will affect as well the feedback input . Therefore , we follow the same criterion as with and we multiply the whole feedback function by a small factor if to account for P-unit adaptation . A necessary condition for cancellation is to have a stable phase relationship for each segment and , hence , each weight . Such a requirement is fulfilled by considering that there is a particular set of PFs responsible for the cancellation of a given AM frequency . This has been corroborated with in vivo electrophysiological measurements [16] , and therefore we assume here that our feedback pathway is frequency-specific . As also observed experimentally in [16] , cancellation starts to decay at high AM frequencies , hypothetically due to failures of granule cells for bursting at least once per cycle under global stimulation ( and thus failing to drive learning properly ) . In agreement with these observations , we notice a slightly consistent decay of cancellation for , which can be easily taken into account by reducing to a value of for this frequency , to improve the fitting of experimental data . Following the definition of a burst that induces plasticity [23] , the spike train of the model SP cell was constantly monitored for small ( 2 spikes within ) and large ( 4 spikes within ) bursts . These particular definitions of burst are only adopted here to simplify the online computations , as the quantitative behavior of our model does not depend sensitively on such assumptions , or even on the presence of strong intrinsic mechanisms for bursting generation ( see Discussion for details ) . It is worth mentioning here that spikes in each SP burst must be independent ( i . e . there cannot be a small burst in a large burst , or a large and a small burst in 5 spikes ) . Since each PF segment produces a presynaptic burst arriving at the apical dendrite , there is one PF burst at every time in the model , and thus PF bursts are spaced apart . When the SP cell bursts under global stimulation at time , the burst learning rule identified in vitro ( Eq . 3 ) is immediately invoked for all PF segments . To quantify the level of cancellation of the global stimuli , we follow [16] and employ the following criterion: ( 12 ) where are , respectively , the amplitude of SP response ( measured in spikes per second ) for global and local stimulation . More precisely , the PSTH ( i . e . firing rate as a function of stimulus phase ) of the SP cell was fitted to a sinusoidal function ( plus baseline level ) for the global stimulation case , and the amplitude of such sinusoidal was taken as . Because of the rectification , the same fit could not be applied to the local stimulation case , since the response clearly deviates from a sine wave . The SP response to local stimulation was then fitted to a Gaussian distribution ( plus baseline level ) and the height of such a Gaussian was taken as . This criterion was followed for both experimental data and model predictions . The degradation measurement is the complementary of the frequency-averaged cancellation , and it was employed to show , in a clear manner , how much the cancellation level is degraded when increasing the stimulus contrast ( Fig . 1D ) . It is defined as ( 13 ) with is the number of AM frequencies considered in the study ( which are and ) and the sum runs over all these frequencies . | The ability to cancel redundant information is an important feature of many sensory systems . Cancellation mechanisms in neural systems , however , are not well understood , especially when considering realistic conditions such as signals with different intensities . In this work , we study , employing experimental recordings and computational models , a cerebellar-like circuit in the brain of the weakly electric fish which is able to perform such a cancellation . We observe that in vivo recorded neurons in this circuit display a contrast-invariant cancellation of redundant stimuli . We employ a mathematical model to explain this phenomenon , and also to gain insight into several dynamics of the circuit which have not been experimentally measured to date . Interestingly , our model predicts that time-averaged contrast levels of around 15% , which are commonly experienced by interacting fish , would shape the circuit to behave as observed experimentally . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2013 | Learning Contrast-Invariant Cancellation of Redundant Signals in Neural Systems |
Holoprosencephaly ( HPE ) is a failure of the forebrain to bifurcate and is the most common structural malformation of the embryonic brain . Mutations in SHH underlie most familial ( 17% ) cases of HPE; and , consistent with this , Shh is expressed in midline embryonic cells and tissues and their derivatives that are affected in HPE . It has long been recognized that a graded series of facial anomalies occurs within the clinical spectrum of HPE , as HPE is often found in patients together with other malformations such as acrania , anencephaly , and agnathia . However , it is not known if these phenotypes arise through a common etiology and pathogenesis . Here we demonstrate for the first time using mouse models that Hedgehog acyltransferase ( Hhat ) loss-of-function leads to holoprosencephaly together with acrania and agnathia , which mimics the severe condition observed in humans . Hhat is required for post-translational palmitoylation of Hedgehog ( Hh ) proteins; and , in the absence of Hhat , Hh secretion from producing cells is diminished . We show through downregulation of the Hh receptor Ptch1 that loss of Hhat perturbs long-range Hh signaling , which in turn disrupts Fgf , Bmp and Erk signaling . Collectively , this leads to abnormal patterning and extensive apoptosis within the craniofacial primordial , together with defects in cartilage and bone differentiation . Therefore our work shows that Hhat loss-of-function underscrores HPE; but more importantly it provides a mechanism for the co-occurrence of acrania , holoprosencephaly , and agnathia . Future genetic studies should include HHAT as a potential candidate in the etiology and pathogenesis of HPE and its associated disorders .
Holoprosencephaly ( HPE ) is a congenital malformation resulting from the failure of the forebrain to divide into left and right hemispheres [1] , [2] . HPE occurs at a frequency of 1 in 10 , 000–20 , 000 live births , although affected embryonic individuals are thought to be as high as 1 in 250 pregnancies , making it the most common brain anomaly in humans [3] , [4] . Within the clinical spectrum of HPE , a broad range of brain malformations are observed . Alobar holoprosencephaly is the most severe form , and in this case hemisphere bifurcation completely fails to occur resulting in the forebrain developing as a single holosphere together with a single cyclopic eye [5] . In milder instances such as lobar holoprosencephaly , near complete hemisphere separation occurs but cortical structures are hypoplastic and specific brain nuclei remain congruous [5] . It has long been recognized that the face predicts the brain [6] and HPE manifests with a range of craniofacial anomalies in humans . In extreme cases of HPE , cyclopia and a nasal proboscis can occur [3] , [7] . In very mild forms , such as cebocephaly , only a single nostril or incisor might be present and the philtral ridges may be absent [8] . Thus phenotypically , HPE is a heterogeneous disorder and this is also true etiologically . Exposure to environmental teratogens such as alcohol [9] , [10] and retinoids [9] can result in HPE phenotypes . Gestational diabetes is also a factor as 1–2% of newborn infants of diabetic mothers exhibit HPE [11] . Genetically , HPE is similarly heterogeneous and is currently associated with mutations in at least 12 different loci encompassing multiple signaling pathways such as BMP , NODAL , ZIC , SIX , and SHH [4] . What is common amongst many of the loci and signaling pathways is that they play important roles in the development of the ventral brain and midline structures of the embryo . This is particularly true for Sonic Hedgehog ( SHH ) signaling . Shh is a member of the Hedgehog ( Hh ) family of signaling molecules that also includes Indian ( Ihh ) and Desert Hedgehog ( Dhh ) . Each Hh is a secreted glycoprotein that undergoes autoproteolytic cleavage and dual lipid post-translational modification to generate its proper active form . Autoproteolytic cleavage of Hh precursor molecules generates an N-terminal fragment ( Hh-N ) referred to as the mature form . Hh-N is then modified via the addition of a cholesterol moiety to its C-terminus [12] , [13] , followed by addition of a palmitoyl moiety to its N-terminus [14] . These lipid modifications are required for Hh protein multimerisation , distribution and activity . To date , mutational analyses of human HPE , together with naturally occurring and engineered mouse mutants , have identified the genetic lesions responsible for only about 20% of individuals with HPE . Hence it is critical to identify additional candidate genes for the majority of patients whose genetic lesions remain unknown . Furthermore , HPE is a heterogeneous disorder and often found in patients together with other malformations such as acrania , anencephaly and agnathia and it is not known if these phenotypes arise through a common etiology and pathogenesis . Here we describe the AP2-Cre ( “Creface” ) mouse [15] as an insertional mutation in Hhat , and define Hhat as a novel HPE associated gene which can mechanistically explain the co-occurrence of HPE together with acrania and agnathia .
“Creface” ( AP2-Cre ) is a transgenic line of mice [15] in which nuclear localizing Cre recombinase is driven by a specific TFAP2A enhancer element [16] . We discovered that interbreeding heterozygous Creface+/T mice failed to generate any post-natal viable homozygous CrefaceT/T animals . Therefore we investigated the etiology and pathogenesis of the CrefaceT/T mutant phenotype during embryogenesis . Morphological abnormalities in CrefaceT/T embryos are readily identifiable as early as E9 . 5 . In contrast to control littermates , CrefaceT/T embryos exhibited smaller telencephalic hemispheres together with diencephalic and mesencephalic hypoplasia ( Figure 1A , 1B ) . Pax6 expression demarcates the telencephalon and prosomere ( P ) territories 1 and 2 of the diencephalon and in situ hybridization analyses with Pax6 revealed the specific absence of P2 as well as abnormal neural morphology in E9 . 5 CrefaceT/T embryos ( Figure 1A–1F ) . Pax6 also labels the optic placode and interestingly , although present , the optic vesicles are displaced ventrally and medially in CrefaceT/T embryos ( Figure 1C , 1D , 1G , 1H ) . At later stages of gestation , the forebrain in CrefaceT/T embryos often lacked a ventricular canal and instead persisted as a single–lobed or incompletely bifurcated neuroepithelium . In contrast , control littermates , displayed bifurcated hemispheres surrounding the forebrain ventricle ( Figure 1I , 1J ) . Ocular anomalies in CrefaceT/T embryos manifested as microphthalmia but in addition , the eye often remained embedded in grossly disorganized brain tissue and the lack of contact with the surface ectoderm resulted in a failure to form tissues such as the cornea ( Figure 1K , 1L ) . E10 . 5 CrefaceT/T mutant embryos are noticeably smaller in size than control littermates and exhibit more prominent craniofacial abnormalities ( Figure 2A , 2B ) . In particular , the frontonasal region of the embryo as defined by the medial nasal prominences and spacing between the bilateral nasal slits is dramatically reduced in size to the extent that only a single slit is present in CrefaceT/T mutant embryos ( Figure 2C , 2D ) . Craniofacial anomalies in CrefaceT/T embryos are not limited to the brain and frontonasal region as the maxillary and mandibular components of the first pharyngeal arch are also hypoplastic at E9 . 5–10 . 5 ( Figure 2E , 2F ) . This manifests in E14 . 5 mutant embryos as a narrow protruding midface , together with more severely pronounced maxillary and mandibular hypoplasia ( Figure 2G , 2H ) . In addition to craniofacial defects , mutant embryos exhibited limb defects including oligodactyly ( Figure 2G , 2H and data not shown ) . E14 . 5 CrefaceT/T embryos displayed considerable edema with the outer layer of skin being displaced from the body cavity , most likely due to defects in lymphatic development ( Figure 2G , 2H ) . Large areas of blood pooling were also often observed in the anterior region of the embryos , which may be indicative of more general vascular anomalies . These lymphatic and vascular anomalies progressively worsened coinciding with embryonic lethality prior to birth . In the current analysis we concentrated on the molecular and structural changes associated with the CrefaceT/T craniofacial defects . In control embryos Eya2 bilaterally demarcates the nasal placode ectoderm ( Figure 3A , 3C , 3E ) while Six2 is expressed bilaterally in the mesenchyme of each medial nasal prominence ( Figure 3G , 3I ) . In E9 . 5–10 . 5 CrefaceT/T mutants , there is a single continuous central domain of placodal Eya2 activity ( Figure 3B , 3D , 3F ) , while Six2 expression is absent from midline tissues ( Figure 3H , 3J ) . This is consistent with frontonasal agenesis , nasal placode fusion and a single nasal pit/slit in CrefaceT/T mutant embryos ( Figure 2C , 2D ) . We next examined the signaling molecules Fgf8 and Bmp4 , which are known to regulate craniofacial development [17] . At E10 . 5 , Fgf8 normally labels the epithelium flanking the nasal pits almost uniformly ( Figure 4A , 4C ) , while Bmp4 marks only specific ventral domains of the nasal prominences ( Figure 4E , 4G ) . However , CrefaceT/T mutants display single continuous domains of epithelial Fgf8 and Bmp4 expression ( Figure 4B , 4D , 4F , 4H ) . These findings are also consistent with frontonasal agenesis , nasal placode fusion and a single nasal pit/slit in CrefaceT/T mutant embryos . To characterize any defects in skeletogenesis in CrefaceT/T mutants , we stained embryos at E15 . 5 and 17 . 5 ( Figure 5 ) with Alcian blue and Alizarin red to label cartilage and bone , respectively . At E15 . 5 control embryos exhibited considerable domains of differentiated cartilage within the head , particularly within the developing cranium , ear and nasal regions as evidenced by extensive blue tissue staining ( Figure 5A ) . Ossification was clearly evident in multiple elements including the frontal and parietal bones but was particularly well advanced in components of the viscerocranium including the premaxillary , maxillary and dentary bones . In marked contrast , E15 . 5 CrefaceT/T littermates demonstrated a complete absence of Alizarin red stained ossified bone within the skull and jaw ( Figure 5B ) . Mutant embryos also displayed considerably diminished chondrogenesis of calvarial , nasal and otic mesenchyme ( Figure 5B ) . Not only were cranial elements such as the nasal cartilage hypoplastic or missing , but staining of cartilage in the vertebral column was also absent . Section histology revealed that compared to control littermates , which have bifurcated continuous nasal cavities separated by a nasal septum , E14 . 5 CrefaceT/T embryos lacked a nasal septum and possessed only a single yet discontinuous nasal cavity ( Figure 5E , 5F ) . In addition , although the palatal shelves were readily identifiable adjacent to the tongue in control embryos , this was not the case in CrefaceT/T littermates , and is indicative of a defect in vertical extension and medial growth of the palatal shelves toward the midline ( Figure 5G , 5H ) . The oral cavity was considerably narrower in mutant embryos and the tongue was hypoplastic . By E17 . 5 , cranioskeletal mineralization was almost complete in control embryos . The frontal , parietal , interparietal , supraoccipital , exoccipital and temporal bones that contribute to the neurocranium are nearly fully ossified as were the nasal , premaxilla , maxilla , jugal and dentary bones within the viscerocranium ( Figure 5C ) . In contrast CrefaceT/T littermate embryos exhibited acrania as the frontal parietal , interparietal and supraoccipital , bones were all completely absent ( Figure 5D ) . The nasal cartilage that was present was a tube-shaped structure that was devoid of ossification . The basioccipital and exoccipital bones that constitute the base of the skull were present , but were dramatically reduced in size and abnormally shaped . E17 . 5 CrefaceT/T embryos also display agnathia as we observed the presence of only rudimentary premaxilla , maxilla and dentary bones . The dentary bones lacked all processes including the condyle , angular and coronoid processes . Furthermore CrefaceT/T embryos also exhibited disrupted tooth development as incisor and alveolar molar tooth primordia which were readily identifiable in control embryos , were absent from mutant littermates ( data not shown ) . Given the extensive nature of cranioskeletal anomalies in CrefaceT/T embryos , we characterised their specific pathogenesis in more detail . During endochondral ossification proliferating chondrocytes at distal ends of a skeletal element undergo hypertrophy and dramatically increase in size . As development proceeds , hypertrophic chondrocytes undergo apoptosis and are replaced by invading osteoblasts ( reviewed in [18] ) . Sagittal sections through the exoccipital and basioccipital bones of E15 . 5 Creface+/T embryos revealed the presence of proliferating chondrocytes at the distal end of the element ( Figure 5I , 5K ) . These cells were located adjacent to a domain of prehypertrophic chondrocytes which were in turn were flanked by noticeably larger hypertrophic chondrocytes positioned centrally within each element . In contrast , sections of CrefaceT/T mutants revealed a significant perturbation in chondrocyte development . The skeletal elements were reduced in size and this appeared to be associated with a diminished zone of proliferating chondrocytes ( Figure 5J , 5L ) . The constellation of craniofacial abnomalities observed in CrefaceT/T embryos is consistent with acrania-holoprosencephaly-agnathia . Holoprosencephaly and limb anomalies including digit oligodactyly are classic features of perturbed Hh signaling during early embryogenesis . We therefore hypothesized that the AP2-Cre transgene used to generate Creface mice , inserted into or interfered with a locus important for Hh signaling . However , to rule out the possibility that the insertion occurred in Hh signaling genes previously associated with HPE , we initially performed complementation tests by intercrossing Creface+/T mice with Shh+/− [19] and Ptch1+/− [20] mice . None of the Creface+/T;Shh+/− , or Creface+/T;Ptch1-LacZ+ embryos harvested at E17 . 5–18 . 5 displayed craniofacial anomalies consistent with HPE , acrania or agnathia ( data not shown ) . This indicated that the CrefaceT/T phenotype was not the result of transgene integration into Shh , or Ptch1 genomic loci . Given that the genetic lesions responsible for HPE have only been identified in about 20% of affected individuals , there is considerable interest in discovering new genes that contribute to its etiology and pathogenesis . This is particularly true where HPE is a subcomponent of more complex syndromes . Therefore , using a PCR-based method for DNA walking and mapping known as Vectorette , we determined that the Creface transgene had integrated into intron 9 of Hedgehog acyltransferase ( Hhat ) at nucleotide position 22949 ( Figure S1A ) . To further verify that Hhat was indeed the gene disrupted , we genotyped E10 . 5 embryos obtained from intercrosses of Creface+/T mice using control and transgene-specific reactions . Genotyping of the endogenous Hhat intron 9 region produced a 1 . 4 kb DNA band from both wild-type and heterozygous ( Creface+/T ) embryos , but did not produce a band from mutant ( CrefaceT/T ) embryos as expected ( Figure S1B ) . Furthermore , using a primer designed to recognize the Creface:Hhat fusion , this enabled amplification of an integration specific 250 bp fragment from Creface+/T heterozygous and CrefaceT/T mutant embryos but not from wild-type embryos ( Figure S1B ) . Collectively , these results confirmed that the Creface transgene integrated within intron 9 of Hhat . It is well recognized that transgenes integrate into host genomes as concatamers and can disrupt endogenous gene expression [21]–[23] . Consistent with this we were unable to RT-PCR amplify full length Hhat using RNA obtained from CrefaceT/T mutant embryos , in contrast to wild-type and Creface+/T heterozygous embryos ( Figure S1C ) . To examine the expression of Hhat in control and mutant embryos , we generated a riboprobe corresponding specifically to exons 8–10 of Hhat ( Figure 6A ) . Through in situ hybdrization of E9 . 5–10 . 5 embryos followed by sectioning , we observed that Hhat is expressed in and around the notochord ventral to the neural tube in wild-type embryos . In contrast , the level of Hhat expression was considerably diminished or absent in CrefaceT/T mutant littermate embryos ( Figure 6B , 6C ) . This indicates that Hhat activity is disrupted through Creface transgene insertion . These data clearly validate that integration of the Creface transgene physically disrupts Hhat rendering the gene non-functional , which results in the morphological defects characteristic of CrefaceT/T mutant mice . Creface+/T heterozygous and CrefaceT/T homozygous mice will herein be referred to as Hhat+/Creface and HhatCreface/Creface respectively . Hhat encodes an acyltransferase that palmitoylates Hh proteins [14] . This fatty acid modification increases the potency of Hh signaling in vitro [24] and is also required for long-range Hh signaling in vivo [25] . Consistent with this , Drosophila mutants lacking Hh palmitoylation exhibit patterning defects in Hh-responsive cells [26]–[29] . We therefore hypothesized that Hhat loss-of-function should disrupt the palmitoylation of Shh and perturb its signaling effects in HhatCreface/Creface embryos . To test this , we generated mouse embryonic fibroblast ( MEFs ) from control and HhatCreface/Creface embryos , transfected each with a Shh expression vector , and incubated them with palmitic acid for 12 hours . Incorporated palmitic acid was then tagged with biotin . Protein extraction , followed by western blotting with an anti Shh antibody and alkaline phosphatase streptavidin staining , confirmed that despite the presence of similar levels of total Shh protein , the degree of palmitoylation in HhatCreface/Creface MEFs was considerably diminished compared to controls ( Figure 6D ) . This clearly demonstrates that the functionality of Hhat is compromised in HhatCreface/Creface embryos . Our expectation from Hhat loss-of-function in concert with the holoprosencephaly phenotype was that the lack of palmitoylation should perturb the spatiotemporal activity of Shh signaling throughout HhatCreface/Creface embryos . Hence we initially examined the expression of Shh via in situ hybridization in control and mutant embryos . Compared to control littermates , Shh was expressed in the notochord in E8 . 5 and E9 . 5 HhatCreface/Creface embryos albeit at slightly reduced levels but was absent from the ventral telencephalon and floor plate ( Figure 7A–7F , 7I–7L ) . Shh expression was also absent from the branchial arch ( BA ) ectoderm and pharyngeal endoderm of E10 . 5–11 . 5 HhatCreface/Creface embryos ( Figure 7G , 7H ) . Shh activity in tissues such as the ventral forebrain and branchial arch ectoderm is induced by and dependent upon prior expression of Shh in the notochord and endoderm respectively [30] , [31] . Thus the alterations to Shh expression in HhatCreface/Creface embryos were indicative of specific disruptions to Shh signaling gradients as a consequence of Hhat loss-of-function and lack of Shh palmitoylation . To confirm this , we characterized the distribution of Shh protein in E9 . 5 embryos ( Figure 8 ) . Control embryos exhibited Shh activity in the ventral telencephalon , branchial arch ectoderm , pharyngeal endoderm , notochord and floor plate ( Figure 8A , 8C , 8E , 8G ) . However , in HhatCreface/Creface embryos , Shh protein was absent from all these sites with the exception of the notochord ( Figure 8B , 8D , 8F , 8H ) . These findings are consistent with our observations of Shh expression via in situ hybridization ( Figure 7 ) and correlate with the localization of Hhat activity . Thus our results indicate that localized tissue specific production of Shh can occur in HhatCreface/Creface embryos , however the failure to induce secondary domains of activity suggests a disruption in long-range Shh signaling gradients as a function of perturbed palmitoylation . Ptch1 is a receptor for Hh ligands and its activity is widely used as a read out of the gradient and range of Hh signaling . Therefore to better document the degree of spatial perturbation of Hh signaling in HhatCreface/Creface embryos , we outcrossed Hhat+/Creface mice to Ptch1-LacZ mice [20] and used the LacZ reporter as a measure of Ptch1 activity . E9 . 5–11 . 5 Hhat+/Creface;Ptch1-LacZ+ embryos exhibit intense LacZ expression in the ventral telencephalon ( arrows ) , pharyngeal endoderm , notochord ( arrowheads ) and surrounding mesenchyme as well as in the floorplate ( Figure 9A , 9C , 9E , 9G , 9I , 9K ) . Often , a gradient of Ptch1 activity is present within or adjacent to these sites of Shh synthesis , which reflects the extensive range of Hh signaling in each of these regions ( Figure 9I , 9K ) . In contrast , Ptch1-LacZ activity is considerably reduced in HhatCreface/Creface embryos ( Figure 9B , 9D ) . More specifically these mutant embryos lack LacZ expression in the ventral telencephalon , branchial arch ectoderm , pharyngeal endoderm and floor plate ( Figure 9F , 9H , 9J , 9L ) . LacZ activity in the mesenchyme surrounding the notochord is present in HhatCreface/Creface mutants , but is considerably reduced compared to control littermates ( Figure 9I–9L ) . These results specifically highlight the spatiotemporal diminishment of the gradient and range of Shh activity in HhatCreface/Creface embryos . Hhat is required for the palmitoylation of all Hh proteins , not just Shh . Furthermore the cartilage and bones which are disrupted in HhatCreface/Creface embryos are dependent upon the actions of both Shh [19] and Indian hedgehog ( Ihh ) [32] . Consistent with this Shh−/− mutant embryos exhibit agenesis of the calvarial bones , while loss of Ihh results in reduction of cranial bone size and all markers of osteodifferentiation during endochondral as well as membranous ossification [33] . Therefore we also documented the activity of Ptch1 during ossification to determine if global Hh signaling was impaired during cranioskeletal differentiation and if this also contributed to the pathogenesis of craniofacial anomalies in HhatCreface/Creface embryos . In sections of E17 . 5–18 . 5 control embryos , strong Ptch1-LacZ activity was observed in the perichondrium and chondrocytes within cranioskeletal elements such as the exoccipital and basioccipital bones ( Figure 9M , 9O ) . In contrast , Ptch1-LacZ activity was apparently absent from the exoccipital bone in HhatCreface/Creface;Ptch1-LacZ+ mutants ( Figure 9N ) and was considerably reduced in the basioccipital bone ( Figure 9P ) . The disruptions in Ptch1 activity are indicative of a global diminishment of Hh signaling in HhatCreface/Creface embryos . Our results therefore demonstrate a direct in vivo requirement for Hhat in establishing Hh signaling gradients during early and late stages of embryogenesis . Thus perturbation of Hh signaling directly underpins the etiology of cranioskeletal defects in HhatCreface/Creface embryos . HhatCreface/Creface embryos display decreased Shh signaling in the ventral telencephalon and branchial arches from E8 . 5–10 . 5 . Temporally , this coincides with cranial neural crest cell migration and colonisation of the frontonasal , maxillary and mandibular facial prominences . Neural crest cells generate most of the cartilage , bone and connective tissue in the head and face [34] , [35] and Shh is known to be required for their proper migration and survival [36]–[39] . We hypothesized that defects in neural crest cell development could therefore be a major contributing factor to the pathogenesis of cranioskeletal anomalies in HhatCreface/Creface embryos . Hence , we compared the formation , migration and survival of neural crest cells in control and HhatCreface/Creface embryos . No obvious defects in the initial specification of cranial neural crest cells were observed from in situ hybridization analyses with general neural crest cell markers such as Snail1 and Crabp1 ( Figure S2A–S2H ) , or more lineage specific markers such as Sox9 and Sox10 ( Figure S2I–S2P ) . Additionally , we performed lineage tracing analyses using Wnt1-Cre together with Rosa26R mice to permanently label neural crest cells . In E9 . 5–10 . 5 control embryos , streams of LacZ positive neural crest cells migrated into and colonized the developing frontonasal prominences as well as the pharyngeal arches ( Figure S3A , S3B ) . In HhatCreface/Creface;Wnt1-Cre+;R26R+ embryos LacZ positive neural crest cells were present in similar patterns to control littermates , further demonstrating that there was no significant defect in neural crest cell formation , migration or colonization of the facial prominences and pharyngeal arches in HhatCreface/Creface embryos ( Figure S3C , S3D ) . However , the patterns of LacZ staining highlighted the hypoplasia of medial , lateral , maxillary and mandibular facial prominences in HhatCreface/Creface embryos , and also revealed gross anomalies in development of the cranial ganglia as the trigeminal appeared to be hypoplastic , and the hypoglossal and vagal ganglia were fused ( Figure S3C , S3D ) . Cranial ganglia form part of the peripheral nervous system and are derived from both neural crest and sensory placode cells [40] . The gross cranial ganglia defects observed via lacZ staining in E10 . 5 HhatCreface/Creface;Wnt1-Cre+;R26R+ embryos were confirmed through anti-neurofilament ( 2H3 ) immunostaining ( Figure S4 ) . Not only were the cranial ganglia and in particular the trigeminal hypoplastic , but interestingly , immunostaining more discretely revealed that the maxillary branch of the trigeminal was consistently narrower in HhatCreface/Creface embryos compared to control littermates ( Figure S4A , S4D ) . This correlates with maxillary hypoplasia in mutant embryos ( Figure 2A–2D ) . In addition , the hypoglossal and vagal ganglia are aberrantly fused and the oculomotor nerve which is readily identifiable in control embryos is missing in HhatCreface/Creface littermates . The dual origin of cranial ganglia from neural crest and ectodermal placode cells prompted us to examine whether defects in development of either progenitor population underpinned the ganglia defects in HhatCreface/Creface mutants . E10 . 5 embryos were processed via in situ hybridization for Sox10 and Eya2 , which demarcate neurogenic neural crest and sensory placode cells respectively . Although the spatiotemporal patterns of Sox10 and Eya2 expression were quite similar between control and mutant embryos , the domains of activity highlighted the hypoplasia of the trigeminal ganglia and fusion of the hypoglossal and vagal ganglia in HhatCreface/Creface embryos ( Figure S4B , S4C , S4E , S4F ) . Collectively , these results indicate that Hhat loss-of-function and disrupted Hh signaling does not globally affect the induction or migration of neural crest and sensory placode cells . However , hypoplasia and aberrant fusion of the cranial ganglia as evidenced by both neural crest ( Sox10 ) and sensory placode ( Eya2 ) markers suggested that Hh signaling plays a regionalized role in the development and patterning of both progenitor cell populations . Hypoplasia of the facial prominences together with the trigeminal ganglia , suggested that Hhat - through its effects on mediating the gradient and range of Hh signaling - may play a critical role in progenitor cell survival . Consistent with this idea , Shh has been shown to be critical for neural crest cell survival during craniofacial morphogenesis [36] , [37] , [41] . Therefore we examined whether there were any alterations in progenitor cell survival in HhatCreface/Creface embryos by characterizing the spatiotemporal differences in cell death in control versus mutant embryos . Using the apoptotic marker cleaved-Caspase3 , we observed a considerable increase in apoptotic cells in the neural crest cell derived frontonasal , maxillary , mandibular and prospective palatal mesenchyme of E9 . 5–10 . 5 HhatCreface/Creface embryos compared to control littermates ( Figure 10A–10H ) . In contrast , we did not observe any significant differences in the number of proliferative cells using the marker phospho-Histone 3 ( data not shown ) . Our data demonstrates that Hhat loss-of-function perturbs Hh signaling during early embryogenesis resulting in increased apoptosis in neural crest cell derived craniofacial mesenchyme . This underscores hypoplasia of the facial prominences during early embryogenesis and together with defects in differentiation , subsequently contributes to the characteristic acrania-holoprosencephaly-agnathia malformations observed in HhatCreface/Creface embryos . In addition to regulating cell survival , Hh signaling also plays important roles in governing branchial arch patterning . In fact , branchial arch ectoderm-derived Shh signaling , regulates downstream targets such as Fgf8 and Bmp4 , which respectively influence proximal versus distal pharyngeal arch and jaw patterning . Fgf8 is normally expressed in the proximal maxillo-mandibular ectoderm of E9 . 5–10 . 5 embryos and is flanked ( Figure 4A , 4C ) distally within the mandible by an adjacent ectoderm domain of Bmp4 ( Figure 4E , 4G ) . Compared to control littermates , Fgf8 ( Figure 4B , 4D ) and Bmp4 ( Figure 4F , 4H ) expression were absent from the first pharyngeal arch ectoderm of E10 . 5 HhatCreface/Creface embryos demonstrating their dependence on Hh signaling . Both Bmp and Fgf signaling are known to play critical temporal and tissue context dependent roles in cell proliferation and survival . Hence we examined the extent of their respective roles in the pathogenesis of craniofacial anomalies in HhatCreface/Creface mutants during early embryogenesis . Bmps transduce signals by binding to complexes of type I and II serine/threonine kinase receptors which then activate canonical signaling via receptor Smads ( R-Smads ) 1 , 5 and 8 . Immunostaining with phospho-SMAD1/5/8 revealed no obvious alterations in SMAD signaling in the frontonasal , maxillary or mandibular mesenchyme of E10 . 5 HhatCreface/Creface embryos compared to control littermates ( data not shown ) . The lack of any significant change in SMAD signaling is likely due to redundancy with other Bmp signals such as Bmp7 and furthermore is consistent with previous analyses that conditionally deleted Bmp4 from the branchial arch ectoderm [42] . In contrast , FGF signaling functions through membrane bound receptors which activate downstream effectors , such has mitogen-activated protein kinase ( MAPK ) /extracellular signal-regulated kinase ( ERK ) . We observed a considerable decrease in both the domain and level of phosphorylated Erk1/2 staining within the frontonasal , maxillary and mandibular mesenchyme of HhatCreface/Creface mutant embryos compared to control littermates ( Figure S5A , S5B ) . This indicates that diminished Erk1/2 signaling impacts upon neural crest cell survival and contributes to hypoplasia of the facial prominences underpinning the pathogenesis of craniofacial anomalies in HhatCreface/Creface embryos . Consistent with this , conditional deletion of Erk1/2 in neural crest cells results in extensive agenesis of the neural crest cell derived craniofacial skeleton ( P . A . T . unpublished ) . Furthermore , activation and repression of Shh signaling has been shown to respectively increase and decrease the levels of phosphorylated Erk1/2 in vitro [43] . Taken together with our in vivo data , this collectively highlights the vital role played by Hh-Fgf-Erk signaling in promoting neural crest cell survival and preventing apoptosis during normal craniofacial morphogenesis and in the pathogenesis of craniofacial anomalies in HhatCreface/Creface embryos .
SHH was the first locus mapped in association with HPE in affected patients [1] , [2] and knockouts of Shh in mice recapitulate the characteristics of HPE observed in human individuals . Mutations in Hh signaling pathway members , such as Patched1 ( Ptch1 ) and Smoothened ( Smo ) also result in HPE [4] . Collectively , this illustrates the importance of Hh signaling in embryonic craniofacial development and in the etiology of HPE . To date , mutational analyses of human HPE together with naturally occurring and engineered mouse mutants have identified the genetic lesions responsible for only about 20% of the cases of HPE . Hence it is critical to identify additional candidate genes for the majority of patients whose genetic lesions remain unknown . Here we describe the AP2-Cre ( “Creface” ) mouse [15] as an insertion mutation in Hhat . HhatCreface/Creface embryos exhibit holoprosencephaly; extensive apoptosis in the craniofacial mesenchyme; frontonasal , mediolateral , maxillary and mandibular prominence hypoplasia; patterning defects in the facial prominences and cranial ganglia; and diminished chondrogenesis and osteogenesis . Collectively these anomalies contribute to midfacial hypoplasia and agenesis of the jaw . In addition to holoprosencephaly and agnathia , HhatCreface/Creface mutant embryos lack all but the ventral bones of the skull . We observed complete agenesis of the nasal , frontal , parietal , and interparietal bones amongst others , while the basioccipital , exoccipital , and basisphenoid bones were all severely hypoplastic . Thus we have identified HhatCreface/Creface as a novel mouse model of acrania-holoprosencephaly-agnathia . These phenotypes are consistent with perturbation of Hh signaling . Hh proteins are secreted glycoproteins that undergo autoproteolytic cleavage to generate their active forms . However dual cholesterol and palmitoyl post-translational lipid modification are also critical for proper Hh protein multimerization , distribution and activity [12]–[14] . Hhat encodes an acyltransferase and our results demonstrate that Hhat loss-of-function results in diminished palmitoylation of Shh . In support of our observations , Skinny hedgehog ( Skn ) which is also known as Hhat , was recently shown to catalyse the palmitoylation of all Hh proteins in invertebrates such as Drosophila [27] , and mammals such as mice [25]–[29] . Furthermore , Drosophila mutants lacking Hh palmitoylation , exhibit patterning defects in Hh-responsive cells during wing and eye development , while limb development and spinal cord neurogenesis are disrupted in Skn mutant mice [25]–[29] . In agreement with this , HhatCreface/Creface mutants exhibit a diminished gradient and range of Hh signaling as early as E9 . 5 as evidenced by the specific spatiotemporal loss of Hh production and downregulation of Ptch1 . Neither Shh protein nor Ptch1-lacZ reporter activity could be detected in the ventral forebrain , floor plate or pharyngeal arch ectoderm of HhatCreface/Creface embryos . During normal embryogenesis , Shh expression and subsequent Ptch1 activity in these tissues is induced by or dependent upon gradients of Shh emanating from other tissues such as the notochord and pharyngeal endoderm [30] , [31] , [38] , [44] . Although some localized and possibly short-range Hh signaling remained intact , our data indicates that long-range Hh signaling gradients fail to be properly established during mammalian embryogenesis in the absence of Hhat . Thus the absence of palmitoylation prevents the formation of Hh multimeric complexes , subsequently limiting establishment of a Hh gradient . Therefore our analyses of HhatCreface/Creface mouse embryos highlight the conserved requirement for Hhat in establishing Hh signaling gradients in vivo during mammalian embryogenesis and in particular during craniofacial development . Thus Hhat plays a critical role in the regulation of Hh signaling during embryogenesis . Hh signaling performs a number of key roles during craniofacial development . Hh is required to maintain the viability of neural crest cells [37] during their colonization of the facial prominences and also regulates patterning of the frontonasal region and pharyngeal arches [45] , [46] . Interestingly , Fgf8 is expressed in the proximal pharyngeal arch ectoderm [47] , while Bmp4 is expressed in the distal ectoderm and underlying mesenchyme [48] . Fgf8 and Bmp4 are critical regulators of jaw development as the loss of either gene results in specific jaw defects . Conditional deletion of Fgf8 from the pharyngeal ectoderm results in the absence of molar teeth and most of the chondrocranial and dermatocranial elements that form the proximal jaw [49] , [50] . In contrast , conditional inactivation of Bmp4 in the mandibular ectoderm results in agensis of incisor teeth together with severe distal truncations of the jaw [42] . Thus , the jaw is patterned in a proximo-distal manner through Fgf and Bmp activity respectively [42] , [50] . HhatCreface/Creface embryos exhibit a complete absence of Fgf8 and Bmp4 expression in the pharyngeal arch ectoderm which demonstrates these genes are downstream targets of Hh signaling . Consistent with this idea , Fgf8 and Bmp4 expression is similarly diminished in Shh loss-of-function embryos [39] , [51] and also as a consequence of pharyngeal endoderm ablation in chick embryos [44] . Taken together , this data indicates that Shh signaling from the pharyngeal endoderm is required for activation of Shh in the pharyngeal ectoderm and that this domain of activity in turn is essential for ectodermal Fgf8 and Bmp4 signaling during jaw development . Therefore , the presence of agnathia and anodontia observed in HhatCreface/Creface embryos correlates with the loss of Fgf8 and Bmp4 activity in the pharyngeal arch ectoderm . Fgf8 and Bmp4 , both play critical roles in neural crest cell and craniofacial mesenchyme survival [42] , [49] and examination of Erk1/2 and Smad1/5/8 activity as downstream readouts of Fgf and Bmp signaling respectively , indicated that Fgf signaling was primarily affected in HhatCreface/Creface embryos . Interestingly , Shh is known to be required for the viability of neural crest cells [37] and activation and repression of Hh signaling in vitro increases and decreases Erk1/2 activity respectively [43] . Our analyses therefore suggest that Hh signaling acts through Fgf-Erk to promote neural crest cell viability and facial prominence outgrowth during craniofacial development . Consistent with this , Hhat is expressed at low but consistent levels throughout the facial prominences between E10 . 5–12 . 5 [52] . Shh loss-of-function embryos exhibit a reduction in the overall size of the brain , a single optic vesicle with agenesis of external eye structures , nasal proboscis and malformations of the craniofacial skeleton . Shh mutants also exhibit agenesis of all the calvarial bones irrespective of their neural crest or mesoderm origins [19] . The similarity in phenotypes between Shh−/− embryos and HhatCreface/Creface mutants suggests that loss of Shh signaling in HhatCreface mutants may also primarily account for the extensive defects in cranial vault . However , Ihh also plays an important role in craniofacial development during late gestation . Loss of Ihh results in reduced chondrocyte proliferation , an absence of bone calcification , hypoplasia of the cranial bones and diminished markers of osteodifferentiation during endochondral as well as membranous ossification [32] . [33] . Although there are three members of the Hh gene family , neither Ihh nor Dhh is appreciably expressed in craniofacial tissues prior to E12 . 5 [36] , [53] , [54] . Thus , whereas early neural crest cell survival and craniofacial morphogenesis depends primarily upon Shh signaling , later craniofacial differentiation and maturation requires both Shh and Ihh . Therefore during early embryogenesis , perturbation of Shh signaling is likely to be primarily responsible for apoptosis in the craniofacial mesenchyme and defects in facial prominence and pharyngeal arch patterning and outgrowth in HhatCreface/Creface mutants . Consistent with this idea , perturbation of Hh signaling in neural crest cells through conditional deletion of Smo has no effect on neural crest migration but elicits a profound effect on the formation of skeletal and non-skeletal elements of the head [36] . Thus the combination of defects in patterning of the facial prominences , extensive apoptosis in the neural crest cell derived craniofacial mesenchyme and altered skeletogenic differentiation , underpins the craniofacial malformations in HhatCreface/Creface embryos . In summary , we have identified HhatCreface/Creface mice as a novel model of acrania-holoprosencephal-agnathia . Hhat palmitoylates Hh proteins and is critical for establishing proper gradients of Hh signaling during embryogenesis . Our work therefore highlights the importance of long-range Hh signaling in craniofacial development and suggests HHAT is a potential candidate in the etiology and pathogenesis of HPE and its associated congenital human disorders .
Mice were housed in the Laboratory Animal Services Facility at the Stowers Institute for Medical Research according to IACUC animal welfare guidelines . Hhat+/Creface were maintained on a CD1 background; genotyping of the Hhat+/Creface mice was performed as described below . The Patched1 ( Ptch1 ) -LacZ [20] , Rosa 26 Reporter ( R26R ) [55] , Shh [19] , and Wnt1-Cre [56] , [57] mice were maintained as previously reported . For embryo collection , the day of plug was noted as embryonic ( E ) day 0 and control embryos described in the analyses were either wild-type or heterozygous littermates . A minimum of 4 embryos were used for each assay performed unless otherwise stated . Embryo morphology was visualized by staining whole embryos with DAPI to label all cell nuclei and imaging fluorescent signal . Embryos were fixed in 4% paraformaldehyde overnight at 4°C then stained with 2 ug/ml DAPI in PBS and imaged by confocal microscopy . Scale bars designate magnification of embryos however in some cases may be a close approximation . The insertion location of the Creface transgene was identified using the Vectorette kit ( Sigma , St . Louis , MO ) . A Creface;Vectorette DNA library was created using the EcoRI-Vectorette unit according to manufacturer's instructions . To amplify clone DNA containing the transgene and surrounding genomic region , the primer ( 5′-ACA TCT GGG GTG AAG GGA ATT AGG GAG TTG-3′ ) was used in addition to the Vectorette-specific primer to amplify DNA bands containing the integration site . A step-down PCR was used according to manufacturer's instructions; a 5 kb band , clone E1N3 , was gel-purified ( Gel Extraction Kit , Qiagen , Valencia , CA ) and sequenced using the Vectorette sequencing primer . The blasted sequence aligned to intron 9–10 of the Hhat gene , indicating Hhat as the gene disrupted in Creface mutants . Verification of transgene insertion site was performed using PCR in wild-type , heterozygous and mutant genomic DNA from embryos at E10 . 5 . Amplification of the region spanning the insertion site used the transgene-specific primer ( 5′-TGG TTA CCT TCC TCC AGA TAG TATG-3′ ) and Hhat-specific primer ( 5′-CAC TTG CTA ACT AGA AGG AAC TTCC-3′ ) produced a 250 bp band; wild-type primers ( 5′-CCT GGG AAG GAA AAA CCA ATA TGTA-3′ ) and ( 5′-GGT CCT ATC ATG CTA CCA AGA AA-3′ ) amplified a 1 . 4 kb band . Samples were denatured at 94°C for 30 sec , annealed at 57°C for 30 sec , and extended at 72°C for 30 sec for 30 cycles for both reactions; PCR bands for the wildtype and mutant reactions were gel purified and sequenced , confirming Hhat as the gene disrupted by the Creface transgene . To confirm the absence of Hhat mRNA in HhatCreface/Creface mutants , RNA was isolated from wild-type and mutant embryos ( RNeasy kit , Qiagen , Valencia , CA ) and cDNA libraries were created using the Superscript first strand kit ( Invitrogen , Carlsbad , CA ) . Primers ( 5′-AGG TTC TGG TGG GAC CCT GTGT-3′ ) and ( 5′-AGA AAG CAG TGT CCC CAA CAGG-3′ ) were used to amplify the full length Hhat mRNA in wild-type , heterozygous and mutant embryos . Primers to glyceraldehyde 3-phosphate dehydrogenase ( Gapdh ) ( 5′-AGC CTC GTC CCG TAG ACA AAAT-3′ ) and ( 5′-ACC AGG AAA TGA GCT TGA CAAA-3′ ) were used as an internal positive control . Embryos were collected as described above , fixed overnight in 4% paraformaldehyde ( PFA ) at 4°C , dehydrated in methanol ( MeOH ) , and stored at −20°C until used in the staining protocol . In situ hybridizations were performed following the standard protocol described by [58] . Anti-sense digoxigenin-labeled mRNA riboprobes were synthesized for Bmp4 ( R . Arkell ) , Crabp1 ( S . Schneider-Maunoury ) , Eya2 ( P . Trainor ) , Fgf8 ( I . Mason ) , Shh ( A . McMahon ) , Six2 ( P . Trainor ) , Snail ( A . Nieto ) , Sox9 ( R . Krumlauf ) , and Sox10 ( M . Gassmann ) . Hhat forward 5′-CTG CGT GAG CAC CAT GTT CA-3′ and Hhat reverse 5′-TCT CCA CAG TGA CTC CCA GC-3′ primers were used to generate an exon 8–10 specific probe and whole embryos which was stained with Hhat antisense probe were mounted in Tissue Tek OCT ( VWR , West Chester , PA ) and sectioned at 20 µm thick to observe spatiotemporal activity . Hhat+/Creface mice were mated to Rosa 26-lacZ reporter ( R26R ) mice and embryos were collected at E9 . 5–11 . 5 as described above . Embryos were stained using the β-galacatosidase staining solution kit ( Chemicon/Millipore , Billerica , MA ) according to manufacturer's instructions . Briefly , embryos were fixed on wet ice for 20–30 min in the Tissue Fixative Solution , washed in Tissue Rinse Solution A for 30 min and Tissue Rinse Solution B for 5 min at RT . Embryos were incubated O/N at 37°C in Tissue Stain Solution with X-gal ( 40 mg/mL in DMF ) ( Invitrogen , Carlsbad , CA ) . After incubation , embryos were washed PBS and re-fixed overnight . Embryos were processed in paraffin , cut in 10micron sections , and counterstained in Nuclear Fast Red ( NFR ) ( Sigma , St . Louis , MO ) . For β-galactosidase staining on sections , E15 . 5 embryos were harvested and immersed in LacZ fixative ( 0 . 25%glutaraldehyde , 10% 0 . 5 M EGTA , and 10% 1 M MgCl2 in PBS ) for 2 . 5 hrs at 4°C . Embryos were processed through a sucrose gradient ( 15% then 30% sucrose in PBS ) and snap frozen in OCT . Eight micron sections were cut and re-fixed for 10 min at room temperature . Sections were then washed in LacZ wash buffer ( 0 . 2% MgCl2 , 0 . 01% NaDOC , 0 . 02% NP40 in PBS ) three times for 5 min . β-galactosidase staining was developed using a LacZ stain solution ( 0 . 002% Potassium ferrocyanide , 0 . 01% Potassium ferricyanide , 0 . 04% X-gal ( 25 mg/mL in DMF ) in LacZ wash buffer ) to the desired intensity . The sections were then rinsed three times in LacZ wash buffer for 5 min , counterstained with nuclear fast red and rinsed in distilled water for 1–2 min . Sections were mounted and photographed using a Ziess Axioplan microscope and processed using Photoshop CS2 ( Adobe , San Jose , CA ) . For whole-mount staining using a neurofilament antibody , embryos were fixed in 4% PFA overnight at 4°C and dehydrated in a graded MeOH series . Dehydrated embryos were bleached in methanol∶DMSO∶30% H2O2 ( 4∶1∶1 ) for 5 hrs at room temperature and rehydrated in 50% methanol∶PBS and 15% methanol∶PBS , PBS for 30 minutes each . Embryos were blocked in PBSMT ( 2% milk powder , 0 . 1% Triton X-100 in PBS ) twice for 1 hour at room temperature . Embryos were incubated in primary antibody to Neurofilament ( 2H3 ) diluted at 1∶250 O/N at 4°C in PBSMT . Embryos were rinsed in PBSMT and incubated in secondary antibody using a 1∶200 dilution in PBSMT of HRP-coupled goat anti-rabbit IgG ( Jackson Immunoresearch , West Grove , PA ) . For color development , embryos were rinsed in PBSMT and PBT ( 0 . 2% BSA , Sigma , 0 . 1% Triton X-100 ) three times , 20 minutes each at room temperature and washed in 3 , 3-Diaminobenzidine ( 0 . 3 mg/mL ) ( Sigma , St . Louis , MO ) in PBT for 20 minutes; 0 . 03% H2O2 was added and color was developed to the desired intensity . For section immunohistochemistry , embryos were fixed overnight in 1% PFA at 4°C . Embryos were processed through a sucrose gradient ( 15% , then 30% sucrose in PBS ) , mounted in Tissue Tek O . C . T . ( VWR , West Chester , PA ) and sectioned at 10microns . Sections were rinsed three times in PBT ( PBS with 0 . 1% Triton X-100 ) for 5 min and blocked in 10% goat serum ( Invitrogen , Carlsbad , CA ) in PBT for 1 hr at RT . Slides were incubated in primary antibody diluted in 10% goat serum/PBT overnight at 4°C . The mouse monoclonal antibodies to cleaved-Caspase-3 and pErk1/2 ( Cell Signaling Technologies , Danvers , MA ) , phospho-Histone-3 ( Upstate/Millipore , Billerica , MA ) and Shh ( 5e1 ) were used at 1∶500 ( Caspase-3 , anti-pH3 ) , 1∶250 ( pErk1/2 ) and 1∶25 ( 5e1 ) respectively . Slides were rinsed three times in PBT for 10 min each at room temperature and incubated in a goat anti-mouse Alexa 488 secondary antibody at 1∶250 ( Molecular probes/Invitrogen , Carlsbad , CA ) for 2 hours at 4°C . Sections were counterstained with a 1/1000 dilution of 2 mg/ml DAPI ( Sigma , St . Louis , MO ) in PBS for 5 minutes , followed by multiple rinses in PBS . Slides were then mounted with fluorescent mounting medium ( DakoCytomation , Carpinteria , CA ) . All images were collected using a Ziess Axioplan microscope and processed using Photoshop CS2 ( Adobe , San Jose , CA ) . Antibodies to Shh ( 5e1 ) and Neurofilament ( 2H3 ) were obtained from the Developmental Studies Hybridoma Bank developed under the auspices of the NICHD and maintained by the University of Iowa , Department of Biological Sciences ( Iowa City , IA 52242 ) . Embryos were collected at E15 . 5 and 17 . 5 and fixed in 95% ethanol ( EtOH ) overnight . Embryos were washed in a stain solution containing 0 . 5% Alizarin red ( Sigma , St . Louis , MO ) and 0 . 4% Alcian blue 8× ( Sigma , St . Louis , MO ) in 60% EtOH overnight at room temperature . For E15 . 5dpc embryos , soft tissue was dissolved in 2% KOH for three hours and transferred to 0 . 25% KOH for 30 min . For E17 . 5 staining , the embryos were anesthetized in PBS for 1 hr at 4°C , fixed overnight in 95% EtOH , skinned and eviscerated prior to staining; all remaining soft tissue was dissolved in 2% KOH for six hours and transferred to 0 . 25% KOH for 30 min . Embryos were cleared in glycerol∶KOH ( 20%∶0 . 25%; 33%∶0 . 25%; 50%∶0 . 25% ) . Embryos were stored in 50% glycerol∶0 . 25% KOH until photographed . Immortalized mouse embryonic fibroblast ( MEFs ) derived from control and HhatCreface/Creface mouse embryos were transfected Shh expression vector ( gift from Dr . Pao-Tien Chuang ) using Amaxa Nucleofactor ( Amaxa , Germany ) according to manufactures protocol . Twenty-four hours after transfection , cells were incubated with 50 µM Click-It palmitic acid ( Invitrogen , Carlsbad , CA ) . After 12 hrs incubation , cells were washed three times with cold PBS and whole cell lysate was harvested using RIPA lyses buffer . Protein extracts were subjected to Click labeling reaction using Click-It protein reaction buffer kit ( Invitrogen , Carlsbad , CA ) in order to attach biotin to incorporated palmitic acid . Precipitated samples were again dissolved into RIPA buffer and mixed with mouse anti-Shh antibody ( 5e1 ) overnight at 4°C with rocking , and subsequently immunoprecipitated with protein G beads ( Thermo scientific , Rockford , IL ) with rocking for 2 hrs at room temperature . The beads were resuspended in SDS sample buffer and separated by SDS-PAGE and transferred onto a PVDF membrane ( GE healthcare life sciences , Buckinghamshire , UK ) . The membrane was incubated with alkaline phosphatase streptavidin ( Vector laboratories , Burlingame , CA ) and treated with fluorescent substrate for alkaline phosphatase ( Vector laboratories , Burlingame , CA ) , exposed to X-ray film to detect the palmitoylated Shh . Same membrane was incubated with anti Shh antibody ( H-160; Santa Cruz Biotechnology Inc , Santa Cruz , CA ) and subsequently HRP conjugated secondary antibody ( Sigma , St . Louis , MO ) in order to confirm the amount of Shh protein in each samples . The signals were detected with ECL plus western blotting detection system ( GE healthcare life sciences , Buckinghamshire , UK ) and exposed to X-ray film . | Craniofacial anomalies account for approximately one third of all birth defects , and holoprosencephaly ( HPE ) is the most common structural malformation of the embryonic brain . HPE is a failure of the forebrain to bifurcate and is a heterogeneous disorder that is often found in patients together with other craniofacial malformations . Currently , it is not known if these phenotypes arise through a common etiology and pathogenesis , as the genetic lesions responsible for HPE have only been identified in about 20% of affected individuals . Here we demonstrate for the first time that Hedgehog acyltransferase ( Hhat ) loss-of-function leads to holoprosencephaly together with acrania and agnathia , which highlights the importance of Hh signaling in complex craniofacial disorders . | [
"Abstract",
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] | 2012 | Mutations in Hedgehog Acyltransferase (Hhat) Perturb Hedgehog Signaling, Resulting in Severe Acrania-Holoprosencephaly-Agnathia Craniofacial Defects |
Cytomegalovirus ( CMV ) elicits long-term T-cell immunity of unparalleled strength , which has allowed the development of highly protective CMV-based vaccine vectors . Counterintuitively , experimental vaccines encoding a single MHC-I restricted epitope offered better immune protection than those expressing entire proteins , including the same epitope . To clarify this conundrum , we generated recombinant murine CMVs ( MCMVs ) encoding well-characterized MHC-I epitopes at different positions within viral genes and observed strong immune responses and protection against viruses and tumor growth when the epitopes were expressed at the protein C-terminus . We used the M45-encoded conventional epitope HGIRNASFI to dissect this phenomenon at the molecular level . A recombinant MCMV expressing HGIRNASFI on the C-terminus of M45 , in contrast to wild-type MCMV , enabled peptide processing by the constitutive proteasome , direct antigen presentation , and an inflation of antigen-specific effector memory cells . Consequently , our results indicate that constitutive proteasome processing of antigenic epitopes in latently infected cells is required for robust inflationary responses . This insight allows utilizing the epitope positioning in the design of CMV-based vectors as a novel strategy for enhancing their efficacy .
Cytomegalovirus ( CMV ) infection maintains the strongest immune response known in clinical medicine , dominating the T-cell memory compartment of seropositive hosts [1] . CMV is a herpesvirus that is never fully eliminated from the host , which may explain why these responses can be detected even at late time points upon initial infection [2 , 3] . The T-cell responses to immunodominant CMV antigens appear only to increase with age [4] but at the same time stay functional even in very old , otherwise immunosenescent hosts [5] . Therefore , it has been proposed that CMV recombinants expressing heterologous antigenic determinants [6] may be used as superior vaccine vectors . Cytomegalovirus ( CMV ) based vaccine vectors have attracted broad attention over the past years as promising vectors for the induction of protective cellular T-cell responses against a variety of viral , bacterial and tumor targets [6–11] . Rhesus Cytomegalovirus ( RhCMV ) vectors encoding antigens from the simian immunodeficiency virus ( SIV ) , a virus used in rhesus monkeys as a model of HIV-AIDS disease , sustain a remarkable SIV-specific T-cell response even in CMV-positive animals [11] and clear highly virulent SIV from more than 50% of the monkeys , thus preventing the development of AIDS-like disease [12 , 13] . It has been proposed that the ability of CMV-based vaccines to provide this unprecedented level of protection against SIV depends on effector-memory ( EM ) T-cell responses intercepting viral dissemination at sites of virus entry into the host [14] . How and why CMV sustains this unique immune response is still unresolved and we need to clarify these mechanisms to optimize CMV based vaccines . The cellular and molecular mechanisms of T-cell priming and maintenance by CMV vectors can be addressed in minute detail by infection of inbred and transgenic mice with genetically modified mouse CMV ( MCMV ) . Experimental MCMV infection was shown to induce persistent infiltrates of CD62L- EM CD8 T-cells in solid organs [15] directed against immunodominant MCMV epitopes [16] . Furthermore , MCMV vectors encoding single antigenic epitopes induced inflationary [17] CD8 T-cell responses against the heterologous epitope , and provided immune control upon challenge with a recombinant vaccinia virus carrying the same epitope [6] . On the other hand , only some epitopes encoded by CMV induce inflationary EM responses [16 , 18 , 19] and the mechanisms driving this selection are incompletely understood . A plethora of potential mechanisms contributing to inflationary EM responses has been proposed [20] , including the efficacy of antigen processing , avidity of peptide binding to MHC molecules and avidity of the T-cell receptor ( TCR ) binding to peptide-MHC-I ( pMHC-I ) complex . We showed previously that inflationary responses depend on the context of epitope expression , rather than peptide-intrinsic properties [8] . On the other hand , the viral gene M102 was shown to simultaneously induce inflationary and non-inflationary CD8 T-cell responses [18] . Therefore , promoter activity alone could not explain the entire selection process . We show here that C-terminal localization of a peptide results in drastically improved immune protection by CMV-based vaccine vectors . We also show that an MCMV peptide that induces conventional CD8 T-cell responses from its native site becomes inflationary when transferred on the C-terminus of the same viral protein . Finally , we show that these striking differences in size and type of response correspond to the presence of the pMHC-I complexes on the surface of in vitro virus-infected endothelial cells and that the effect critically depends on the availability of the peptide for constitutive proteasomal processing .
To test immune protection by CMV-based vectors in a model of human papilloma virus ( HPV ) induced cancer , we generated a recombinant MCMV ( MCMVE6+E7 ) expressing the full-length E6 and E7 proteins of the HPV strain 16 ( HPV16 ) . We placed E6 and E7 under the control of the HCMV immediate-early ( IE ) promoter , because we showed previously that an IE promoter induces stronger CD8 T-cell responses than an early one [8] . Moreover , we utilized an MCMV backbone lacking the viral genes m1 to m16 [21] and thus providing ample cloning capacity . At 10 weeks post infection ( p . i . ) with MCMVE6+E7 , the response to the immunodominant Db-restricted E749-57 peptide RAHYNIVTF was detectable , although weaker than the response to the endogenous inflationary IE3 epitope ( S1 Fig ) . We tested next the immune protection by MCMVE6+E7 against a challenge with E6+E7 transformed TC-1 tumor cells [22] . 25 , 000 TC-1 cells were administered at 27 weeks post immunization and MCMVE6+E7 immunized mice showed reduced tumor growth in comparison to mock-vaccinated mice , but not a complete block of tumor growth ( Fig 1A ) . To define if protection was mediated by CD8 T-cell responses , we generated another MCMV recombinant expressing only the MHC class I epitope E749-57 on the C-terminus of the ie2 protein ( MCMVie2E7 ) . Control mice were immunized with the recombinant MCMVie2SL , expressing the SSIEFARL epitope in the same location [8] , or mock-immunized with PBS . Tumor growth upon challenge with TC-1 cells was completely prevented in MCMVie2E7 ( Fig 1B ) , and the mice remained tumor-free for the duration of the experiment . The improved immune protection by MCMVie2E7 over MCMVE6+E7 was unexpected , because MCMVE6+E7 expressed numerous antigenic epitopes and MCMVie2E7 only one , which was also present in MCMVE6+E7 . We reasoned that improved protection by MCMVie2E7 could have been due to differences in promoter activity between HCMV IE and MCMV ie2 , or by the better priming due to the C-terminal peptide localization . We analyzed this by generating a third MCMV mutant , where we fused the full-length E6 and E7 proteins to the C-terminus of the ie2 protein of a full-length MCMV ( MCMVie2E6-7full ) . Hence , MCMVie2E6-7full used the MCMV ie2 promoter to drive the full-length E6+E7 transcriptional unit , but RAHYNIVTF was located in its native site , and not at the C-terminus . Six out of 9 mice vaccinated with MCMVie2E6-E7full and challenged with 25 , 000 TC-1 cells displayed tumor growth ( Fig 1C ) , arguing that epitope localization , rather than promoter activity , resulted in absolute immune protection by MCMVie2E7 . In that case , CD8 T-cell responses against the E749-57 epitope RAHYNIVTF should be stronger in MCMVie2E7 infection than in MCMV recombinants expressing the full length protein . We compared immune response by pMHC-I dextramer staining ( representative dot blots in Fig 1D ) , and the response was undetectable in MCMVE6+E7 infected mice , stronger in the MCMVie2E6-7full group and strongest upon MCMVie2E7 infection ( Fig 1E ) . In sum , our data argued that C-terminal epitope localization improved CD8 T-cell responses and thus , immune protection . To test if immune protection by a C-terminally expressed epitope would be generally applicable to another epitope and another MCMV gene , we used our previously described MCMVs expressing the Kb-restricted SSIEFARL epitope as a C-terminal tag on the ie2 ( MCMVie2SL ) or on the M45 ( MCMVM45SL ) protein [8] . Mice were immunized with either of the mutants or wild-type MCMV as control and challenged with a recombinant vaccinia virus expressing the same epitope ( rVACVSL ) . Both MCMVs expressing the SSIEFARL epitope significantly controlled rVACVSL replication ( Fig 2A ) , providing further evidence that C-terminal localization of the antigenic epitope will result in immune protection . It has been proposed that the immune protection induced by CMV-based vaccine vectors rests on the induction of antigen-specific EM T-cells [12] . Hence , we analyzed the phenotype of SSIEFARL-specific CD8 T cells upon MCMVie2SL , MCMVM45SL or rVACVSL infection . Blood leukocytes were in vitro restimulated with the peptide for 6h and IFNγ responding cells were classified according to CD127 and KLRG1 expression into EM ( KLRG1+CD127- ) or central memory ( CM ) ( KLRG1-CD127+ ) cells . All infections induced a predominantly EM phenotype in IFNγ+ cells at 7 days post infection ( dpi ) , but this response remained EM in both MCMV infections until 180 dpi ( Fig 2B and 2C ) , while it rapidly shifted to a CM phenotype in rVACV infection ( Fig 2C ) . Since the M45 gene encodes a natural Db-restricted epitope ( HGIRNASFI ) , which induces non-inflationary CM responses [18 , 23 , 24] , we were surprised by the EM response to SSIEFARL upon MCMVM45SL infection ( Fig 2 ) . We showed previously that MCMVie2SL induces inflationary CD8 T-cell responses to SSIEFARL , whereas the responses contract by 14 dpi in MCMVM45SL infection [8] . We considered that the C-terminal modification of the M45 protein could have destabilized the whole protein and altered the phenotype of the responses against all epitopes encoded by this M45 variant . Therefore , we compared the CD8 T-cell response to SSIEFARL and HGIRNASFI epitopes upon MCMVM45SL infection and noticed a drastic difference in their size and the quality . SSIEFARL induced ~30-fold stronger responses than HGIRNASFI at all times p . i . ( Fig 3A ) and cells responding to HGIRNASFI showed a CM phenotype ( CD127+ , KLRG1- ) within weeks p . i . , while SSIEFARL-specific cells retained an EM ( CD127- , KLRG1+ ) phenotype for up to 180 dpi ( Fig 3B ) . Similarly , CD62L expression remained low on SSIEFARL specific cells , regardless of expression context ( S1B Fig ) . Since the C-terminal modification did not alter the phenotype of responses against HGIRNASFI , the EM phenotype of responding T cells was specific for SSIEFARL . The test if the difference in responses to SSIEFARL and HGIRNASFI depended on peptide sequence or localization within the protein , we generated a new recombinant MCMV , where the HGIRNASFI epitope was moved from its original location to the M45 C-terminus . First we generated a negative-control virus lacking the HGIRNASFI epitope ( MCMVM45I->A ) , by replacing the Db-anchoring isoleucine at the HGIRNASFI C-terminus with an alanine ( S2A Fig ) , which precluded efficient peptide processing and anchoring to the Db molecule ( the approach is described in [25] ) . On this background we introduced the HGIRNASFI peptide at the C-terminal end of the M45 protein ( S2B Fig ) . The new recombinant virus ( MCMVM45Cterm ) showed no growth defects in vitro and in vivo ( S2C and S2D Fig ) . Mice were infected with either of the new mutant viruses or with MCMVWT and HGIRNASFI-specific CD8 T-cell frequencies were monitored over 180 days by peptide restimulation of blood leukocytes and intracellular staining for IFNγ . As expected , HGIRNASFI-specific CD8 T cells were undetectable in mice infected with the MCMVM45I->A recombinant , but clearly responded to MCMVWT infection . Remarkably , MCMVM45Cterm elicited an ~ 8-fold stronger response to HGIRNASFI at 7 dpi than MCMVWT ( Fig 4A , upper row ) . While HGIRNASFI-specific CD8 T cells declined from this peak in both groups by 180 dpi , their percentage was about 70-fold higher in MCMVM45Cterm than in MCMVWT infected mice ( Fig 4A , bottom row ) . Detailed analysis of the response kinetics showed that responses upon MCMVWT infection contracted by 14 dpi and remained low thereafter , whereas the contraction of responses in MCMVM45Cterm infection was followed by a slight , but clearly noticeable inflation starting by 28 dpi ( Fig 4B ) . The long-term phenotype of the peptide-specific cells was CM in MCMVWT infection , but EM upon MCMVM45Cterm infection ( Fig 4C–4E ) . Thus , C-terminal localization of the HGIRNASFI peptide within the M45 protein resulted not only in a stronger and inflating CD8 T-cell response , but also in a high percentage of peptide-specific cells with an EM phenotype at late time points after infection . It was theoretically possible that virus mutagenesis and propagation resulted in unwanted mutations of immune evasion genes , improving peptide presentation and CD8 T-cell priming . To exclude this scenario , we compared MHC-I surface expression upon infection with MCMVM45Cterm , MCMVM45I->A , MCMVWT and a mutant lacking the immune evasion genes m06 and m152 ( MCMVΔm06m152 ) . All of the MCMV recombinants , except MCMVΔm06m152 , efficiently down-regulated MHC class I Db molecules on infected liver sinusoidal endothelial cells ( LSECs ) ( Fig 5A and S3A Fig ) . Thus , the inflationary EM response to the HGIRNASFI epitope encoded by MCMVM45Cterm was not due to different surface levels of MHC-I . Since the HGIRNASFI epitope located at its native site is poorly processed and presented on infected fibroblasts [26] , we considered that the improved response to MCMVM45Cterm might be due to the availability of the peptide itself on MHC-I molecules . Hence , to measure the presentation of HGIRNASFI on LSECs , we analyzed the IFNγ response of a HGIRNASFI-specific CD8 T-cell line ( CTL ) upon co-culture with a recently published LSECs line [27] . LSECs infected with the control virus ( MCMVM45I->A ) or with MCMVWT did not activate M45-specific CTL , whereas the MCMVM45Cterm virus induced a robust activation ( Fig 5B , upper row ) . Importantly , no CTL responses were observed upon infection with MCMVΔm06m152 , demonstrating that the HGIRNASFI epitope expressed at its native site cannot activate CTL responses to infected LSECs even if MHC-I molecules are present at high levels on the cell surface . In theory , it was possible that an unknown MCMV gene that impairs the processing and presentation of the HGIRNASFI peptide in MCMVWT infection was accidentally lost in the MCMVM45Cterm mutant . Therefore , we UV-inactivated the viruses to abolish de novo gene expression and we co-cultured the cells exposed to UV-inactivated virus with M45-peptide-specific CTLs . Since M45 is a tegument protein , it is available in cells upon entry of UV-inactivated virus . As shown in Fig 5B bottom row and S3B Fig , inactivation of MCMVWT and MCMVΔm06m152 did not result in recognition of the infected cells by the CTL , whereas MCMVM45Cterm induced a measurable response , although somewhat weaker than upon infection with viable virus . To conclusively show that HGIRNASFI localization within the M45 protein determines antigenic peptide availability on the Db molecules of the infected cells , we performed a targeted nanoflow liquid chromatography mass spectrometry ( nanoLC-MS3 ) analysis . Total Db MHC-I molecules were immunoprecipitated ( IP ) from lysates of LSECs infected with MCMVWT , MCMVM45I->A , MCMVΔm06m152 or MCMVM45Cterm . Upon epitope elution from the pMHC-I complexes , IP samples were analyzed by targeted nanoLC-MS3 for the presence of the HGIRNASFI peptide . As expected , the target peptide was not detected in the sample from MCMVM45I->A-infected cells . In line with previously published functional assays [26 , 28] , the peptide was also not detected in MCMVWT-infected cells , or in those infected with MCMVΔm06m152 , although we detected traces of HGIRNASFI in one out of three repetitions of MCMVWT and MCMVΔm06m152 infection . However , the target peptide was highly abundant in all 3 replicates of the MCMVM45Cterm-infected LSECs ( Fig 5C ) and its spectrum matched the one from the synthetic HGIRNASFI peptide ( S3C Fig ) . Thus , the MS data confirmed that the C-terminal localization of the peptide facilitated its processing and its presentation in pMHC-I complexes . Considering the very poor HGIRNASFI presentation on MCMVWT infected cells , it is counterintuitive that the M45 epitope is immunodominant at 7 dpi [18] . However , this early immunodominance might be explained by peptide cross-presentation on professional antigen-presenting cells ( APCs ) . This would imply that inflationary responses depend on epitope presentation on latently infected non-hematopoietic cells . To validate this idea , we generated chimeric mice with impaired MHC-I antigen presentation on professional APCs ( but maintained on non-hematopoietic cells ) , by hematopoietic reconstitution of gamma-irradiated C57BL/6 recipients with TAP-deficient bone-marrow ( BM ) cells ( TAP-/-→B6 ) . Mice were infected with MCMVM45Cterm or MCMVWT and monitored for HGIRNASFI-specific responses . Additional controls included homochimeric mice where C57BL/6 mice were used both as BM donors and recipients ( B6→B6 ) . While B6→B6 mice showed kinetics that essentially matched the one observed in wild-type mice , the TAP-/-→B6 mice revealed a complete loss of HGIRNASFI response upon MCMVWT infection ( Fig 5D top panel and S3D Fig ) . In contrast , MCMVM45Cterm induced strong CD8 T-cell responses against the peptide in TAP-/-→B6 mice ( S3D Fig ) that were undistinguishable from responses in B6→B6 mice at later times p . i . ( Fig 5D bottom panel ) . Interestingly , the initial peak response , seen in WT mice or in B6→B6 controls was absent from the TAP-/-→B6 mice , which may indicate that this initial response is mainly driven by cross-presentation . Finally , one should note that approximately 90% of BM-derived cells in chimeric mice were donor-derived ( S3E Fig ) . Therefore , the long-term CD8 T-cell response to the C-terminal epitope was maintained in absence of APC-dependent cross-presentation ( Fig 5D ) , implying that it may depend on its direct presentation by virus-infected cells , although we cannot exclude the possibility that the initial priming was due to cross-presentation by the few remaining TAP-competent APCs derived from the recipient BM . Taken together , our results showed that peptide localization within the M45 protein , rather than targeted activity of immune evasion genes , is the limiting factor for CTL recognition of the HGIRNASFI peptide in MCMV-infected LSECs . Since the MHC-I availability of the same peptide expressed by the same viral gene differed greatly based on its localization within the protein , we considered that this difference may be due to improved peptide processing prior to loading on MHC-I molecules . Antigenic peptide processing and subsequent surface presentation of peptide MHC-I complexes can be enhanced by altering amino acid residues flanking an epitope [29] . Thus , we generated a novel MCMV recombinant called MCMVM45ASL , carrying the SSIEFARL peptide at the C-terminus of the M45 protein , but preceded by 2 alanines ( S4A Fig ) . Thus , these two alanines were the only difference between MCMVM45ASL and MCMVM45SL [8] . The in vitro and in vivo growth of MCMVM45ASL was comparable to MCMVWT ( S4B Fig ) . To test the effect of these flanking residues on epitope recognition by CD8 T cells , we co-cultured CD8 T cells from transgenic gBT-I mice expressing a T-cell receptor specific to the Kb-SSIEFARL complex [30] , with IC-21 macrophages infected with MCMVM45SL or MCMVM45ASL , and assessed them for TNFα and IFNγ production . Infection with the recombinant MCMV containing the alanine spacer induced a stronger T-cell response than the one without it ( Fig 6A ) . The same result was observed upon co-culture with in vitro infected LSECs ( S4C Fig ) . This implied that peptide processing is a rate limiting step in activating CD8 T cells by our mutants . Therefore , we tested if the same would apply in vivo and affect the size of inflationary responses to SSIEFARL . We infected mice with MCMVM45SL , MCMVM45ASL or , as a non-inflationary control , with rVACVSL . At 7 dpi , the percentage of SSIEFARL-responding cells was identical in mice infected with either MCMV recombinant ( Fig 6B ) . Likewise , both mutants induced SSIEFARL responses with inflationary phenotype ( S4D Fig ) . On the other hand , the peptide-specific CD8 T cells showed an inflationary trend only in mice infected with the MCMVM45ASL recombinant ( Fig 6B ) , and differences in peptide-specific responses were statistically significant at all times after 60 dpi . Hence , improved peptide processing resulted in stronger MI . To test if C-terminal expression improved the processing of peptides by the proteasome , MCMVM45Cterm or MCMVWT-infected LSECs were co-cultured with CTLs in the presence of two proteasome inhibitors—MG-132 or Lactacystin . Both inhibitors impaired CTL activation upon co-culture with cells infected with the MCMVM45Cterm recombinant in a dose dependent manner ( Fig 6C ) . On the other hand , CTL activation by cells that were loaded exogenously with the peptide remained unimpaired ( S4E Fig ) and MCMVWT infection did not activate CTLs in presence or absence of the inhibitors ( Fig 6C ) . Finally , treatment with a protease inhibitor ( leupeptin ) did not influence CTL recognition of the MCMVM45Cterm-infected target cells ( Fig 6C ) , confirming that the C-terminally expressed epitope was proteasomally processed for CTL recognition . We infected mice lacking the immunoproteasome component LMP7 with MCMVWT or MCMVM45Cterm , and compared the kinetics of their HGIRNASFI responses to the parental C57BL/6 strain . The response was absent in LMP7-/- mice upon MCMVWT infection ( Fig 6D top panel ) , but fully maintained upon MCMVM45Cterm infection . ( Fig 6D bottom panel ) . Interestingly , the acute response at 7 dpi with MCMVWT was completely abrogated ( p<0 . 001 ) in LMP7-/- mice ( Fig 6D top panel ) , but only slightly impaired ( p<0 . 05 ) upon MCMVM45Cterm infection ( see Fig 6D bottom panel ) . This argued that even the acute CD8 T-cell response was largely immunoproteasome independent when the epitope was relocated to the C-terminal position . Taken together , our data strongly argued that the efficacy of peptide processing defines the rate of CD8 T-cell inflation for a given peptide in MCMV infection .
We reported here several findings that were counterintuitive in light of the existing literature . Previous publications showed that immune sensing of an antigenic epitope may impair the transcription of viral genes expressed later in the process of viral reactivation [31] , and that antigen-specific inflationary responses compete with each other [8 , 32] . Taken together , the evidence was unified in the Immune Sensing Hypothesis [20] , where intermittent MCMV transcription during viral latency results in antigen expression , T-cell sensing and suppression of genes expressed later during reactivation , thus defining the immunodominance hierarchy . On the other hand , the Immune Sensing Hypothesis did not explain the inflationary responses against epitopes expressed by the early genes M38 or m139 [18] . We showed here that the conventional responses against the natural M45 epitope are not due to its silenced transcription in latency . In fact , we showed that the very same viral gene can simultaneously induce inflationary and conventional responses to two epitopes expressed within its protein product , thus demonstrating that promoter activity cannot alone predict which epitopes are inflationary . We observed that sequences flanking an antigenic peptide and its position within a protein may critically define the type of responses and that the efficacy of peptide processing by the constitutive proteasome is a rate-limiting factor for MI . Our results are not necessarily in conflict with the immune sensing hypothesis , but rather complement it with a secondary mechanism defining epitope dominance . Namely , while we show that immunodominance and MI depend on processing efficacy , the epitopes cannot be antigenic unless expressed , and hence the pattern of gene expression during viral latency is necessarily an important contributing mechanism [8 , 20] . Previous publications showed that CTL priming upon HSV-1 infection requires cross-presentation [33] , and that the same might be true for the MCMV responses [34 , 35] , but we showed that this holds true only for conventional CTL responses against a non-inflationary epitope . Transferring the conventional HGIRNASFI epitope to the C-terminus of the M45 protein allowed its direct presentation on virus-infected LSECs , and induced inflationary responses . Impaired cross-presentation ( Fig 5D ) abrogated the conventional HGIRNASFI response , but not the inflationary one . Previous data showed that non-hematopoietic cells , such as LSECs , are sites of MCMV latency [36 , 37] and that peptide presentation on non-hematopoietic cells is required for inflationary , but not for conventional immune responses [38 , 39] . However , our work differs in experimental design from published evidence in two important aspects , and thus allows us to posit novel conclusions . Firstly , we generated recombinant viruses that allowed us to compare the response to the same epitope expressed in two different locations of the same gene , while published evidence was based on cross-comparison of two distinct MCMV epitopes , expressed by different viral genes . Therefore , previous evidence could have been explained by peptide-intrinsic properties , or by cell-type specific differences in promoter activity , whereas our data excluded these scenarios and allowed us to focus on processing as the determinant of memory inflation . Secondly , we performed bone-marrow reconstitution of C57BL/6 mice with TAP deficient bone-marrow , while previous work was based on MHC-deficient recipient mice of wild-type bone marrow . Therefore , prior evidence showed that direct presentation is required for memory inflation , whereas our work might indicate that it is sufficient for this phenomenon . Namely , while the response to the HGIRNASFI epitope in wild-type MCMV infection required cross-presentation , because it was completely abrogated in mice transferred with TAP-/- bone-marrow , responses were undiminished when the epitope was expressed from the C-terminus , arguing that cross-presentation on APC might be dispensable for the induction of inflationary responses . If that was true , our result would point to direct priming by antigen presented on non-hematopoietic cells , which would defy a key dogma in immunology . It is important to note , however , that the irradiation and bone-marrow transfer procedure resulted in chimerism , where approximately 10% of PBMCs remained TAP-competent ( S3E Fig ) . While this was not enough for the priming of conventional CD8 T cell responses in the setup of infection with MCMVWT , our data cannot exclude the possibility that the few remaining TAP-competent cells were sufficient to provide cross-priming and jump-start the inflationary immune response . In conclusion , while our data are intriguing , more detailed analyses are necessary to ascertain if inflationary responses may occur in the absence of an initial bout of cross-priming . Previous evidence showed that the immunoproteasome is not strictly required for processing of inflationary epitopes [40] . Similarly , a recent publication showed that the non-inflationary HGIRNASFI epitope may induce inflationary responses when expressed as a minigene within an adenovirus-based vaccine vector [41] . However , these experiments could still be explained within the Immune Sensing Hypothesis , where memory inflation is predicated by the context of gene-expression of the antigenic epitope . By testing the same epitope in the context of the same protein , we excluded confounding factors and showed directly that peptide processing by the constitutive proteasome and direct antigen presentation on non-professional APC are the rate-limiting events for MI . Predictive algorithms to identify epitope insertion sites with optimized antigenic processing [42] , have been recently applied to score peptide processing of natural and modified epitopes expressed by MCMV [43] . However , published data argue that the proteasome needs to cleave peptides very precisely behind the anchoring amino acid at the C-terminus of an epitope ( reviewed in [44] ) , whereas additional amino acids present on the N-terminal end of the epitope precursor peptide may be trimmed by aminopeptidases upon TAP-mediated transport into the ER [45] . Therefore , designing vectors that already carry the antigenic peptide at the C-terminus of a protein abrogates the need for proteasomal cleavage on the sensitive C-terminus of the peptide and thereby substantially improves the inflationary CD8 T-cell response . This insight likely explains why the surface presentation of the HGIRNASFI peptide , that T cells do not recognized in vitro ( Fig 5B ) or in vivo [24] at its native site , could be substantially optimized by this tweak in vector design . Accordingly , the E7 antigen , just like the prostate specific antigen [7] , induced weak CD8 T-cell responses and poor protection when expressed within full length proteins in recombinant MCMVs , but a single E7 peptide fused to the C-terminus of the ie2 MCMV protein induced not only robust CD8 T-cell responses , but also absolute anti-tumor immunity ( Fig 1 ) . Similarly , SSIEFARL epitopes expressed on the C-terminus of two MCMV proteins provided protection against viral challenge ( Fig 2A ) . In conclusion , our data showed that simple shifting of an epitope sequence to the C-terminus of a protein optimizes peptide processing and inflationary CD8 T-cell responses and circumvents the need for predictive algorithm scoring of the epitope insertion site . Although our results unequivocally demonstrate that response magnitude and MI depend on the peptide context within a viral protein , one should take into account that additional parameters are likely to contribute to memory inflation and epitope immunodominance . These may include peptide intrinsic properties , such as its avidity of binding to MHC-I molecules and the avidity of TCR binding to pMHC complexes [46] , but also extrinsic properties , such as the promoter strength and epitope competition [8 , 31 , 32] , or the expression of additional viral genes , as has been recently described in the rhesus CMV model of infection [47] . Therefore , the mechanisms driving the exceedingly strong CMV responses remain a field of active research . Considering the huge potential that CMV-based vectors may have for the control of numerous lethal pathogens [9 , 12 , 13] , understanding the mechanisms that optimize antigen presentation and the induction of T-cell responses remains of paramount scientific and clinical relevance . By providing the first direct evidence for a causal link between antigen processing efficacy , epitope presentation , memory inflation and immune protection , our study makes a fundamental contribution towards this goal .
Mice were housed and handled in accordance with good animal practice . All animal experiments involving HPV immunization and challenge were performed at OHSU according to federal ( U . S . Animal Welfare Act ) and institutional guidelines , following the Institutional animal care and usage committee ( IACUC ) requirements , under the protocol ( IACUC Study #IS00003413 ) . Experiments involving LMP7-/- mice were performed according to U . K . Animal Welfare Act of 2006 and Home Office regulations ( project license no . PPL 30/3293 ) after review and approval by the local Ethical Review Board at the University of Oxford . All other animal experiments were performed at HZI in compliance with the German animal protection law ( TierSchG BGBI S . 1105; 25 . 05 . 1998 ) and were approved by the responsible state office ( Lower Saxony State Office of Consumer Protection and Food Safety ) under permit number 33 . 9-42502-04-11/0426 . The mice were housed and handled in accordance with good animal practice as defined by FELASA and the national animal welfare body GV-SOLAS . 129S2/SvPas Crl ( 129/Sv ) mice were purchased from Charles River ( Sulzfeld , Germany ) . C57BL/6 mice were purchased from Janvier ( Le Genest St Isle , France ) or Jackson Laboratory ( Sacramento , CA , USA ) . Mice used for generation of bone marrow chimeras ( B6 . 129S2-Tap1tm1Arp/J , C57BL/6J , B6 . SJL-Ptprca Pepcb/BoyJ ) were purchased from The Jackson Laboratory ( Sacramento , CA USA ) . gBT-I . 1 mice [30] were a gift from G . Behrens . LMP 7-/- mice on a C57BL/6 background [48] were bred and housed at the University of Oxford Biomedical Sciences Specified Pathogen Free ( SPF ) Facility . Age-matched female C57BL/6 mice ( Harlan , Bicester UK ) were used as controls . M2-10B4 ( CRL-1972; ATCC ) , and NIH 3T3 fibroblasts ( CRL-1658; ATCC ) were maintained in DMEM supplemented with 10% fetal calf serum , 1% Glutamine and 1% Penicillin/Streptomycin . IC-21 macrophages ( TIB-186; ATCC ) were maintained in RPMI 1640 medium supplemented with 10% fetal calf serum , 1% Glutamine and 1% Penicillin/Streptomycin . TC-1 tumor cells were grown as described [22] . LSECs from C57BL/6 mice were generated and maintained as described [27] . C57BL/6 murine embryonic fibroblasts ( MEFs ) were prepared and maintained as described previously [49] . The peptides M45 ( H-2Db-restricted , 985HGIRNASFI993 ) and the HSV-1 glycoprotein B-derived epitope ( H-2Kb-restricted , 498SSIEFARL505 ) [50] were synthesized and HPLC purified ( 65–95% purity ) at the HZI peptide-synthesis platform . Primers used in the study are listed in the S1 Table . MCMVM45ASL , MCMVM45Cterm , MCMVM45I->A and MCMVie2E7 recombinants were generated by En passant mutagenesis as described by Tischer and colleagues [51] , with modifications described by us [52] . In brief , linear PCR products were generated using the plasmid pGP704 I-SceIKan [52] as template and primers containing MCMV-homologue sequences and constructs to be introduced in the MCMV genes on their 3’ ends . Each linear construct was generated by 2 rounds of PCR amplification [52] , and contained sequences of the antigenic peptides inserted in the MCMV genome and sites of homologies allowing targeting to specific sites in the virus genome . All primers used in the study are listed in S1 Table . Upon cloning on chloramphenicol LB-agar plates insertion was confirmed by colony PCR and sequencing of the insertion site . To generate MCMVE6+E7 , the fragments containing the entire E6 and E7 genes of HPV16 were cloned into pOriR6K-ie-zeo [53] . The resulting plasmid pO6-ie-E6+E7 was inserted into pSM3fr-Δ1-16-FRT [21] via Flp-mediated recombination [53] resulting in pSM3fr-Δ1-16-FRT- ie-E6+E7 . The same E6 and E7 gene sequence was fused on the 3’-end of the ie2 gene in a pSM3fr MCMV BAC [54] by homologous recombination to generate pSM3fr-ie2E6+7 . All viruses used in this study are listed in S2 Table . Reconstitution of MCMV from recombinant BACs was done by transfection of MEFs as described previously [8] . BAC-derived wild-type [55] and all recombinant MCMVs used in the study were propagated on M2-10B4 cells and virus stocks were purified on sucrose gradient as described previously [37] . Virus stocks were titrated on MEFs as described before [8] . MCMV from organ homogenate or tissue culture supernatants were titrated on MEFs as described previously [56] . Recombinant VACV expressing an immunodominant peptide from HSV-1 ( VACVSL ) was obtained from Dr . J . Nikolich-Zugich , University of Arizona , and grown on Vero cells [57 , 58] . In vitro growth fitness of the recombinant viruses was determined on NIH 3T3 cells as described before [8] . For infection with centrifugal enhancement , plates were centrifuged at 2000rpm for 30 min , incubated at 37°C , 5% CO2 for another 30 min , upon which the supernatants were replaced with fresh medium . This procedure increases virus infection by a factor of 20 and thus a nominal MOI of 0 . 2 with centrifugal enhancement equals an MOI of 4 without it . Mice ( 6–12 weeks old ) were intraperitoneally infected and housed in SPF conditions throughout the experiment . Infected mice showing very weak immune priming ( 2 standard deviations below average ) at 7 dpi were regarded as outliers due to suboptimal infection and were excluded from the study . 129Sv mice were i . p . infected with 2x105 PFU of MCMVWT or MCMVM45SL . Eight months later , mice were i . p . challenged with 106 PFU of recombinant VACVSL . Ovaries were harvested at 7 days post challenge and infectious VACV titers established by plaque assay on Vero cells . For tumor challenge experiments animals were i . p . primed and boosted at 4 weeks interval with recombinant MCMV as indicated . Prior to challenge with 2 . 5x104 exponentially growing TC-1 tumour cells [22] mice were shaved on the hind left flank and s . c . injected . Mice were followed for tumor growth and tumor volume calculated by length x height x 0 . 5 . Bone marrow cells were isolated from tibias and femurs of B6 . 129S2-Tap1tm1Arp/J or C57BL/6J mice ( CD45 . 2 expressing ) , depleted of T cells with anti-PE MicroBeads ( Milteniy Biotec ) and CD90 . 2-PE ( BioLegend ) antibody according to the manufacturer protocol . Recipient mice ( B6 . SJL-Ptprca Pepcb/BoyJ ) were gamma-irradiated with a lethal dose of 9 . 5–10 Gγ , and 6–8 hours upon irradiation received 3–5×106 bone marrow cells . Within the first two weeks upon reconstitution , mice were prophylactically treated with enrofloxacin . Experiments on BMC mice were performed 12 weeks after reconstitution . Splenocytes from C57BL/6 mice ( latently ( >3 months ) infected with MCMVWT ) , were isolated for CTL generation as described before [49] with minor modifications . IL-2 was added at a concentration 200U/ml , the peptide at a concentration 10-10M . CTL were used after the 2nd round of re-stimulation . Target cells were seeded in 96-well plates . Infection was performed at an MOI 0 . 2 with centrifugal enhancement ( ~MOI 4 in standard conditions ) as described above . After 1h infection , infectious supernatants were removed and effector cells ( either in vitro generated M45 ( Db ) or ex vivo harvested gBT-I . 1 CD8 T-cells ) were added at an E:T ratio of 3:1 . The cells were incubated at 37°C for 1h , upon which brefeldin A ( Cell Signaling Technology ) was added at a concentration 10μg/ml and cells were incubated for 14h . For proteasomal inhibition experiments , target cells were pretreated for 5h with indicated inhibitors . LSECs were infected with indicated viruses at an MOI 2 with centrifugal enhancement . Cells were harvested 24 hpi and cell lysates were prepared with complete RIPA buffer with 2x protease inhibitor cocktail mix ( Roche ) . The lysates were sonicated with Bioruptor Plus at low intensity and MHC-I molecules were immunoprecipitated with GammaBind Plus Sepharose Beads ( GE Healthcare ) coupled with anti-mouse H-2D ( b ) ( Clone 28-14-8 , BD Pharmingen ) . Immunoprecipitates were stored at -70°C until MS analysis . Peptides were eluted from the immunoprecipitated pMHC complexes with 0 . 2% trifluoroacetic acid ( TFA ) in water , subjected to ultrafiltration with a cut-off of 10 kDa ( Vivacon 500 filters , Sartorius Stedim Biotech ) , desalted with OMIX C18 10–100 μL pipette tips ( Agilent Technologies ) and vacuum dried . Samples were re-suspended in 3% acetonitrile ( ACN ) , 0 . 1% formic acid ( FA ) and 0 . 01% TFA in water prior to LC-MS analysis . Nano ultra-performance liquid chromatography mass spectrometry ( nano-UPLC-MS ) analysis was performed using a NanoAcquity UPLC system ( Waters Corp . ) coupled to a QTRAP6500 ( AB SCIEX ) mass spectrometer equipped with a nano-ESI ( electron spray ionization ) source . Samples were separated on a nanoAcquity UPLC BEH C18 analytical column ( 0 . 075 x 250mm ) ( Waters ) . The LC separation started with 97% eluent A ( 0 , 1% FA and 0 , 01% TFA in water ) to 10% eluent B ( 0 , 1% FA and 0 , 01% TFA in ACN ) by 1 min with a linear gradient and then to 40% eluent B by 50 min with a linear gradient . The flow rate was set to 300nL/min . The mass spectrometer was operated in a low mass hardware profile operating in positive mode . The nano-ESI voltage was set at 2700 V , curtain gas at 30 L/min , ion source gas at 15 L/min , collision gas ( CAD ) high and interface heater temperature at 150°C . The resolution of the first ( Q1 ) and third quadrupole ( Q3 ) was set at unit resolution . Synthetic reference peptides for target and control epitopes were provided by the in-house DKFZ core facility with an analytical purity of >95% . Known cytoskeletal and housekeeping-protein derived H2-Db-restricted epitopes were used as positive controls [59 , 60] . A minimum of four fragments with the best signal-to-noise ratio were assigned per peptide during direct injection of synthetic peptides . Critical MS parameters ( e . g . , declustering potential , collision energy ) were manually optimized to achieve the best sensitivity for the following peptides ( precursor ion m/z: fragment ion m/z ) : HGIRNASFI ( 507 . 78: y8 877 . 49; b82+ 442 . 23; a82+ 428 . 23; b7-H2O 718 . 37; b7 736 . 38 ) ; FGPVNHEEL ( 521 . 25: y72+ 419 . 21; b82+ 455 . 71; y5 641 . 29; y8 894 . 43 ) ; KALINADEL ( 493 . 78: b8 855 . 46; MH-H2O2+ 484 . 77; b7 726 . 41; b6 611 . 39 ) ; AALENTHLL ( 491 . 27: b5-NH3 482 . 22; y72+ 420 . 23; y7 839 . 46; y6 726 . 38 ) . The MS results of synthetic peptides were manually compared to the MS results acquired in the IP sample using the Analyst 1 . 6 . 2 ( AB SCIEX ) software . Identity of the targeted peptides was confirmed by their retention times , chromatographic profiles and MS3 spectra of each fragment . Targeted nanoLC-MS3 was performed to ensure sufficient sensitivity and specificity of MS results . Retention times ( Fig 5C ) and MS3 spectra ( S3C Fig ) of the IP samples were compared to the results of the synthetic HGIRNASFI reference peptide . Blood sample collection , processing and subsequent stimulation of blood lymphocytes with indicated peptides and intracellular cytokine staining was performed as described before [8] . The antibody panels used for the samples stimulated with the SSIEFARL peptide or stained with SSIEFARL-Kb tetramers shown previously [8] were expanded with anti-CD62L-eFluor605NC ( MEL-14 , eBioscience ) for tetramer staining or with anti-KLRG1-Biotin ( Clone 2F1; Biolegend ) and anti-CD127-PE ( Clone A7R34; Biolegend ) for peptide re-stimulation . For intracellular cytokine staining of the samples stimulated with the HGIRNASFI peptide we used the following antibody panel: anti-CD4-Pacific Blue ( Clone GK1 . 5; Biolegend ) ; anti-CD8a-PerCP/Cy5 . 5 ( Clone 53–6 . 7; Biolegend ) ; anti-CD44-Alexa Fluor 700 ( Clone IM7; Biolegend ) ; anti-CD11a-PE-Cy7 ( Clone 2D7; BD Bioscience ) ; anti-CD3-APC-eFluor 780 ( Clone 17A2 , eBioscience ) , anti-CD127-PE ( Clone A7R34; Biolegend ) , anti-KLRG1-Biotin ( Clone 2F1; Biolegend ) , Streptavidin-Briliant Violet-570 ( Biolegend ) . For intracellular staining we used anti-IFNγ . Following antibodies were used for characterization of the immune response in bone marrow chimeras: anti-CD4-Pacific Blue ( Clone GK1 . 5; Biolegend ) ; anti-CD8a-PerCP/Cy5 . 5 ( Clone 53–6 . 7; Biolegend ) ; anti-CD44-Alexa Fluor 700 ( Clone IM7; Biolegend ) ; anti-CD11a-PE-Cy7 ( Clone 2D7; BD Bioscience ) ; anti-CD3-APC-eFluor 780 ( Clone 17A2 , eBioscience ) ; HGIRNASFI-Db tetramers-APC . For responses to the HPV-E7 epitope cells were by stained with anti-CD8a PerCP-Cy5 . 5 ( clone 53–6 . 7; BD Bioscience ) , anti-CD4-FITC ( clone RM4-5; Biolegend ) , H-2D ( b ) E7 ( RAHYNIVTF ) PE Dextramer ( Immudex ) . In experiments on LMP7-/- mice , we used anti-mouse CD8 , CD44 , KLRG-1 , CD27 , CD127 and CD62L ( all eBioscience ) and the Live/dead fixable near-infrared dead cell stain kit ( Life-Technologies , Paisley , UK ) . The effector cells from co-culture experiments were transferred in fresh 96-wells and stained with the following antibodies: anti-CD4-Pacific Blue ( Clone GK1 . 5; Biolegend ) ; anti-CD8a-PerCP/Cy5 . 5 ( Clone 53–6 . 7; Biolegend ) ; anti-IFNγ-APC ( Clone XMG1 . 2; Biolegend ) and anti-TNFα-FITC ( Clone MP6-XT22 , BioLegend ) . For quantification of MHC class I expression on LSECs or MEFs , cells were trypsinized , washed in 1xPBS and stained for 30 min with anti-MHC-I ( H-2Db ) -PE ( Clone 28-14-8 , Biolegend ) . Cells were acquired in BD LSR-II or BD LSRFortessa cytometers ( BD Bioscience ) . Cytometric results were analyzed with FlowJo software ( version 9 . 5 . 3 ) . Statistical analysis was performed using GraphPad Prism program ( version 5 . 04 ) . Kruskal-Wallis analysis followed by Dunns post-analysis was used to compare multiple samples at single time points . Comparisons between two groups were performed using the Mann—Whitney U test ( two-tailed ) . | Experimental cytomegalovirus ( CMV ) based vaccine vectors have provided highly encouraging results as innovative vaccine formulations against deadly virus infections , such as Ebola or AIDS . Nevertheless , it has remained incompletely understood why CMV is so efficient at stimulating T-lymphocytes , the immune cells that recognize pathogens within infected cells . We have generated an array of CMV mutants expressing the same antigen in different genes or in different parts of the same gene . This allowed us to identify that the immediate environment of the antigen , rather than properties of the antigen itself , crucially determine the immune protection conferred by CMV-based vaccines , implying that optimal immunity depends on the ability of host cells to degrade CMV proteins into peptides , short units that are recognized by T-cells . Detailed analysis revealed that strong and sustained T-cell immunity occurs only when their antigenic targets are processed by a primitive cellular machinery that is present in all cells of the body , rather than by its newly-evolved counterpart , which is present only in specialized antigen-presenting cells . Most importantly , our results provide a simple strategy to develop improved CMV vaccines by positioning the antigenic peptides at the right spot in CMV proteins . | [
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] | 2016 | Peptide Processing Is Critical for T-Cell Memory Inflation and May Be Optimized to Improve Immune Protection by CMV-Based Vaccine Vectors |
Myotubularin MTM1 is a phosphoinositide ( PPIn ) 3-phosphatase mutated in X-linked centronuclear myopathy ( XLCNM; myotubular myopathy ) . We investigated the involvement of MTM1 enzymatic activity on XLCNM phenotypes . Exogenous expression of human MTM1 in yeast resulted in vacuolar enlargement , as a consequence of its phosphatase activity . Expression of mutants from patients with different clinical progression and determination of PtdIns3P and PtdIns5P cellular levels confirmed the link between vacuolar morphology and MTM1 phosphatase activity , and showed that some disease mutants retain phosphatase activity . Viral gene transfer of phosphatase-dead myotubularin mutants ( MTM1C375S and MTM1S376N ) significantly improved most histological signs of XLCNM displayed by a Mtm1-null mouse , at similar levels as wild-type MTM1 . Moreover , the MTM1C375S mutant improved muscle performance and restored the localization of nuclei , triad alignment , and the desmin intermediate filament network , while it did not normalize PtdIns3P levels , supporting phosphatase-independent roles of MTM1 in maintaining normal muscle performance and organelle positioning in skeletal muscle . Among the different XLCNM signs investigated , we identified only triad shape and fiber size distribution as being partially dependent on MTM1 phosphatase activity . In conclusion , this work uncovers MTM1 roles in the structural organization of muscle fibers that are independent of its enzymatic activity . This underlines that removal of enzymes should be used with care to conclude on the physiological importance of their activity .
X-linked centronuclear myopathy ( XLCNM , also called myotubular myopathy; OMIM 310400 ) is a recessive congenital muscle disorder affecting mainly males and due to mutations in the MTM1 gene coding for the phosphoinositides ( PPIn ) phosphatase myotubularin [1] . The most severe form of XLCNM is characterised by hypotonia at birth , muscle atrophy , generalized muscle weakness and respiratory failure leading to high neonatal mortality [2] . Milder clinical phenotypes and progression were also reported and some are compatible with nearly normal lifespan [3] . Muscle biopsies from XLCNM patients show hypotrophic muscle fibers with an abnormal central positioning of nuclei . A mouse model lacking the MTM1 protein ( Mtm1 KO ) has been characterized and reproduces the muscle mass decrease and most histopathological features of XLCNM , including muscle fibers hypotrophy and abnormal organelles positioning [4] . While the MTM1 gene is ubiquitously expressed , skeletal muscle is the tissue mainly affected . To date almost 200 different disease-causing mutations have been identified in the MTM1 gene [3] , [5]–[7] . Most mutations cause severe forms of the myopathy characterized by a strong decrease in the protein level , at least in fibroblasts or lymphoblasts , whereas others cause milder forms of the disease [8] , [9] . A very mild XLCNM phenotype was even described in a 67-year-old grandfather with a N180K missense mutation [3] . However , the genotype-phenotype correlation is not extensive and the importance of the PPIn phosphatase activity in the disease phenotype was not defined . Myotubularin ( MTM1 ) displays PPIn 3-phosphatase activity and converts phosphatidylinositol 3-phosphate ( PtdIns3P ) into PtdIns and phosphatidylinositol 3 , 5-bisphosphate ( PtdIns ( 3 , 5 ) P2 ) into PtdIns5P . The PtdIns3P phosphatase activity of myotubularin was identified in a purified protein complex in brain and confirmed in vitro and ex vivo after the isolation of the cDNA [10] , [11] . The catalytic site and mechanism of MTM1 resembles those of dual-specificity protein phosphatases . Indeed , mutation of the catalytic cysteine of MTM1 into serine ( C375S , phosphatase-dead ) totally abolished its enzymatic activity [12]–[14] . PtdIns3P produced by the PtdIns 3-kinase hVPS34/Vps34 , is enriched at early and late endosomes and is essential for endosomal protein sorting and trafficking , autophagy and proper morphology of the endosomal compartment in human and yeast cells ( for a review see [15] ) . PtdIns3P is also produced by class II PtdIns 3-kinases in multicellular eukaryotes , while these kinases are absent in yeasts [16] , [17] . PtdIns ( 3 , 5 ) P2 , the other substrate of MTM1 , is generated from PtdIns3P by the 5-kinase PIKfyve/Fab1 . The absence of PtdIns ( 3 , 5 ) P2 resulting from impairment of PIKfyve activity in mammalian cells , or from FAB1 gene deletion in yeast S . cerevisiae , leads to a swollen or enlarged endosomal/lysosomal compartment associated with retrograde endosomal trafficking defects ( for a review see [18] ) . The study of MTM1 in human cells is hampered by the presence of highly conserved paralogues , termed MTMR for myotubularin related proteins . There are thirteen MTMR proteins ( MTMR1 to MTMR13 ) , seven of which are active phosphatases while the other six are dead phosphatases lacking key catalytic residues . S . cerevisiae contains only one member of the myotubularin family , Ymr1 ( yeast myotubularin related-1 ) encoded by the YJR110W gene [19] , [20] . Ymr1 displays PtdIns 3-phosphatase activity in vitro and in vivo and deletion of the YMR1 gene leads only to minor phenotypes [12] , [21] . The aim of this study is to understand the importance of the MTM1 PPIn phosphatase activity in the phenotype and severity of the disease . We first analyzed at the cellular level the functional impact of MTM1 mutations isolated from patients using yeast S . cerevisiae ymr1Δ deletion strains . Among the described mutations in MTM1 , we have chosen to enzymatically characterize missense mutations affecting different MTM1 domains that lead to severe , mild or very mild XLCNM forms . We then analyzed the morphology of the vacuolar/lysosomal compartment , the subcellular distribution of these proteins and the intracellular levels of the different PPIn . The yeast results show that some disease mutants display phosphatase activity . In parallel , we performed rescue experiments for the XLCNM-like phenotypes displayed by the Mtm1 KO mice using adeno-associated viral gene transfer of murine wild-type , phosphatase-inactive C375S or S376N constructs . Taken together , our data show that phosphatase-dead MTM1 mutants ameliorated most phenotypes of knock-out mice thus suggesting that myotubularin displays phosphatase-independent functions to maintain normal skeletal muscle .
We analyzed in yeast S . cerevisiae the expression of four missense mutations in MTM1 , two affecting the PPIn interaction PH-GRAM domain ( MTM1V49F and MTM1R69C ) , one the protein-protein interaction domain RID ( MTM1N180K ) and one the phosphatase catalytic domain ( MTM1R421Q ) , leading respectively to severe , mild or severe ( depending on the family ) , very mild , and severe XLCNM forms ( Figure 1A ) [3] , [5] , [6] , [22] . We used as controls the wild-type MTM1 and the artificial phosphatase-dead MTM1C375S mutant [12] . The different MTM1 constructs were expressed into the ymr1Δ yeast mutant from either low ( CEN ) or high ( 2 µ ) copy number plasmids . Growth curves ( optical density of liquid cultures as a function of time ) as well as drop tests revealed that none of the plasmids induced a significant growth defect and that only cells expressing MTM1 or MTM1R69C showed a slight growth delay ( Figure S1 ) . The Western blot analysis shows that the different human MTM1 proteins were produced at the expected molecular weight ( 70 kDa ) and that protein levels in cells transformed with CEN plasmids were lower than in cells transformed with 2 µ plasmids ( Figure 1B ) . Nonetheless , there were differences in the levels of the different MTM1 produced . Indeed , mutants having little or no effect on growth , like the phosphatase-dead MTM1C375S , were most abundant while mutants delaying growth , like MTM1R69C , were least abundant suggesting that yeast cells regulate the production of the exogenous human proteins . The expression of MTM1 in fission yeast Schizosaccharomyces pombe induced an enlarged vacuolar phenotype [13] , [23] . This was also observed upon expression of MTMR3 in S . cerevisiae and the enlarged vacuolar phenotype was correlated with MTMR3 PPIn 3-phosphatase activity [24] . We stained the vacuolar membrane with the lipophilic fluorescent dye FM4-64 in ymr1Δ cells upon MTM1 or MTM1C375S production ( pVV204 , CEN ) or overproduction ( pVV200 , 2 µ ) . Wild-type yeast cells show unilobar vacuoles , whereas the ymr1Δ mutant displayed small multilobar vacuoles ( Figure 2 ) . In contrast , MTM1 overproduction resulted in larger cells with a small or large unilobar vacuole , whereas MTM1C375S overproduction resulted in no obvious change in cell morphology ( Figure 2A ) . The vacuolar morphologies were classified by microscopic observation into three categories: large unilobar or giant , small one or two lobes and more than two lobes or fragmented vacuoles ( Figure 3 ) . A significantly higher percentage of cells with abnormally large vacuoles was observed upon MTM1 production and this was increased with overproduction , whereas this percentage was low for MTM1C375S and similar to the empty plasmid controls ( Figure 3 ) . The enlarged vacuole phenotype could be due to the 3-phosphatase activity of MTM1 or to loss of Fab1 kinase functions upon MTM1 production . Indeed , the fab1Δ mutant also displays the enlarged vacuole phenotype ( Figure 2A ) [25] . Osmotic stress in wild-type yeast results in vacuolar fragmentation ( Figure 2A ) , due to stimulation of Fab1 kinase activity [26] , [27] . The ymr1Δ cells producing either MTM1 or phosphatase-dead MTM1C375S displayed fragmented vacuoles upon osmotic shock , whereas in the fab1Δ mutant vacuoles remained enlarged ( Figure 2A ) , indicating that overproduction of MTM1 did not block the Fab1 kinase activity . A similar result was previously obtained with S . cerevisiae cells expressing human MTMR3 [24] . These results show that MTM1 phosphatase activity is directly responsible for the enlarged vacuole phenotype observed in yeast cells . Thus , analysis of vacuolar morphologies can be used to assess the MTM1 enzymatic activity of mutants found in XLCNM patients . The vacuolar morphology of the ymr1Δ mutant expressing or overexpressing the four MTM1 constructs with XLCNM mutations was analyzed ( Figure 1A ) . Many cells producing MTM1V49F , MTM1R69C and MTM1N180K displayed enlarged vacuoles ( Figure 2B ) . Producing or overproducing the MTM1R421Q in the ymr1Δ mutant did not result in major changes in vacuolar morphology , suggesting that this mutation strongly impairs the enzymatic activity in vivo . Upon production ( CEN ) and overproduction ( 2 µ ) of MTM1V49F or MTM1N180K , the percentage of cells with small and large unilobar vacuoles increased as observed for MTM1 ( Figure 3 ) . Thus MTM1V49F and MTM1N180K responsible for severe and very mild forms of XLCNM respectively display a phosphatase activity in vivo . Production ( CEN and 2 µ ) of MTM1R69C resulted in similar or higher numbers of enlarged vacuoles as compared to MTM1 ( Figure 3 ) . As for MTM1 , osmotic stress also induced fragmentation of the vacuole in cells expressing the different MTM1 with XLCNM mutations ( Figure 2B ) confirming that Fab1 kinase activity was not impaired . To determine whether similar enlarged vacuolar phenotypes were also observed in wild-type yeast cells ( WT SEY6210 ) upon MTM1 expression , we analyzed the vacuolar morphology of WT cells transformed with pVV200 or pVV204 empty plasmids or coding for MTM1 or the different MTM1 mutants ( Figure S2 ) . The expression of the MTM1 , MTM1V49F , MTM1R69C and MTM1N180K constructs induced an increase in the vacuolar size in both WT and ymr1Δ mutant cells , showing that this phenotype was not specific for the latter yeast mutant cells . Taken together , our data show that the vacuolar phenotypes induced by the different MTM1 mutants are not reflecting the severity of XLCNM phenotypes . The enlarged vacuolar phenotypes observed upon production of various MTM1 proteins in yeast cells suggest that these human phosphatases have access to their membranous PPIn substrates . To determine their intracellular distribution , protein extracts from ymr1Δ cells producing the different MTM1 proteins were subjected to subcellular fractionation to separate the membrane fractions P13 and P100 from the cytosolic fraction S100 ( Figure S3 ) . MTM1 was found mainly in P13 and P100 fractions and a similar fractionation was observed for the different MTM1 mutants . This membrane association of MTM1 , despite the absence of a transmembrane domain or a lipid anchor , suggests that MTM1 interacts with lipids or proteins independently of its phosphatase activity since MTM1C375S was also membrane-associated . These results show that the different vacuolar phenotypes observed upon production of the different MTM1 proteins are not due to differences in their subcellular distribution . The vacuolar phenotypes suggest that MTM1 , MTM1V49F , MTM1R69C or MTM1N180K dephosphorylate PtdIns3P and PtdIns ( 3 , 5 ) P2 in yeast cells . To assess their phosphatase activity in vivo we determined the intracellular levels of PtdIns3P and PtdIns5P in ymr1Δ cells producing the different MTM1 constructs . Cells were labeled with 32P , lipids extracted and separated by thin-layer chromatography ( TLC ) and spots corresponding to phosphatidylinositol-monophosphates ( PtdInsP ) and phosphatidylinositol-bisphosphates ( PtdInsP2 ) were isolated , deacylated and resolved by anion-exchange HPLC chromatography . Four different PPIn are identified in yeast S . cerevisiae: PtdIns3P , PtdIns4P , PtdIns ( 3 , 5 ) P2 and PtdIns ( 4 , 5 ) P2 . The relative abundance of [PtdIns3P , PtdIns4P , PtdIns ( 3 , 5 ) P2 , PtdIns ( 4 , 5 ) P2] is 40∶40∶7∶13 in wild-type SEY6210 strain whereas in fab1Δ mutant strain this ratio changes to 74∶21∶0∶5 [25] . It was previously shown that in ymr1Δ cells PtdIns3P levels are 2-fold higher than in wild-type cells and represent 82% of the total PtdInsP species [12] . To compare the phosphatase activity of the different MTM1 constructs , we calculated the percentage of PtdIns3P over total PtdInsP for the different strains ( Figure 4A ) . This showed that MTM1R421Q mutant affecting a residue in the catalytic pocket displayed a poor phosphatase activity , as PtdIns3P levels were comparable to those of the phosphatase-dead MTM1C375S control . In contrast , the three other XLCNM patient mutants MTM1V49F , MTM1R69C and MTM1N180K showed PtdIns3P phosphatase activity comparable to the wild-type MTM1 , as they displayed a strong decrease in the PtdIns3P levels which represented only 45–50% of total PtdInsP ( Figure 4A ) . These results were confirmed by in vitro phosphatase assays [28] done on MTM1 , MTM1C375S , MTM1V49F , MTM1R69C and MTM1N180K proteins immuno-isolated from yeast ymr1Δ cells ( Figure S4 ) . Among the different PPIn detected in yeast cells , the PtdIns ( 3 , 5 ) P2 is the least abundant and represents about 0 . 1% of the total inositol phospholipids [18] . Indeed , HPLC chromatograms of PtdInsP2 showed that under our experimental conditions PtdIns ( 3 , 5 ) P2 was barely detectable in normal conditions for the different strains ( not shown ) . PtdIns ( 3 , 5 ) P2 intracellular levels can be increased by osmotic shock [26] . To avoid any osmotic stress treatment of the cells and to detect PtdIns ( 3 , 5 ) P2 dephosphorylation by MTM1 under normal conditions , we quantified the resulting product PtdIns5P by a sensitive mass assay [28] , [29] . Thus ymr1Δ yeast cells producing MTM1 , MTM1C375S , MTM1V49F , MTM1R69C , MTM1N180K and MTM1R421Q were grown to exponential phase , lipids were extracted and separated by TLC , and spots corresponding to PtdInsP were extracted and submitted to an in vitro kinase assay to detect PtdIns5P . The ymr1Δ cells expressing MTM1C375S or the empty plasmid showed a basal level of PtdIns5P , whereas in the presence of active MTM1 there was a strong increase in PtdIns5P ( Figure 4B ) . Comparison of PtdIns5P levels showed that MTM1R421Q can be considered as an inactive phosphatase , as it displayed similar levels to MTM1C375S ( Figure 4B ) . The three other XLCNM patient mutants MTM1V49F , MTM1R69C and MTM1N180K displayed a PtdIns ( 3 , 5 ) P2 phosphatase activity since significant quantities of PtdIns5P were detected ( Figure 4B ) . Based on the p-values ( Figure 4B ) , the PtdIns5P production by the MTM1R69C mutant is not significantly different than the one detected for MTM1 . These results were further confirmed by in vitro phosphatase assays showing proper dephosphorylation of PtdIns ( 3 , 5 ) P2 by MTM1 , MTM1V49F , MTM1R69C and MTM1N180K produced in yeast ymr1Δ cells , whereas in the same conditions the MTM1C375S and MTM1R421Q were less active ( Figure S4 ) . These results show that MTM1 mutants responsible for myopathy are either active or inactive phosphatases . Indeed , the MTM1V49F mutant is associated to severe forms of the disease and displays phosphatase activity , even so its activity is reduced compared to the wild type phosphatase . The second mutant in the PH-GRAM domain of MTM1 , MTM1R69C shows similar phosphatase activity as the wild-type MTM1 and is associated to mild or severe phenotype . In conclusion , not all MTM1 mutants responsible for myopathy lack the phosphatase activity . As results in yeast suggested that some XLCNM patient mutants retain the phosphatase activity , we aimed to investigate the role of the phosphatase activity on the development of the XLCNM phenotype in vivo . We tested the ability of the MTM1C375S phosphatase-dead mutant to correct the XLCNM-like muscle phenotype of Mtm1 knockout ( KO ) mice compared to wild-type MTM1 using Adeno-associated virus ( AAV ) gene transfer . We used the constitutive Mtm1 KO mouse that develops a homogeneous XLCNM in the 129PAS background [30] . These mutant animals show a progressive muscle weakness starting clinically at 3 weeks of age and leading to death by 7 to 9 weeks , probably from respiratory failure . They display most phenotypes observed in patients as a decrease in muscle mass , muscle fiber hypotrophy , nuclei and mitochondria positioning defects , desmin aggregation , and alteration in T-tubule structure . At 6 weeks old , Mtm1 KO mice injected with empty AAV vector show a 38% decrease in the Tibialis anterior ( TA ) muscle weight compared to wild-type mice injected with empty AAV ( Figure 5C ) . Mtm1 KO TA muscles displayed smaller and rounder myofibers with increased proportion of internal nuclei compared to wild-type mice ( Figure 5B and 5D ) . In addition , Mtm1 KO muscles had an abnormal oxidative staining with higher intensities in the subsarcolemmal region and in the center of fibers , reminiscent of an accumulation of mitochondria at these regions ( Figure 5B ) . TA muscles of 2–3 weeks old Mtm1 KO mice were injected with either AAV2/1-Mtm1-WT ( AAV-Mtm1-WT ) or AAV2/1-Mtm1-C375S ( AAV-Mtm1-CS ) , where the C375S mutation abolishes the phosphatase enzymatic activity towards PPIn substrates ( Figure 4 ) . The contralateral muscle was injected with AAV2/1-Empty ( AAV ) as an internal control . The effect of AAV-mediated Mtm1-WT or Mtm1-CS expression was analyzed 4 weeks after injection . The level of ectopic Mtm1-WT and Mtm1-CS expression was analyzed by western blot in injected muscles . In both injected muscles the level of the protein was reestablished and similar to the levels of the endogenous protein in WT muscle ( Figure 5A ) . We confirmed that exogenous expression of Mtm1-WT in TA muscle corrects the XLCNM-like phenotype in the constitutive 129PAS Mtm1 KO mice , as previously shown in muscle-specific KO mice on a B6 background [31] , showing that the MTM1 protein is acting primarily in skeletal muscle and not in other tissues as only muscle was injected . The Mtm1 KO model displays a severe muscle atrophy ( Figure 5 and [4] , [30] ) . Muscles injected with AAV-Mtm1-CS showed a significant increase of the weight compared to Mtm1 KO muscle injected with AAV ( 0 , 13%±0 , 03 for AAV-Mtm1-CS compared to 0 , 08%±0 , 02 for AAV alone ) , reaching similar levels to Mtm1 KO injected with Mtm1-WT ( Figure 5C ) . Next we investigated if the increase in weight correlates with an improvement at the histological level . Hematoxylin and eosin ( HE ) staining revealed similar improvement of the histological aspects of Mtm1 KO muscles injected with either AAV-Mtm1-WT or AAV-Mtm1-CS ( Figure 5B ) . Quantitative analysis of the distribution of myofiber areas showed a clear increase in fiber size for both AAV-Mtm1-WT and AAV-Mtm1-CS treated muscles compared to Mtm1 KO muscles ( Figure 5E ) . None of the constructs restored fiber area and the muscle weight to the level of wild-type mice 4 weeks after infection and it was not possible to test if longer infection would increase the correction of these features under our experimental conditions as mice were dying around the age of analysis . Fiber area distribution is different comparing AAV-Mtm1-WT and AAV-Mtm1-CS; AAV-Mtm1-CS leads to a higher increase in the number of fibers with an area in the range of 200 to 3200 µm2 , compared to fibers with an area superior to 3200 µm2 with AAV-Mtm1-WT . Our data show that both AAV-Mtm1-WT and AAV-Mtm1-CS partially but significantly improved muscle atrophy and fiber hypotrophy of the Mtm1 KO mice . Furthermore Mtm1 KO muscle fibers are also characterized by a progressive disorganization in the distribution of mitochondria . Thus , we evaluated the localization of these organelles by succinate dehydrogenase staining ( SDH ) that labels the oxidative activity . The SDH staining of muscle sections ( Figure 5B ) revealed that the abnormal central concentration of oxidative activity was improved with both AAV-Mtm1-WT and AAV-Mtm1-CS . In addition , abnormal internalization of nuclei represents another hallmark of the XLCNM pathology . We thus counted the number of fibers with internal nuclei ( not in contact with the sarcolemma ) in wild-type and Mtm1 KO muscles injected with AAV versus Mtm1 KO muscles treated with AAV-Mtm1-WT or AAV-Mtm1-CS ( Figure 5D ) . We observed a strong and similar reduction of the percentage of internal nuclei in AAV-Mtm1-WT and AAV-Mtm1-CS treated muscles compared to Mtm1 KO muscles ( 21 , 15%±8 , 23 for AAV-Mtm1-CS; 15 , 68%±0 , 62 for AAV-Mtm1-WT compared to 46 , 67%±8 , 18 for Mtm1 KO and 2 , 28%±2 , 11 for wild-type mice ) . To determine whether the significant improvement of the histological features was associated to improved muscle performance , we measured the in situ force of the muscle . The isolated muscle was stimulated by the sciatic nerve , and the maximal force produced was recorded and normalized to muscle weight ( Figure 6 ) . The specific maximal force of untreated TA muscles of 6 week-old Mtm1 KO mice was lower by 81% compared to wild-type muscle . The muscles transduced with AAV-Mtm1-CS and AAV-Mtm1-WT exhibit an increase of the specific maximal force compared to Mtm1 KO ( 0 , 44 mN/mg±0 , 23 for AAV-Mtm1-CS; 0 , 66 mN/mg±0 , 17 for AAV-Mtm1-WT compared to 0 , 06 mN/mg±0 , 04 for Mtm1 KO and 1 , 31 mN/mg±0 , 21 for wild-type mice ) ( Figure 6 ) . Altogether , our results show that the MTM1C375S phosphatase-dead mutant improves most XLCNM-like histological and the specific muscle force of the Mtm1 KO model at a level comparable to that of the wild-type MTM1 protein . To further decipher the molecular basis for these phosphatase-independent improvements we analyzed the localization of desmin , a muscular MTM1 protein interactor . Indeed , it was recently shown that MTM1 binds specifically desmin and regulates the filament assembly and organization [32] . Desmin is the major component of intermediate filaments ( IFs ) cytoskeleton of muscle , which plays a central role in the integration of structure and function of striated muscle by linking the contractile apparatus to the sarcolemmal cytoskeleton as well as to several cytoplamic organelles and the nucleus . Desmin is found mainly in the Z-disk in a normal skeletal muscle . In the muscle biopsies from XLCNM patients , desmin localization is altered . The Mtm1 KO mice muscles present an accumulation of aggregates that disrupt the continuity and organization of the desmin network ( Figure 7A ) . We could previously show that ectopic expression of MTM1-WT in the Mtm1 KO muscle restores the normal organization of the desmin network [32] . We examined the muscle injected with the MTM1C375S phosphatase-dead mutant . The muscle transduced with AAV-Mtm1-CS exhibited a clear improvement of the desmin localization compared to the desmin aggregates observed in the Mtm1 KO muscle injected with the empty virus ( Figure 7A ) . Furthermore , the mislocalization of desmin in Mtm1 KO corresponds also to a shift in desmin equilibrium from the soluble to the insoluble fraction , indicating a defect in the desmin assembly process ( Figure 7B ) . We observed a significant and similar increase in the desmin solubility in AAV-Mtm1-WT and AAV-Mtm1-CS treated muscles compared to Mtm1 KO muscles ( Figure 7B ) . To confirm that AAV-Mtm1-WT and AAV-Mtm1-CS displayed similar efficiency regarding the correction of desmin solubility we analyzed the level of MTM1 expression in the same samples ( Figure 7C ) . The MTM1 expression was similar in both type of muscles suggesting that phosphatase-dead mutant MTM1C375S improves the desmin organization as efficiently as MTM1-WT . Moreover , we analyzed the distribution of MTM1-WT and MTM1-C375S in the membrane fraction of skeletal muscle using a microsomal preparation from Mtm1 KO muscles injected with AAV-Mtm1-WT and AAV-Mtm1-CS and from wild-type muscle . Microsomal fractions were analyzed using protein markers for the membrane fraction ( α-sarcoglycan and SERCA1 ) , for the cytoplasmic ( β-tubulin ) and for the nuclear fractions ( the TATA-box binding protein ( TBP ) ) . The MTM1-WT and MTM1-C375S proteins ectopically expressed in the Mtm1-KO muscle were similarly distributed in the microsomal fractions ( Figure 7F ) . These results suggest that MTM1-WT and MTM1-C375S localize similarly in the membrane fraction in skeletal muscle . Altogether , our results show that the phosphatase activity of MTM1 is not required for normal desmin localization in muscle fibers , and suggest that maintenance of the desmin network is a phosphatase-independent function of MTM1 that is important in XLCNM . To determine whether the substantial amelioration of the histological features was correlated to the improvement of the structural organization of the triads , we analyzed the muscles by electron microscopy . Indeed , previous studies have shown that the muscles lacking myotubularin as well as XLCNM muscle biopsies present abnormal organization of triads [30] , [33] , [34] . The electron micrographs obtained from the wild-type , Mtm1 KO and Mtm1 KO injected with AAV-Mtm1-WT and AAV-Mtm1-CS muscles were analyzed . The wild-type muscle showed proper organization of the fibers and sarcomere arrangement , and the typical triad structure . In contrast , the micrographs from Mtm1 KO muscle exhibited sarcomere disorganization and a decrease in the number of well-positioned triads ( Figure 8A and 8B ) . Interestingly , the Mtm1-KO muscle injected with AAV-Mtm1-WT showed a clear improvement in the general organization of the sarcomere and the presence of the well–formed triads ( Figure 8A–8C ) . An improvement was also observed in the Mtm1 KO muscle injected with the AAV-Mtm1-CS . The ratio of triads per sarcomere in Mtm1 KO muscles of 6 weeks-old mice was decreased by 83% compared to wild-type muscle . Muscles transduced with AAV-Mtm1-WT and AAV-Mtm1-CS presented a significant increase in this ratio ( 0 , 9±0 , 18 for AAV-Mtm1-CS; 1 , 04±0 , 24 for AAV-Mtm1-WT compared to 0 , 23±0 , 21 for Mtm1 KO and 1 , 2±0 , 3 for wild-type mice ) ( Figure 8B ) . In addition , the shape of the triad was analyzed in the wild-type and in the Mtm1-KO muscle transduced with AAV-Mtm1-WT and AAV-Mtm1-CS . The Mtm1 KO was not considered for this analysis , as recognizable triads were nearly absent in this muscle . The muscle transduced with the phosphatase-dead mutant exhibited recognizable triads with a more dilated shape than the muscle transduced with the AAV-Mtm1-WT and wild-type muscle ( Figure 8C ) . Altogether , our results show that the MTM1C375S phosphatase-dead mutant improves the general organization of the muscle fibers and restores the presence and number of the triads in the Mtm1 KO model at a level comparable to the wild-type MTM1 protein . However the muscle transduced with AAV-Mtm1-CS exhibited only a partial correction of the shape of the triads compared to the AAV-Mtm1-WT , suggesting a potential role of the phosphatase activity in the shape of the triad . To determine whether the MTM1 phosphatase activity contributed to the improvement of the XLCNM phenotypes , we measured the level of PtdIns3P in the different muscles . For these measurements , we extracted total lipids from the tibialis anterior of wild-type , Mtm1-KO and Mtm1-KO muscles injected with AAV-Mtm1-WT or AAV-Mtm1-CS . We used a novel sensitive mass assay for measuring PtdIns3P from total muscle lipid extracts without metabolic labeling [35] . The level of PtdIns3P in the sample was quantified and normalized to total phospholipids , the resulting pmol of PtdIns3P/µmol of phospholipids data were expressed as fold increase compared to the wild-type muscle transduced with AAV ( Figure 9 ) . The lipid extracts from Mtm1 KO mice exhibited a higher level of PtdIns3P compared to wild-type ( 2 . 19 fold increase for Mtm1 KO muscle ) . These data support the conclusion that PtdIns3P is a physiological substrate of MTM1 in mammalian muscle and that the disease is paralleled by an alteration of PPIn metabolism in the Mtm1 KO model . The lipid extracts from the muscles transduced with AAV-Mtm1-WT showed a normalization of the PtdIns3P to levels similar as the wild-type muscle . In contrast , muscles transduced with AAV-Mtm1-CS exhibited , as the Mtm1 KO muscles , higher levels of Ptdns3P compared to the wild-type muscle ( 3 . 3 fold increase for AAV-Mtm1-CS ) ( Figure 9 ) . The difference in PtdIns3P levels between the Mtm1 KO injected with empty AAV and AAV-Mtm1-CS is not statistically significant since the p-value is 0 . 06 . However , there is a tendency towards increased PtdIns3P levels with AAV-Mtm1-CS that could be caused by a substrate-trapping property of this mutant resulting in the protection of PtdIns3P from consumption by other enzymes . Thus , the Mtm1-C375S mutant is catalytically inactive in vivo and might be a substrate-trapping mutant . The results show that the correction of the phenotypes with the MTM1C375S phosphatase-dead was not correlated to normalization of the PtdIns3P levels in muscle . The analysis of the PtdIns3P level in mice muscles did not exclude that the MTM1C375S mutant might be a substrate-trapping mutant . Thus , this mutant could promote the correction of the Mtm1 KO mice phenotypes through a dominant-negative effect by blocking the access of effectors to PtdIns3P and/or PtdIns ( 3 , 5 ) P2 . To address this issue , we used the MTM1S376N ( Mtm1-SN ) mutant associated to severe XLCNM [36] . The S376N mutation located in the catalytic site abrogates the in vitro phosphatase activity [12] and this inactive mutant was predicted to disrupt the substrates binding based on the myotubularin MTMR2 crystal structure [37] . Indeed , the replacement of this serine 376 with a bulkier aminoacid removes the hydrogen bond formed with the oxygen of the D1 phosphate of the lipid substrate and is also predicted to produce an allosteric clash with both the D1 phosphate of the substrate and with two aminoacids of the catalytic pocket ( Asp280 and Asp288 , Figure S5 ) . To determine its in vivo activity , we produced this mutant in yeast ymr1Δ cells , analyzed the vacuolar size and quantified the resulting PtdIns5P product ( Figure S5 ) . The results show that the MTM1S376N protein is produced in yeast cells , and that this MTM1S376N mutant is catalytically inactive as judged from the vacuolar phenotypes and the lack of dephosphorylation of PtdIns ( 3 , 5 ) P2 in PtdIns5P ( Figure S5 ) . Next , we analyzed the major XLCNM-like phenotypes in the Mtm1 KO muscle injected with AAV-Mtm1-SN mutant ( Figure 5 ) . The Mtm1 KO muscles transduced with AAV-Mtm1-CS showed a clear improvement of the muscle concerning the muscle weight , fiber size , organelle and nuclei positioning and a similar improvement was observed for the injection of AAV-Mtm1-SN mutant ( Figure 5 ) . While not excluding that the C375SS mutant could have some substrate-trapping properties , these results strongly suggest that the amelioration of the XLCNM phenotypes described for AAV-Mtm1-CS are not due to a substrate-trap effect of the C375S mutation . As the S376N mutant is also phosphatase-dead , this supports the conclusion that the MTM1 phosphatase activity does not contribute to the maintenance of most XLCNM phenotypes .
In this study , we investigated the involvement of MTM1 enzymatic activity on the phenotypes of XLCNM . Using heterologous expression of human genes in yeast , we showed that the PPIn phosphatase activity of MTM1 was directly linked to vacuolar homeostasis . The vacuolar phenotypes induced by expressing different MTM1 mutants found in patients and their measured impact on PPIn levels revealed that not all MTM1 mutants were associated to inactive phosphatase . In addition , using gene transfer in a murine model of XLCNM , we were able to significantly ameliorate most morphological phenotypes with two different phosphatase-inactive mutants of MTM1 . Altogether , our data strongly suggest that the main roles of MTM1 in adult muscle are largely independent of its enzymatic activity , with the exception of triad shape and fiber size distribution . We report here a sensitive assay to determine human MTM1 phosphatase activity in yeast S . cerevisiae using vacuole size as a read-out . We showed that vacuole size correlates with the levels of intracellular PtdIns3P and PtdIns ( 3 , 5 ) P2 dephosphorylated by MTM1 . In yeast cells , a reverse correlation was observed between the in vivo phosphatase activity and the MTM1 protein level . Indeed , the enzymatically inactive MTM1R421Q and MTM1C375S are the most produced whereas the most active MTM1R69C was the least abundant ( Figure 1B ) . This suggests that yeast cells regulate the levels of human MTM1 to avoid massive deregulation of PPIn levels . This regulation could be post-transcriptional since the same effect was observed with two different replication origins ( 2 µ or CEN-ARS ) combined with two different promoters , the yeast PGK1 and the bacterial tetO promoters . Thus , an equilibrium between intracellular protein levels and PtdIns3P and PtdIns ( 3 , 5 ) P2 dephosphorylation rates may have been reached to ensure yeast growth in the presence of human MTM1 active forms . This is further supported by the fact that despite being massively produced in yeast , enzymatically active MTM1 did not drastically deplete intracellular PtdIns3P but restored similar PtdIns3P levels to the SEY6210 WT strain . It may also reflect a specificity of MTM1 towards distinct intracellular subpools of PtdIns3P . Using different approaches in two eukaryotic models , the yeast S . cerevisiae ymr1Δ and the Mtm1 KO mouse , our results indicate that the XLCNM disease is not solely linked to a defect in MTM1 phosphatase activity . In yeast cells , several XLCNM patient mutants responsible for severe forms of the disease displayed a phosphatase activity comparable to wild-type MTM1 . In this model , the MTM1 phosphatase activity was linked to vacuolar homeostasis , in accordance with the known function of PtdIns3P and PtdIns ( 3 , 5 ) P2 in yeast cells [18] . In the Mtm1 KO mice AAV gene transfer of wild-type MTM1 or phosphatase-inactive MTM1C375S and MTM1S376N mutants significantly improved the XLCNM phenotypes . Comparison of AAV-Mtm1-WT and AAV-Mtm1-CS injected Mtm1 KO mice muscles revealed that ectopic expression of MTM1C375S phosphatase-dead mutant corrected similarly as MTM1 wild-type: the muscle weight , nuclei positioning , oxidative staining and fiber shape ( HE staining ) , desmin localization and solubility , sarcomere organization , the presence of well-oriented triads at the sarcomere and the specific maximal force , whereas the distribution of the fiber size and the triads shape were only partially ameliorated ( Figure 10 ) . Whether the complete correction of these phenotypes requires longer time of expression or the phosphatase activity of MTM1 remains an open question . Thus , apart from PtdIns3P levels , which are mainly dependent on the phosphatase activity , only fiber size distribution and triads shape appear both phosphatase-dependent and phosphate-independent functions of MTM1 . Interestingly , since mice were injected with MTM1C375S or MTM1S376N at 3 weeks when animals start to present some pathological signs , it supports that these dead phosphatases did not only improve but were also able to revert the progression of the disease . Thus , even though the phosphatase activity of MTM1 is very important for its cellular function likely by impacting on vesicular trafficking , the loss of this activity is not responsible for the maintenance of most muscle phenotypes observed in the disease . This strongly suggests that defects in PPIn metabolism and vacuolar homeostasis are not the main cause in the maintenance of XLCNM phenotypes . However , we do not exclude that defect in the regulation of triad shape may affect muscle function at later stages , even so we did not observe significant differences in muscular specific maximal force after 4 weeks of transduction . Based on previous studies and on our results , we favor the hypothesis that the MTM1 phosphatase activity is crucial for the onset of the disease but less important for its maintenance in later stages of the myopathy . In this study only the PtdIns3P levels could be measured in the muscle , thus we cannot exclude that there might be a correlation with restored PtdIns ( 3 , 5 ) P2 , although the MTM1C375S mutant was shown to lack enzymatic activity against both PtdIns3P and PtdIns ( 3 , 5 ) P2 [28] . Moreover , Kiger and colleagues reported that down regulation of PI3K class II ( Pi3K68D ) in drosophila could rescue viability and several defects observed in mutant of mtm , the fly orthologue for MTM1 , MTMR1 and MTMR2 [17] , [38] . It is possible that in fly the role of myotubularins are more tightly linked to the phosphatase activity than in mammals where the diversification of myotubularins may have developed some phosphatase-independent and tissue-specific functions . Mutations responsible for XLCNM are found all along the MTM1 protein [3] , [6] . For most of them , including missense mutations , the MTM1 protein level was strongly decreased or not detectable in fibroblasts , lymphoblasts or myoblasts from patients , suggesting that XLCNM results in most cases from the absence or instability of MTM1 [8] . Recent results show that Mtm1 p . R69C mice model is associated to mild CNM phenotypes with undetectable MTM1 protein levels , however the presence of residual protein that might account for the milder phenotype compared to the Mtm1 KO mice cannot be ruled out [39] . One exception was the MTM1S376N mutant associated to normal protein level in lymphoblast and leading to severe XLCNM [8] , [9] . The MTM1S376N mutant is phosphatase inactive in vitro [12] and in vivo ( Figure S5 ) . However , the level of this MTM1 mutant was not investigated in patients' muscle , as it requires a muscle biopsy from a patient deceased a long time ago in the neonatal period , and thus it is possible that this mutation leads to the instability of the protein in muscle . Moreover , Pierson et al . recently showed that the mutation predicted to lead to the R69C aminoacid change was in fact promoting a splicing defect and loss of the protein in the skeletal muscle of the R69C knock-in mice [39] , suggesting that other missense mutations might impact on splicing in the diseased tissue . As exogenous expression of this MTM1S376N mutant and of the phosphatase-dead MTM1C375S improves muscle atrophy , fiber hypotrophy and organelles positioning defects , we conclude that these pathways are not mainly linked to the phosphatase activity but to other functions of MTM1 . Based on these new findings , we rather propose that the MTM1 myotubularin protein might be a key effector involved in complex protein-protein interactions required for proper muscular functions . Indeed , MTM1 does not display a skeletal muscle-specific expression [40] , whereas the XLCNM disease is mostly restricted to skeletal muscle . This would suggest that MTM1 interacts with muscle specific proteins and is required for their proper localization/function . Among these , the muscle-specific intermediate filament desmin involved in organelle positioning is a good candidate . MTM1 is required for proper desmin localization and assembly and some XLCNM-causing mutations disrupt the MTM1-desmin interaction [32] . Furthermore the MTM1C375S phosphatase-dead mutant restores the abnormal desmin organization in MTM1-deficient muscle , suggesting that the phosphatase activity of MTM1 is not required for normal desmin organization and assembly in muscle fibers . Thus , the maintenance of desmin organization and IFs network is a phosphatase-independent function of MTM1 that is important for maintenance of the muscle structure and function . Our work also shows that a disease due to mutation ( s ) affecting an enzyme is not always associated with loss of the corresponding enzymatic activity . Such dual function of PPIn metabolizing enzymes has been described for class I PI3K . Knock-out mice lacking PI3K protein expression show different phenotypes than knock-in mice expressing a kinase-dead mutant [41] , [42] . Moreover , manipulation of myotubularins or other PPIn regulatory proteins by knock-down , overexpression or specific intracellular targeting is being widely used to decipher the roles of these lipids [43] . Our results call for cautiousness when interpreting the observed effects as they may result from a function unrelated to their enzymatic activity . In conclusion , our data unravel an important and novel aspect of XLCNM as we provide evidences for a scaffolding activity of MTM1 for muscle specific proteins , such as desmin , which appears more important than its phosphatase activity in the maintenance of the XLCNM pathology . Our findings have important implications in the design of therapeutic approaches aiming to manipulate the phosphoinositide level in the different diseases linked to myotubularin homologues . Whether the MTM1 phosphatase activity is also dispensable for the development of the disease and the exact link between PPIn modulation and muscle function remains to be established .
Animals were housed in a temperature-controlled room ( 19–22°C ) with a 12:12-h light/dark cycle . Mice were humanely killed by CO2 inhalation followed by cervical dislocation , according to national and European legislations on animal experimentation . The human MTM1 ORF was cloned into pENTR™1A plasmid ( Invitrogen ) to generate an entry clone . Gateway system ( Invitrogen ) was used to clone the different MTM1 constructs into the yeast expression vectors pVV200 and pVV204 or into a pAAV-MCS vector . S . cerevisiae fab1Δ ( MATα ura3Δ0 , leu2Δ0 , his3-Δ1 , met15-Δ0 , fab1::kanMX4 ) mutant ( EUROSCARF collection ) , ymr1Δ ( MATα ura3-52 , leu2-3 , 112 , his3-Δ200 , trp1-Δ901 , lys2-801 , suc2-Δ9 ymr1::HIS3 ) and WT ( SEY6210 strain; MATα ura3-52 , leu2-3 , 112 , his3-Δ200 , trp1-Δ901 , lys2-801 , suc2-Δ9 ) cells [21] were grown at 30°C in rich medium ( YPD ) : 1% yeast extract , 2% peptone , 2% glucose or synthetic drop-out medium ( SC ) : 0 . 67% yeast nitrogen base without amino acids , 2% glucose and the appropriate amino acids mixture to ensure plasmid maintenance . Yeast cells were lysed by glass beads using a FASTprep ( MP Biomedicals ) in PBS1X , sorbitol 0 . 3 M , Complete Mini EDTA-free protease inhibitor cocktail ( Roche Diagnostics ) and PMSF 1 mM . Lysates were cleared and analyzed by SDS-PAGE and Western blot using mouse monoclonal 1G6 anti-MTM1 ( 1/10 , 000 ) [8] and mouse monoclonal anti-PGK1 ( 1/400 ) ( Invitrogen ) antibodies . Muscles were homogenized in 50 mM Tris , 10% glycerol , 1 mM EDTA , 50 mM KCl , 10 mM beta-glycerophosphate , 10 mM NaF , 1 mM Na3VO4 , 0 . 1% SDS , 2% Triton X-100 and protease inhibitors ( Roche Diagnostics ) using a Polytron homogenizer ( Kinematica Inc . ) . Mouse anti-glyceraldehyde-3-phosphate dehydrogenase ( Chemicon ) and rabbit anti-MTM1 antibody ( R2868 ) were used for detection . FM4-64 ( Invitrogen ) staining was performed as previously described [44] . Labeled yeast cells were observed by fluorescence microscopy ( Axiovert200 , Zeiss ) in SC-trp medium . Cells were counted and classified into different categories: more than two lobes , small one or two lobes and unilobar large or giant vacuoles . Labeling and lipid extraction procedures were done as previously described [45] . ymr1Δ cells expressing MTM1 were grown for 16 h in presence of 40 µCi/ml H332PO4 ( Perkin Elmer ) before lysis by TCA . Lipids were extracted with 95% EtOH∶diethyl ether∶pyridine at 15∶5∶1 v/v . Samples were analyzed by TLC and labeled spots were identified by autoradiography and PPIn standards . Labeled PtdInsP as well as PtdInsP2 were scraped off the plates , collected and deacylated before being analyzed by high-performance liquid chromatography ( HPLC ) Whatman PartiSphere 5 SAX ( 4 . 6×125 mm ) as previously described [46] . ymr1Δ cells producing the different MTM1 were grown to exponential phase , lipids extraction and TLC separation were performed as described above . Spots corresponding to PtdInsP were extracted and submitted to an in vitro kinase assay using recombinant PtdIns5P 4-kinase type IIα in presence of [γ-32P]-ATP [28] , [29] . Among the different PtdInsP species , this kinase specifically phosphorylates PtdIns5P to PtdIns ( 4 , 5 ) P2 and the measured quantity of 32P-PtdIns ( 4 , 5 ) P2 will directly represent the in vivo PtdIns5P intracellular levels . The total lipids from TA muscles were extracted with the Dounce homogenizer using the method of Bligh and Dyer [47] and prepared for mass assay to measure the intracellular PtdnIs3P levels in muscle by a novel mass assay using recombinant PIKfyve kinase in presence of [γ-32P]-ATP [35] . rAAV2/1 vectors were generated by a triple transfection of AAV-293 cell line with pAAV2-insert containing the insert under the control of the CMV promoter and flanked by serotype-2 inverted terminal repeats , pXR1 containing rep and cap genes of AAV serotype-1 , and pHelper encoding the adenovirus helper functions . Viral vectors were purified and quantified by real time PCR using a plasmid standard pAAV-eGFP . Titers are expressed as viral genomes per ml ( vg/ml ) and rAAV titers used here were 5–7 . 1011 vg/ml . Two to three week-old male wild-type and Mtm1 KO 129PAS mice were anesthetized by intraperitoneal injection of 5 µl/body gram of ketamine ( 20 mg/ml , Virbac ) and xylazine ( 0 . 4% , Rompun , Bayer ) . Tibialis anterior ( TA ) muscles were injected with 20 µl of AAV2/1 preparations , or AAV2/1 empty virus solution . Animals were housed in a temperature-controlled room ( 19–22°C ) with a 12:12-h light/dark cycle . Mice were humanely killed by CO2 inhalation followed by cervical dislocation , according to national and European legislations on animal experimentation . TA muscles were dissected 4 weeks after injection and frozen in nitrogen-cooled isopentane and liquid nitrogen for histological and immunoblot assays , respectively . Muscle force measurements were evaluated by measuring in situ muscle contraction in response to nerve and muscle stimulation , as described previously . Animals were anesthetized by intraperitoneal injection of pentobarbital sodium ( 50 mg per kg ) . The distal tendon of the TA was detached and tied with a silk ligature to an isometric transducer ( Harvard Bioscience , Holliston , MA ) . The sciatic nerve was distally stimulated , response to tetanic stimulation ( pulse frequency of 50 to 143 Hz ) was recorded , and absolute maximal force was determined . After contractile measurements , the animals were sacrificed by cervical dislocation . To determine specific maximal force , TA muscles were dissected and weighted . Transverse cryosections ( 8 µm ) of mouse TA skeletal muscles were stained with hematoxylin and eosin ( HE ) , succinate dehydrogenase ( SDH ) and viewed with a fluorescence microscope ( DM4000; Leica Microsystems , Sunnyvale , CA ) . Cross-sectional area ( CSA ) was analyzed on HE sections from TA mouse skeletal muscles , using the RoiManager plugin of ImageJ image analysis software ( Rasband , W . S . , ImageJ , U . S . National Institutes of Health , Bethesda , Maryland , USA , http://rsb . info . nih . gov/ij/ , 1997–2009 ) . The percentage of TA muscle fibres with centralized or internalized nuclei was counted using the cell counter plugin of ImageJ image analysis software . Transverse cryosections ( 8 µm ) sections of mouse TA skeletal muscles were prepared , fixed , and stained with antibodies to desmin ( Santa Cruz ) . Nuclei were detected by co-staining with Hoechst ( Sigma-Aldrich ) for 10 minutes . Sections were viewed using a fluorescence microscope ( DM4000; Leica Microsystems , Sunnyvale , CA ) . Muscle biopsies from TA muscles of anesthetized mice were fixed with 4% PFA and 2 . 5% glutaraldehyde in 0 . 1 M phosphate buffer ( pH 7 . 2 ) and processed as described [30] . Determination of the triads organization was accomplished on images at the magnification of ×25 , 000 . The triad structure was identified using morphological criteria on the longitudinal sections of the muscle and the number of triads per sarcomere was quantified . Ratio of triads/sarcomere was calculated by dividing number of triad structure identified by the total number of sarcomere present on the section . Frozen muscles were homogenized to prepare membranous ( microsomal ) fractions from skeletal muscles [32] . Membrane fractionation was confirmed using several protein markers: mouse anti-SERCA1 ( MA3–911; ABR ) and mouse anti-α-sarcoglycan [48] , cytoplasmic protein mouse anti-β-tubulin ( IGBMC antibody facility ) and nucleus protein mouse anti-TATA-box-binding protein ( IGBMC antibody facility ) . Cells or muscles were treated as described in [32] with the following modifications . Extracts were obtained by homogenization in extraction buffer ( 50 mM Tris-Cl pH 7 . 5 , 50 mM NaCl , 5 mM EDTA , 5 mM EGTA , 1 mM DTT , 0 , 5% Triton X-100 , 2 mM PMSF ) supplemented with complete protease inhibitor tablet ( Roche ) , 1 mM Leupeptin and 1 mM pepstatin A ( SIGMA ) . Equal weight of tibialis anterior muscles were homogenized with a Polytron homogenizer ( Kinematica Inc . ) in ice-cold extraction buffer supplemented with 0 . 05% ( w/v ) SDS . The muscle extracts were incubated ON at 4°C in the extraction buffer with 0 . 1% of N-Lauroylsarcosine Sodium Salt solution ( SIGMA ) . Muscle extracts were centrifuged during 30 min at 30 , 000 rpm at 4°C . Pellets were collected as the insoluble material and solubilized in extraction buffer supplemented with 8 M Urea . Extended experimental procedures are available in Text S1 . | X-linked centronuclear myopathy is a muscle disorder characterized by neonatal hypotonia and abnormal organelle positioning in skeletal muscle . This myopathy is due to different mutations in the MTM1 gene encoding the phosphoinositide phosphatase myotubularin . Disease-causing mutations are found all along the protein sequence and not only in the phosphatase catalytic domain . We investigated the link between myotubularin phosphatase activity and disease phenotypes . We used brewer yeast as a simple cellular model to analyze the in vivo phosphatase activity of different disease mutants . Our results show that mutations responsible for severe forms of myopathy are either active or inactive phosphatases . To further question this finding , we used the mice myotubularin knock-out model that reproduces faithfully the histopathological findings from human patients . Expression of phosphatase-dead mutants improved most phenotypes of knock-out mice comparable to wild-type myotubularin . This shows that the maintenance of normal skeletal muscles is largely independent from myotubularin phosphatase activity , while defects in the activity may participate in the onset of the disease . Moreover , it could have important implications in the design of therapeutic approaches aimed at manipulating the phosphoinositide levels in the different diseases linked to myotubularin homologues . Finally , these data call for cautiousness when manipulating such enzymes to conclude on the physiological relevance of their activity . | [
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] | 2012 | Phosphatase-Dead Myotubularin Ameliorates X-Linked Centronuclear Myopathy Phenotypes in Mice |
Although vaccines pose the best means of preventing influenza infection , strain selection and optimal implementation remain difficult due to antigenic drift and a lack of understanding global spread . Detecting viral movement by sequence analysis is complicated by skewed geographic and seasonal distributions in viral isolates . We propose a probabilistic method that accounts for sampling bias through spatiotemporal clustering and modeling regional and seasonal transmission as a binomial process . Analysis of H3N2 not only confirmed East-Southeast Asia as a source of new seasonal variants , but also increased the resolution of observed transmission to a country level . H1N1 data revealed similar viral spread from the tropics . Network analysis suggested China and Hong Kong as the origins of new seasonal H3N2 strains and the United States as a region where increased vaccination would maximally disrupt global spread of the virus . These techniques provide a promising methodology for the analysis of any seasonal virus , as well as for the continued surveillance of influenza .
Influenza , a negative-sense RNA orthomyxovirus , is one of the few diseases that is truly global in scale . It is responsible for approximately three to five million cases of severe acute respiratory illness and 250 , 000 to 500 , 000 deaths each year throughout the world [1] . In 2009 , the swift isolation of swine-origin H1N1 strain ( S-OIV ) from all continents within several weeks of onset reinforced the idea that influenza is a highly infectious agent circulating worldwide [2] , [3] . Although vaccination remains one of the most powerful ways of combating influenza , choosing a representative strain for vaccine composition poses a challenging problem . Due to the virus's high evolutionary rate , significant resources must be spent to update vaccines each year in order to match the dominant epitope of the season . Even with annual strain selection , major antigenic reassortment can obviate otherwise promising vaccine candidates , as occurred with the ‘Fujian/411/2002’-like H3N2 strain in 2003 [4] , [5] . To prevent such vaccine failures , a solid understanding of the global spread of influenza must inform the design process . If reservoirs for new viral strains can be identified , surveillance in these areas can better optimize prediction of seasonal variants in seeded regions . Previous papers investigating the global circulation of H3N2 , the major seasonal influenza subtype prior to pandemic H1N1 , focused on transmission within and between climate zones . Important motivating factors for such analysis include increased aerosol transmission in cold and dry conditions , as well as increased indoor crowding and decreased host immunity in cold and wet conditions [6] , [7] . In the temperate zones , influenza exhibits distinct seasonality with flu-related cases spiking in the winter . However , several papers have confirmed the presence of viral diversity even between these epidemic peaks [8] , [9] , [10] , suggesting two possible scenarios during the inter-epidemic period: either viral infections locally persist at a low level only to reemerge as the dominant strains of the epidemic season , or an outside source introduces new genetic diversity into temperate populations each year . Although a degree of local persistence may occur , phylogenetic analysis supports the latter scenario , with few direct links between strains of the same region but successive seasons [8] , [9] , [10] . For a given temperate zone , these conclusions suggest the tropics or the opposite temperate zone as plausible external seeding regions . At first blush , northern-southern temperate oscillations seem credible . Each year , northern and southern temperate climates have alternating seasonal influenza epidemics , lasting from November to April , and May to September respectively [11] . A possible mechanism of viral spread could involve transmission from the seasonal peak of one temperate zone into the season ebb of the other . On the other hand , specific epidemiological characteristics suggest a tropical origin for influenza . For example , although both climates share a similar yearly burden of mortality from influenza , the tropics do not possess the same consistent seasonal peaks during the winter months [9] , [12] , [13] . With a constant , low-level circulation of viruses year-round , the tropics represent an ideal epicenter for the extended transmission of new viruses to the rest of the world [14] , [15] , [16] . Several papers tracking H3N2 across continents have asserted that this tropical reservoir of influenza strains lies within East-Southeast Asia [12] , [14] , [17] . Russell , et al . analyzed H3N2 data to identify regions of the world that are antigenically and genetically leading or trailing . They found that newly emerging strains appeared in E-SE Asia roughly 6–9 months earlier than in other parts of the world , while South America experienced delayed transmission of roughly 6–9 months following other parts of the world [8] . However , such studies have been limited by several drawbacks . Most papers focus on H3N2 as a single entity , when in reality , it co-circulates with several other subtypes , the most important of which is seasonal H1N1 [11] . Although they possess different surface antigens , H3N2 and H1N1 share enough genetic similarity to display cross-immunity . As a result , seasonal H1N1 may demonstrate transmission patterns distinct from H3N2's [18] , [19] . Such codependence between different subtypes is exemplified by the pandemic years of 1957 and 1968 , when H2N2 replaced preexisting H1N1 and H3N2 replaced preexisting H2N2 , respectively [20] , [21] . Similarly , the antigenically different pandemic H1N1 strain of 2009 has largely overtaken previously circulating H1N1 and H3N2 [22] . During the years our dataset took place , evidence that H3N2 and H1N1 rarely co-dominate in a season further supports the idea of codependent dynamics [7] . A second shortcoming stems from biases in the number of sequences from different regions and different seasons [8] . Most isolates of H3N2 and H1N1 were sampled from North America , whereas Africa and South America have been largely neglected [23] . Many sequences were obtained within the last 15 years , making reliable tracking over long periods of time problematic . On the level of climate zones , the number of temperate isolates far outstrips the tropics . Although hemagglutinin ( HA ) , the HA1 domain , and neuraminidase ( NA ) have the most globally representative distributions of sequences , even these remain skewed ( Figure S1 , Figure S2 ) . In this paper , we present a novel probabilistic model for tracking the spread of influenza that employs two strategies to eliminate regional and seasonal data bias . The first involves clustering isolates of high sequence similarity by region and season . Since we would expect highly similar sequences from the same time and location to be related , we considered seeding events between clusters to be of greater significance . Consideration of clusters rather than individual sequences nullifies the over-representation of a high number of isolates from a single region and season ( Figure 1 ) . As a second strategy for eliminating bias , we determined statistical significance of inter-cluster seeding events by modeling transmission as a binomial distribution with prior probabilities based on the proportion of sequences isolated before a given time point . To illustrate our methodology , Figure 2 depicts the 2003–2004 flu season , which was marked by failure to predict the dominant , tropically-derived Fujian/411/2002-like H3N2 strain . We identified a strong seeding pattern from the tropics to all three climate zones , supporting the effectiveness of our methodology . We applied this model to the H3N2 and H1N1 coding regions of HA and NA , the most antigenic proteins of the eight viral segments . Clustering H3N2 sequences confirmed previous findings that this strain originates in the tropics , specifically E-SE Asia , and seeds South America by way of North America last . Clustering H1N1 NA also revealed a similar pattern of circulation beginning in the tropics . However , similar H1N1 analysis by continent and country was not possible due to the absence of a larger number of countries in the dataset . Applying the same methodology to the H3N2 HA1 domain increased the geographic diversity enough to enable reconstruction of the global influenza network prior to the 2009 pandemic strain at a country level . Our results suggest a possible flu seeding hierarchy beginning in China and spreading throughout a highly interconnected E-SE Asian subnetwork . From there , viruses transmit to an Oceanic subnetwork dominated by interchange between Australia and New Zealand . Both subnetworks seed into the USA , which in turn seeds many countries , particularly in South America . Expanding upon the sink-source hypothesis of global influenza dynamics proposed by Rambaut , et al . [15] , we applied techniques of graph theory to identify important source and sink regions in the global flu network . These techniques better describe the dynamic nature of influenza movement across the globe , as well as suggest different vaccination strategies to disrupt maximally viral flow around the world .
Spatiotemporally clustering the complete H3N2 and H1N1 coding sequences for HA and NA allowed the determination of multiple statistically significant seeding seasons between 1988 and 2009 . For our initial analysis , we clustered sequences into three climate zones—northern temperate , tropical , and southern temperate . To determine seasonal boundaries , we defined the northern temperate season to last from 1st July to the 30th June of the following year and the southern temperate season to last from 1st January to the 31st December of the same year [11] . Although the tropics do not have a well-defined seasonal pattern , we determined a consensus tropical flu season from 1st October to 30th September of the next year ( Text S1 , Table S1 ) . Results for H3N2 showed that the overwhelming majority of statistically significant seeding seasons came from the tropics , confirming previous findings ( Figure 3A , Figure S3A ) . Clustering H3N2 by the six major continents rendered an even more detailed picture . For HA , Asia was the primary seeder of Asia , North America , and Oceania . Prominent transmission from North America to Europe and South America was also observed ( Figure S3B ) . Interestingly , this hierarchical seeding structure reflects the findings of Russell , et al . , which identified Asia and South America as antigenically advanced and lagging continents respectively [8] . This network of hierarchical seeding can be visualized as a directed graph plotted against the world map ( Figure 4A ) . Analysis of NA produced similar findings with the exception of North America being its own primary seeder ( Figure 3B ) . No complete HA and NA isolates existed in the NCBI Influenza Virus Resource database [24] for Africa . The complete dataset of HA and NA represented only 17 and 21 countries respectively . Despite the sparse number of countries for analysis , both HA ( Figure S3C ) and NA ( Figure 3C ) consistently identified Hong Kong ( considered a country by NCBI sequence annotation ) as the primary external seeder of USA and New Zealand among others , and New Zealand as the primary external seeder of Australia . Due to fewer available sequences , clustering H1N1 did not yield as many significant seeding events as H3N2; however , our tests suggest that H1N1 adopts a similar seeding pattern with the tropics as a source . Of the two segments , NA sequences display a broader geographical profile than HA . In particular , our HA dataset for H1N1 contained no sequences from Hong Kong and only 1 ( 0 . 091% ) China sequence , while NA contained 9 ( 0 . 69% ) Hong Kong and 3 ( 0 . 23% ) China sequences . Consequently , we considered NA to be more suitable for comparison between H3N2 and H1N1 and HA to be a background signal to assess the effect of Hong Kong and China on global influenza transmission . Even so , the number of these H1N1 Hong Kong and China sequences remained vastly disproportionate to the 361 ( 7 . 42% ) Hong Kong and 133 ( 2 . 73% ) China sequences of H3N2 . Clustering H1N1 NA by climate zone supported the theory of global viral spread from the tropics ( Figure 5B ) . Unlike H3N2 , H1N1 analysis by continent and country was inconclusive due to low ( typically fewer than 3 seeding events ) , homogeneous counts . Although inconclusive , the fact that a tropical signal could be detected at all from such few tropical countries , including Hong Kong and China , suggests that H1N1 adopts a similar seeding pattern out of the tropics . Due to insufficient sampling , however , a more detailed transmission pattern could not be discerned . Although using the complete HA and NA coding genomes facilitated differentiation of isolates by Hamming distance , the absence of data from certain countries limited the information gained from clustering at this geographic detail , a problem that has plagued previous studies [8] . To increase the amount of data from different geographical regions , we clustered H3N2 sequences of the HA1 epitope , expanding the number of isolates in the dataset from 2 , 251 to 4 , 864 , and the number of countries from 17 to 81 . A necessary consequence of expanding geographic coverage was an increase in the number of non-unique solutions ( Text S1 ) . Importantly , clustering HA1 by climate and continent was corroborated by findings from the complete HA and NA sequences , lending credence to the validity of the dataset . Due to the inclusion of isolates from Africa , which was hitherto not present in our datasets , H3N2 HA1 analysis also revealed Europe and North America tied for being the primary seeders of Africa . Country clustering of the HA1 data produced a highly detailed global network of influenza variants . USA , Hong Kong , Australia , and China were identified as the four most prominent seeding countries in that order ( Figure 3D , Table S2 ) . From the data , an inferred seeding hierarchy would begin with China at the epicenter of an E-SE Asian influenza subnetwork . Our analysis supports China as the most predictive seeder of many Asian countries , including Hong Kong . Both China and Hong Kong then serve as a launching pad for the dispersal of new seasonal variants to the rest of the world [14] , [17] , in particular USA and an Oceanic subnetwork dominated by interchange between Australia and New Zealand . Viruses from USA , the largest seeder of the entire world , then spread to a number of South American , European , and African countries . Interestingly , Australia and Hong Kong are equally probable seeders of the USA ( Figure 3D ) . Detailed transmission events are enumerated in Table S2 . An inset of the Asian subnetwork is depicted in Figure 4C , a demonstration of this study's high geographic resolution . As can be seen with the world map plots ( Figure 4A , B ) , a natural representation of the global influenza network is a directed graph with each node representing a clustered region ( climate , continent , and country ) and each edge representing a seeding event with a weight equal to the number of significant seeding seasons . To quantify observed patterns , we employed principles of graph theory to measure the importance of nodes using four different metrics . By counting the number of indegrees and outdegrees of each node for H3N2 , we identified that the tropics and the northern temperate zone ( Figure S4A ) , specifically Asia and North America ( Figure Figure S5A ) , transmit and receive the most seeding events to and from the rest of the world , respectively . In a similar manner , we identified USA , Hong Kong , Australia , and China as the greatest seeders , and USA , Japan , Australia , and Hong Kong as the most seeded ( Figure 6A ) . In this analysis , we differentiated between internal ( self-seeding ) and external ( seeding between nodes ) transmission events . Importantly , we can accurately detect internal events in temperate countries since their flu seasons are discrete . On the other hand , the specificity for internal events in the tropics is much lower due to unpronounced seasonal peaks . To minimize the number of local false positives , we demarcated seasons within the tropics on a per country basis . We found that for all climate zones except the tropics ( Figure S4A ) and all continents except Asia ( Figure S5A ) , the number of internal seeding events paled in comparison to the proportion of external seeding events , . The more numerous internal events in the tropics and Asia indicate a high level of circulation between tropical countries and between Asian countries . This pattern is supported by the highly interconnected E-SE Asian subnetwork depicted in Figure 4C . The small proportion of internal events for countries supports the notion that local persistence often plays only a minor role in influenza transmission [8] , [9] , [10] ( Figure 6A ) . Beyond the absolute number of seeding events , a region's influence on global viral spread is also dependent on the topological structure of the graph itself . As an analogy , consider the influenza network as a system of connected train stations each representing a single region seeding influenza . In such systems , trains begin and end their routes at terminal stations . Similarly , influenza commuters begin their journeys at terminal sources and end at terminal sinks in each season . These start and end terminals can represent regions where new influenza variants respectively originate and ultimately spread to . To quantify the terminal characteristic , we calculated the outdegree minus the indegree of each node , which we term “degree flow . ” Positive degree flow indicates terminal sources , while negative indicates terminal sinks . Countries were also ranked by calculating the proportion of nodes in a 1 , 000 randomized networks with a greater , or lesser , degree flow ( Text S1 ) . For analysis by climate zone , the tropics was identified as the only terminal source , suggesting that flu spreads from the tropical belt outward to both temperate zones ( Figure S4B ) . As for continental clustering , Asia was the only terminal source , indicating that global circulation begins in Asia and ends in terminal sink continents , of which North America was the most prominent ( Figure S5B ) . On a country level , Hong Kong and China were the greatest terminal sources , corroborating our observations ( Figure 3D ) . Australia was also a conspicuous terminal source , especially within the Oceanic subnetwork where it seeded the greatest terminal sink , New Zealand . Several South American countries , including Chile and Argentina , figure as terminal sinks too , correlating with such countries as antigenically delayed [8] ( Figure 6B ) . Trains also stop at waypoint stations , which can be the junction of a large number of routes . Correspondingly , certain regions act as waypoint sources: important intermediate launch pads to other destinations . Others act as waypoint sinks: important points of convergence for multiple routes . Eigenvector centrality can gauge this property on the principle that connections to high-scoring nodes contribute more to the score of the node in question than equivalent connections to low-scoring nodes . We used a method akin to PageRank , Google's method of assigning importance to web pages [25] . Using this method , the northern temperate zone was the most important waypoint source and sink ( Figure S4C ) . Similarly , the predominantly northern temperate continents of North America and Europe were identified as prominent waypoint sources and sinks . Asia , however , was the greatest waypoint source but a poor waypoint sink , correlating with its role as a greater terminal source than North America or Europe ( Figure S5C ) . Interestingly , USA was both the greatest waypoint source and sink ( Figure 6C ) . H1N1 NA clustering by climate zone produced results similar to that of H3N2 NA . The tropics consistently scored highest by seeding outdegree , positive degree flow , and PageRank source . In addition , the tropics possessed a large amount of internal seeding events . These results emphasize that similar to H3N2 , H1N1 circulates within the tropics across seasons only to spread eventually to the temperate zones . Betweenness measures the number of shortest paths between any two vertices in a network that lie on a given node . In the context of influenza , increasing vaccinations in regions of high betweenness would hypothetically have the greatest effect on diminishing the spread of infection worldwide . This novel strategy contrasts with previous studies simulating containment only at the source of influenza [26] , [27] . For H3N2 , this criteria highlighted Europe and North America as promising candidates for vaccination programs ( Figure S5D ) . Clustering by country revealed USA , Japan , and Australia as sites in the influenza network vulnerable to disruption ( Figure 6D ) .
Using statistical and network theory analysis , we analyzed H3N2 and H1N1 sequence data to determine the global spread of influenza . Our novel method employs two main strategies to eliminate geographic and seasonal bias: 1 ) Spatiotemporal clustering of sequence data to count seeding events between clusters and 2 ) Use of binomial prior probabilities based on the regional proportion of viral isolates to screen for significant seeding events . Applying these techniques to coding HA and NA segments of H3N2 by climate zone and continent revealed a seeding pattern stemming from the tropics , particularly Asia . HA1 analysis produced a more detailed picture: each year , a wave of seasonal flu originates in China to feed an E-SE Asian subnetwork . From there , China and Hong Kong seed two major subnetworks , each dominated by Australia and USA . Similar clustering of H1N1 NA sequences by climate zone reproduced tropical transmission to the rest of the world . However , due to inadequate geographic coverage , clustering H1N1 by continent and country proved inconclusive with few significant seeding events detected . One explanation for these results is that important seeding countries , such as China and Hong Kong , were too underrepresented in the dataset . Alternatively , global patterns may be weaker for H1N1 due to cross-reactivity between the two strains [18] , [19] , a conclusion reflected by the smaller number of seeding events for the strain . In our analysis , the total number of seeding seasons for each region did not necessarily correspond to the total number of isolates from each region , indicating that our methodology counters data bias . However , certain confounders may affect results . First , selection bias in sampling remarkable variants , such as patients suffering severe rather than mild or non-symptomatic influenza , would poorly represent flu in the general population . Moreover , many sequences had to be excluded from our dataset due to poor annotation and lack of date information . Finally , although our probabilistic methodology accepts regional and temporal variability , it has low sensitivity for detecting anything but particularly significant seeding events for regions with very few sequences . This issue becomes important in analyses with regions that have no sequences whatsoever , as with near-absent sequences from Hong Kong and China for H1N1 HA . The persistence of such bias highlights the continuing need to sequence viruses in underrepresented areas , especially the tropics . Each year , the current influenza vaccine is formulated separately for the Northern and Southern Hemisphere; one can surmise that two viral strains may not be enough to represent the entire pool of influenza strains around the world . Although there are many other economic and political concerns to consider , our methodology suggests several ways of guiding vaccine strain selection based on biological and epidemiological principles . Graph theory metrics—terminal and waypoint sinks and sources , as well as degree and betweenness centralities—pinpoint potential regions in which increased vaccinations could stem the transmission of influenza globally as well as locally . Increased analytical resolution could optimize vaccine design by choosing the dominant antigenic strain of a country's most predictive seeder . Vaccines could be catered to each country , rather than each hemisphere . At the very least , our analysis advises strain selection from the tropics , from which seasonal strains are dispersed each year . On the other hand , local strain selection within a country should prove comparatively ineffective , as few viruses persist in the inter-epidemic period to seed the following flu season . Our analysis of terminal sources resonates with an old hypothesis that in southern China , zoonotic infection from live-animals markets [28] selling in particular duck—a natural host of influenza [29]—combined with a dense population for sustained viral circulation , could be the main ingredients for the creation of new seasonal influenza variants . In support , two major acute respiratory infections—SARS [30] and H5N1/97 [31] , [32]—have been definitively traced back to southern China , with Hong Kong serving as an important sentinel post for the rest of the world . Other influenza pandemics , 1968 H3N2 ( Hong Kong ) [28] and even as early as 1889 pandemic influenza [33] , have suspected origins in southern China . It would be interesting to dissect the factors that govern waypoint sources and sinks . For example , air travel and other transportation may play a major role in the dispersal of virus worldwide [8] , [19] , [34] , [35] . Many important hubs of the global flu network , including USA , Australia , Hong Kong , and China , have several of the world's busiest airports [36] . Understanding the reasons for these seeding patterns may offer other strategies for arresting the movement of flu . The advent of 2009 pandemic S-OIV has largely depleted the number of seasonal H3N2 and H1N1 infections , most likely via cross-reactivity between novel and seasonal strains [22] . Consequently , the conclusions of this paper may not necessarily apply to current dynamics of seasonal H3N2 and H1N1 . However , the fact that H1N1 shares a tropic-centric movement pattern with H3N2 despite cross-reactivity suggests that these patterns may still persist even in the presence of the cross-reactive S-OIV . Moreover , this paper demonstrates that when more sequence data is deposited in NCBI , a similar methodology can be applied to predict global circulation of S-OIV as well .
All sequence data used in this study was publicly available from the National Center for Biotechnology Information database ( NCBI ) [37] . For each segment , only protein coding regions were considered . Furthermore , we only used sequences with full date ( year , month and day ) and location information to build hierarchies . Geographical coordinates of each isolate were obtained using geolocation information from Google Maps . Sequences were then aligned using the ClustalW v . 1 . 83 multiple sequence alignment package using default parameters for H3N2 and H1N1 , respectively . For each segment , sequences were aligned and those that were poorly aligned compared to the rest of the dataset were removed until all sequences aligned with a Hamming distance no greater than 0 . 15 . Given estimated mutation rates of 6 . 7×10−3 nucleotide substitutions per site per year [12] , [19] , Hamming distances over the 20-year span of our dataset are expected to be no more than 0 . 15 of the sequence length . Outlying sequences were most likely incorrectly sequenced and were discarded from analysis . Our methodology aimed to minimize data bias from geospatial and temporal variability in sequences from NCBI . First , we determined the most parsimonious evolutionary paths traversed by the flu virus . To this end , we sorted sequences from earliest to most recent viral isolates . Working backwards from newest to oldest , we calculated the sequence similarity of each virus to all earlier isolates regardless of geography . We defined a virus's most likely ancestor to be the sequence with minimum Hamming distance . From this data we built evolutionary paths for each virus . Related sequences were clustered ( grouped ) together by common geography and season to simplify the paths . For example , a chain of related viruses in the same region and season would be collapsed into a single umbrella node representing all of them . Our analysis was then based on looking at the transitions between clusters rather than individual viruses . We counted these “seeding events , ” where the closest ancestor of a given cluster of sequences is from a different region or season [8] ( Figure 1 ) . When tallying seeding events , non-unique solutions were not considered where a given viral isolate possessed multiple closest ancestors from different geographical zones or seasons ( Text S1 , Figure S6 ) . The observed frequencies of seeding events between clusters were compared to expected frequencies based on the prior probability of randomly choosing a sequence from a given geographical zone in the past . Using the binomial distribution with the proportion of prior NCBI sequences as a binomial probability , a p-value was calculated for observing more seeding events than expected . The best predictor of a seeding region for each season had the greatest ratio of observed to expected seeding events with a p-value smaller than 0 . 05 ( Figure 2 ) . | As evidenced by several historic vaccine failures , the design and implementation of the influenza vaccine remains an imperfect science . The virus's rapid rate of evolution makes the selection of representative strains for vaccine composition a difficult process . From a global health viewpoint , how to optimally implement a limited stockpile of vaccines is another fundamental question that remains unanswered . An understanding of how influenza spreads around the world would greatly aid the design and implementation process , but regional and seasonal bias in collected virus samples hampers epidemiologic analysis . Here , we show that it is possible to counter this data bias through probabilistic modeling and represent the global viral spread as a network of seeding events between different regions of the world . On a local scale , our technique can output the most likely origins of a virus circulating in a given location . On a global scale , we can pinpoint regions of the world that would maximally disrupt viral transmission with an increase in vaccine implementation . We demonstrate our method on seasonal H3N2 and H1N1 and foresee similar application to other seasonal viruses , including swine-origin H1N1 , once more seasonal data is collected . | [
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] | 2010 | Network Analysis of Global Influenza Spread |
Genetically-controlled plant resistance can reduce the damage caused by pathogens . However , pathogens have the ability to evolve and overcome such resistance . This often occurs quickly after resistance is deployed , resulting in significant crop losses and a continuing need to develop new resistant cultivars . To tackle this issue , several strategies have been proposed to constrain the evolution of pathogen populations and thus increase genetic resistance durability . These strategies mainly rely on varying different combinations of resistance sources across time ( crop rotations ) and space . The spatial scale of deployment can vary from multiple resistance sources occurring in a single cultivar ( pyramiding ) , in different cultivars within the same field ( cultivar mixtures ) or in different fields ( mosaics ) . However , experimental comparison of the efficiency ( i . e . ability to reduce disease impact ) and durability ( i . e . ability to limit pathogen evolution and delay resistance breakdown ) of landscape-scale deployment strategies presents major logistical challenges . Therefore , we developed a spatially explicit stochastic model able to assess the epidemiological and evolutionary outcomes of the four major deployment options described above , including both qualitative resistance ( i . e . major genes ) and quantitative resistance traits against several components of pathogen aggressiveness: infection rate , latent period duration , propagule production rate , and infectious period duration . This model , implemented in the R package landsepi , provides a new and useful tool to assess the performance of a wide range of deployment options , and helps investigate the effect of landscape , epidemiological and evolutionary parameters . This article describes the model and its parameterisation for rust diseases of cereal crops , caused by fungi of the genus Puccinia . To illustrate the model , we use it to assess the epidemiological and evolutionary potential of the combination of a major gene and different traits of quantitative resistance . The comparison of the four major deployment strategies described above will be the objective of future studies .
The deployment of resistant cultivars in agricultural landscapes aims to reduce the ability of plant pathogens to cause disease on crops . However , the durability of plant resistance has often been limited by evolutionary changes in pathogen populations [1] . Typically , there are two main types of resistance . Although exceptions exist , qualitative ( or ‘major gene’ ) resistance is usually monogenic and complete , i . e . conferring full immunity [2–4] . In contrast , quantitative resistance is mostly polygenic and partial , i . e . infection is still possible but pathogen development is reduced to a greater or lesser extent . Consequently , quantitative resistance is often described as affecting one or more components of pathogen aggressiveness ( defined as the quantitative ability to colonise and cause damage to the host ) : lower rate of infection , longer latent period , reduced propagule production , shorter infectious period or lower toxin production [5–8] . Regardless of the source of resistance , pathogens have the potential to evolve infectivity ( defined as the qualitative ability to infect the host ) and aggressiveness in response to the selection posed by plant resistance [4 , 9] . This adaptation of pathogens at the population level is generally driven by mutations from non-adapted pathogen genotypes , changes in frequencies of pre-existing adapted genotypes , or introductions via migration from distant areas [9–11] . Emergence of novel pathotypes can result in the breakdown of qualitative resistance or the erosion of quantitative resistance , and consequently restoration of the infectivity or aggressiveness of pathogen population . The economic costs induced by pathogen adaptation may be huge , due firstly to the yield losses directly associated with an epidemic , and secondly because of the significant research and breeding efforts required to identify new resistance sources and develop new resistant cultivars [12] . Several deployment strategies have been proposed to improve the cost-effectiveness of plant resistance and prevent the frequently documented breakdown of major genes after their uniform deployment over large areas [3 , 4 , 9 , 13 , 14] . These strategies rely on the use of quantitative resistance , which is thought to be more durable because it poses smaller selection pressure on pathogen populations [1] , or on the management of host genetic diversity [15–17] . This diversity can be introduced in time through crop rotations ( e . g . recurring succession of different crops in the same field [18] ) . In space , different crops can be combined in the same field in cultivar mixtures [19 , 20] or in different fields of the landscape as mosaics [3 , 17] . Finally , several resistance sources can be stacked in the same cultivar through pyramiding [21 , 22] . Few empirical studies have directly compared the performance of these different strategies , probably owing to the difficulty of implementing landscape scale experiments in practice ( but see [23] for a comparison of all these strategies , except mosaics , using plastic tunnels ) . Models are not constrained by this difficulty , but surprisingly there are currently no published models enabling a global comparison . Indeed , most models focus on a single strategy ( e . g . crop rotation [24 , 25]; mixtures [26–28]; pyramiding [29 , 30]; mosaics [31–42] ) or a combination of strategies ( e . g . mixture and pyramiding: [43]; mosaic and pyramiding: [44 , 45] ) . Only a few studies explicitly compare two types of strategies [46–50] and only two studies evaluated more than two strategies [51 , 52] . As a result , a comprehensive evaluation of different deployment schemes is complicated , and currently only feasible via pairwise comparisons [53 , 54] . The situation for quantitative resistance is similar , since often only one [28 , 34 , 41 , 42 , 55] , two [36 , 37 , 56] , or a combination [26 , 44 , 49] of pathogen aggressiveness components are targeted , although quantitative resistance can affect several life-history traits of the pathogen . As articulated above , this current gap in our ability to predict which strategy will maximise our ability to control disease epidemics as well as pathogen evolutionary potential ( or indeed whether these goals are compatible ) emphasises the need for models that can compare different deployment schemes within the same framework , using standardised assumptions . Comparison of different resistance deployment strategies requires the use of relevant evaluation criteria , which may vary depending on stakeholder objectives , and thus have an impact on the optimal strategy [39 , 45] . Most published models focused only on one criterion , like resistance durability ( i . e . the duration from initial deployment to the time when resistance is considered to have been overcome ) ( e . g . [30 , 37 , 47 , 51 , 52] ) , or epidemiological protection ( i . e . reduction in pathogen population size and consequently in the proportion of diseased plants ) ( e . g . [26 , 28 , 32 , 34–36 , 43 , 49 , 50] ) . However , no correlation seems to exist between durability and epidemiological protection [45 , 57] , and those objectives can even be incompatible in severe epidemic contexts [33] . It is therefore essential to develop methods to assess deployment strategies against multiple evolutionary and epidemiological criteria [3 , 17 , 58] . The present study describes a demo-genetic model ( i . e . it includes pathogen population demographic dynamics and its genetic evolution ) which simulates the spread of a pathogen in a spatially explicit agricultural landscape . The model is flexible and can simulate mosaics , mixtures , rotation , and pyramiding of different major genes and up to four traits for quantitative resistance , acting on different components of pathogen aggressiveness ( infection rate , latent period , infectious period , reproduction rate ) . Performance of resistance deployment is evaluated using several criteria from evolutionary ( e . g . durability ) or epidemiological ( e . g . disease severity ) perspectives . Thus , the model enables direct comparison of a range of spatio-temporal deployment strategies , but also enables investigation of the effects of landscape , epidemiological and evolutionary parameters on the ability of a given strategy to control disease . Although the main purpose of this paper is to provide a comprehensive description of the simulation model , we take advantage of the generality of the model and address three specific questions of interest to the scientific community . These questions aim to assess the potential of the combination of qualitative and quantitative resistance , given that the former prevents disease spread but is prone to breakdown , whereas the latter allows some disease development but to a lesser extent: The model can be parameterised to simulate various pathogen life histories . Here , we investigate the above questions in the context of rust diseases of cereal crops , caused by fungi of the genus Puccinia which can dramatically affect crop yields worldwide [59 , 60] . Over the past decades , breeders have been engaged in an arms race against these pathogens , which exhibit high evolutionary potential in terms of their ability to overcome resistant crop cultivars following deployment [45 , 60–63] .
The model is stochastic , spatially explicit ( the basic spatial unit is an individual field ) , based on a SEIR ( ‘susceptible-exposed-infectious-removed’ ) structure with a discrete time step . It simulates the spread ( through clonal reproduction and dispersal ) and evolution ( via mutation ) of a pathogen in an agricultural landscape , across cropping seasons split by host harvests which represent potential bottlenecks to the pathogen . The model is based on the model described in previous articles [40 , 45] , but has been considerably modified and extended to enable simulation of a wide array of deployment strategies: mosaics , mixtures , rotations and pyramiding of multiple major resistance genes which affect pathogen infectivity , and up to four quantitative resistance traits . These traits target different aggressiveness components of the pathogen , i . e . the infection rate , the duration of the latent period and the infectious period , and the propagule production rate . Initially , the pathogen is not adapted to any source of resistance and is only present on susceptible hosts . However , through mutation , it can evolve and may acquire infectivity genes ( which leads to breakdown of major resistance genes ) or increase aggressiveness ( which leads to the erosion of the relevant quantitative resistance traits ) . Evolution of a pathogen toward infectivity or increased aggressiveness on a resistant host is often penalised by a fitness cost on susceptible hosts [2 , 64–66] . Consequently , in the present model , pathogens carrying infectivity genes may have lower infection rates ( cost of infectivity ) on susceptible hosts relative to pathogens that do not carry these genes . Similarly , a gain in pathogen aggressiveness on quantitatively resistant hosts is penalised by decreased aggressiveness on susceptible hosts , leading to a trade-off . The evolutionary outcome of a deployment strategy is assessed by measuring the time until the pathogen achieves the three steps to adapt to plant resistance: ( d1 ) first appearance of adapted mutants , ( d2 ) initial migration to resistant hosts and infection ( also referred as ‘arrival’ or ‘introduction’ in invasion biology ) , and ( d3 ) broader establishment in the resistant host population ( i . e . the point at which extinction becomes unlikely ) . Epidemiological outcomes are evaluated using the Green Leaf Area ( GLA ) as a proxy for yield , and the area under the disease progress curve ( AUDPC ) to measure disease severity . In this study , a cropping landscape is represented by both its physical structure ( defined as the spatial arrangement of field boundaries ) and its genetic composition ( defined by the allocation of crop cultivars within and among individual fields ) . All model parameters and values used in the simulations are listed in Table 1 ( see S1 Text for details on model parameterisation to rust pathogens ) . The demo-genetic dynamics of the host-pathogen interaction are based on a SEIR structure . However , to avoid any confusion with the ‘susceptible’ cultivar , we have labelled this structure HLIR for ‘healthy-latent-infectious-removed’ . Thus , in the following , Hi , v , t , Li , v , p , t , Ii , v , p , t , Ri , v , p , t , and Pri , p , t respectively denote the number of healthy , latent , infectious and removed individuals ( in this model , an ‘individual’ is a given amount of plant tissue , and is referred to as a ‘host’ hereafter for simplicity ) , and pathogen propagules , in field i ( i = 1 , … , J ) , for cultivar v ( v = 1 , … , V ) , pathogen genotype p ( p = 1 , … , P ) at time step t ( t = 1 , … , TxY ) . T is the number of time steps in a cropping season and Y the number of simulated years ( i . e . cropping seasons ) . Since the host is cultivated , we assume there is no host reproduction , dispersal or natural mortality ( leaf senescence near the end of the cropping season is considered as part of host harvest ) . Fig 3 gives a schematic representation of the model structure . The epidemiological impact of pathogen spread is evaluated by two different measures . Firstly , we use a measure termed Green Leaf Area ( GLA ) , based on the Healthy Area Duration initially developed by Waggoner et al . [73] . The GLA represents the average number of productive hosts per time step and per surface unit , and is considered as a proxy for crop yield [74] . Secondly , we use the area under the disease progress curve ( AUDPC ) , which is the average proportion of diseased hosts relative to the carrying capacity and represents disease severity [74] . We assume only heathy hosts ( state H ) contribute to crop production ( Fig 3 ) . Thus , for cultivar v during year y: GLAv , y=∑t=t0 ( y ) tf ( y ) ∑i=1JHi , v , tT×∑i=1JAi ( 21 ) AUDPCv , y=∑t=t0 ( y ) tf ( y ) ∑i=1J∑p=1P{Ii , v , p , t+Ri , v , p , t}∑t=t0 ( y ) tf ( y ) ∑i=1JKi , v ( 22 ) In this section , ten scenarios are simulated using the model to assess the evolutionary and epidemiological outcomes of deploying different pyramided combinations of qualitative and quantitative resistances . In this context , the model was parameterised to approximate biotrophic foliar fungal diseases as typified by rusts of cereal crops , caused by fungi of the genus Puccinia .
To assess the durability of a single major gene , the time period from the beginning of the simulation to appearance of mutants able to overcome the major gene ( d1 ) , first infection of the resistant cultivar ( d2 ) , and broader establishment on the resistant cultivar ( d3 ) were used . Under the simulated conditions , mutants always appeared , dispersed across the landscape and established on a resistant cultivar carrying one major gene in less than one year ( Fig 5A , ‘MG1’ ) . When the resistant cultivar carried a second major gene ( i . e . as a pyramid with two major genes ) , the first infection of a resistant host was delayed to 8 . 3 years ( 90% central range , CR90: 0 . 4–23 . 4 ) on average , and the pathogen population was not established before , on average , 20 . 7 years ( CR90: 0 . 9–50 . 0 ) ( Fig 5A , ‘MG2’ ) . In contrast , when a major gene was combined with a quantitative resistance with a 50% efficiency , the breakdown of the major gene was nearly as quick as if the major gene was alone , regardless of the pathogen life-history trait targeted by the quantitative resistance ( Fig 5A , column 3 to 6 , and inset ) . This conclusion is consistent with the results of an experimental study on pepper resistance against root-knot nematode [121] , but differs from those of other studies carried out on different pathosystems , showing that a quantitative resistant background can significantly increase the durability of a cultivar carrying a major gene for resistance [122–124] . In order to test if this difference could be due to our assumption that quantitative resistance has a 50% efficiency , we replicated the numerical experiment with higher efficiencies ( ρw = 60% , 70% , 80% , 90% ) . The results of these new simulations indicated that when quantitative resistance efficiency is higher than 80% , durability of the major gene is improved in combination with quantitative resistance , especially if latent period is targeted ( time to establishment delayed to 5 . 2 years , CR90: 1 . 7–10 . 8 , see S3A Fig ) . Furthermore , above 90% efficiency , quantitative resistance can increase the durability of the major gene compared to pyramiding of two major genes ( average time to establishment between 11 . 0 and 30 . 9 years depending on the targeted trait , Fig 5B ) . When deployed alone , quantitative resistance was on average eroded by 26 . 4% ( CR90: 20–40 ) , 20 . 8% ( CR90: 0–40 ) , 22 . 0% ( CR90: 0–40 ) , and 19 . 2% ( CR90: 0–40 ) by the end of the simulation , for resistances targeting respectively infection rate , duration of the latent period , sporulation rate and duration of the sporulation period ( Fig 6 ) . The associated average speeds of erosion were 2 . 16 , 0 . 78 , 1 . 44 and 1 . 02% per year , respectively . The targeted pathogen life-history trait appeared to have a significant effect on the final level ( Kruskal Wallis χ2 tests with 3 degrees of freedom , p = 8 . 10−9 ) and speed ( p = 6 . 10−5 ) of resistance erosion . The combination with a major gene significantly affected the final level ( Kruskal Wallis χ2 test with 1 df , p = 8 . 10−3 ) , but not the speed ( p = 0 . 85 ) of erosion . It is important to remember that the speed of erosion was computed from the time point when quantitative resistance started to erode ( hence , following breakdown of the major gene ) and not from the beginning of simulations . Assessing the average speed of erosion from the beginning of the simulations would greatly impact the results if the major gene was durable for several years . As previously described , this is not the case with a 50% efficiency of quantitative resistance . However , with a 90% efficiency , the average speed of erosion of quantitative resistance from the beginning of the simulation was 2 . 5 to 3 . 0% per year when deployed alone , and only 0 . 7 to 1 . 7% per year when combined with a major gene . In this context , the combination of quantitative resistance with a major gene significantly delayed the start of quantitative resistance erosion ( Kruskal Wallis χ2 test with 1 df , p<10−15 ) . The AUDPC of the susceptible cultivar ( AUDPCSC ) , the resistant cultivar ( AUDPCRC ) and across the entire cropping landscape ( AUDPCTOT ) averaged across the whole simulation period were used as indicators of the severity of epidemics in the simulated scenarios ( see S4 Fig for examples ) . As expected , regardless of the cultivar or the deployment scenario , disease severity in a landscape where a resistant cultivar is deployed was lower than in a fully susceptible landscape ( dashed line in Fig 7 ) . When only a single resistance source was incorporated into the resistant cultivar , the most effective source was quantitative resistance against duration of the latent period ( AUDPCRC = 0 . 25 ) , followed by quantitative resistance against infection rate , sporulation rate and duration of the sporulation period ( AUDPCRC = 0 . 32 , 0 . 33 and 0 . 34 , respectively ) , and finally major gene resistance ( AUDPCRC = 0 . 37 ) . When a major gene was combined with one of these resistance sources , the combination of two major genes became the most efficient strategy ( AUDPCRC = 0 . 22 ) but also the most variable ( CR90: 0 . 00–0 . 37 ) . Below this , were combinations including quantitative resistance traits in the same order as above and with similar efficiencies ( AUDPCRC = 0 . 24 , 0 . 31 , 0 . 32 , and 0 . 33 against latent period , infection rate , sporulation rate and sporulation duration , respectively ) . For the susceptible cultivar , disease severity was less variable between scenarios than for the resistant cultivar . The average AUDPCSC was 0 . 33 , 0 . 37 , 0 . 37 , 0 . 36 and 0 . 36 , respectively when a major gene or a quantitative resistance trait against infection rate , latent period , sporulation rate or duration of the sporulation period was deployed alone . However , when a major gene was combined with these sources , AUDPCSC was smaller ( 0 . 28 , 0 . 30 , 0 . 29 , 0 . 29 and 0 . 30 , respectively ) . Globally , across the whole landscape , disease severity ( AUDPCTOT ) , was very similar to levels of disease severity seen in the resistant cultivar ( AUDPCRC ) , since this cultivar constituted a high proportion of the area being cropped ( 80% , see Fig 1D for an example ) . The global severity of disease could also be assessed using the GLA ( focusing on healthy hosts ) instead of the AUDPC ( focusing on diseased hosts ) . In our context , GLATOT was highly negatively correlated with AUDPCTOT ( Pearson correlation coefficient of -0 . 92 , p<10−15 ) .
Three main properties distinguish models simulating the deployment of resistance and the interpretation of their results: whether they are demographic or demo-genetic , whether they are deterministic or stochastic , and whether they are spatial or not . In contrast with purely demographic models , our model includes the genetic evolution of the pathogen in addition to pathogen population dynamics and host growth during a cropping season . This enables the explicit simulation of the appearance of new genotypes through mutation [29 , 30 , 46 , 48] , which represents the first step towards resistance breakdown or erosion ( i . e . prior to migration to and broader establishment onto resistant hosts ) . The time required for the achievement of this first step can be the main determinant of the durability of some deployment strategies , like pyramiding [51] . This step is thus essential to comprehensively assess the relative performance of different deployment strategies . We further note that the present model is stochastic , i . e . it relies on probabilistic computations of simulated biological processes . It is consequently well able to account for biologically realistic random events [127] . As illustrated by Lo Iacono et al . [35] , who used a stochastic version of the model developed by van den Bosch et al . [39] , the likelihood of extinction events in pathogen populations can considerably impact the performance of a deployment strategy . Finally , because our model is able to simulate explicit landscapes , it accounts for spatial heterogeneity , which affects landscape connectivity and consequently the ability of the pathogen to disperse . Pathogen dispersal , in addition to being one of the unavoidable steps of adaptation to plant resistance , strongly shapes pathogen evolutionary dynamics [12 , 41 , 128] . Moreover , the spatial nature of this model enables a wide range of deployment options to be considered at different spatial scales , particularly given the possibility to vary deployment parameters like the proportion of the landscape across which resistant cultivars are planted [32 , 51] , as well as the level of spatial aggregation [28 , 45 , 48 , 49 , 129 , 130] . Resistance durability ( i . e . the period of time from initial deployment of a resistant cultivar to when resistance is considered to have been overcome ) is a typical evolutionary target of resistance deployment strategies , but its computation in a model is not obvious . Proposed methods include targeting the point in time when the first adapted pathogens appear [29 , 30 , 39] , when their prevalence [37 , 48 , 55 , 131] or frequency in pathogen population [39 , 51 , 52] exceeds a threshold , or when productivity of the resistant cultivar drops below an arbitrary threshold [45] . Since different measures give different information , the present model includes several measures of durability: the time until first appearance of mutants , the time until first infection of a resistant host by such mutant , and the time until the prevalence of these mutants exceeds a threshold . The first measure assesses the ability of deployment strategies to reduce the probability of appearance of mutants by reducing pathogen population size . The difference between the first and the second measure provides information on the potential to hinder pathogen migration to resistant hosts , and the difference between the second and the last measure is related to the rate of establishment on resistant cultivars as a result of the balance between selection and genetic drift ( occurring between seasons in this model ) . With respect to the establishment process , it is interesting to note that different stages of invasion can be targeted by changing the value of the threshold . Importantly , we considered that time to establishment was best computed using a prevalence threshold ( i . e . total number of resistant hosts infected by adapted pathogens ) as opposed to using a frequency-dependent threshold in the pathogen population ( i . e . the proportion of adapted pathogens in the global pathogen population ) . This is because the frequency of adapted pathogens may never ( or always ) exceed a threshold simply as a result of a very low ( or very high ) proportion of resistant hosts in the landscape relative to susceptible hosts . As pointed out in the introduction , evolutionary and epidemiological outcomes are not necessarily correlated . In this context , several epidemiological outputs are used in this study to characterise the level of protection provided by a deployment strategy against the potential damage caused by an epidemic . Some previous studies focused on the final state of the simulations or on the point at which a stable evolutionary equilibrium is reached , thus proposing criteria related to the final proportion of healthy [43 , 44] or infected [26 , 49 , 129] hosts . However , in addition to long-term measures related to stable equilibria , short-term and transitory periods measures are important because severe epidemics responsible for heavy losses may occur during these stages [125 , 132] . These periods can be accounted for by averaging the number of healthy hosts over the whole simulation run using an analogy of our Green Leaf Area [35–37 , 40 , 42 , 133] or the number of infected hosts using the area under disease progress curve ( AUDPC ) [28 , 32 , 33 , 48 , 50] . Interestingly , these measures offer the possibility to concentrate on different evolutionary phases , for example the short-term period following resistance deployment until resistance breakdown , and the long-term period once resistance is overcome [45] . In the present study , the proposed outputs are based on both GLA and AUDPC , computed not only for the whole simulation run to have a global snapshot of the epidemiological outcome , but also for different time periods: from initial resistance deployment until the first resistance is overcome , from the time when all resistances are overcome until the end of the simulation as well as during the transitory period . The performance of various deployment strategies can differ during these three periods , since they are associated with different epidemiological contexts . Thus , it is important to consider these different measures together . Moreover , the objectives of different stakeholders define whether such criteria should be computed from the AUDPC or from the GLA . Indeed , if the objective is to limit the amount of disease , the use of AUDPC-based variables may be more appropriate , since they target infected hosts . In contrast , GLA-based variables represent the amount of healthy hosts which generally represent the largest contribution to crop yield . Thus , this variable can be useful when considering the impact of a given deployment strategy on productivity , especially when resistance costs , different planting densities , or different host species are involved . It is interesting to note that here , only healthy hosts were assumed to participate in host growth and contribute to final yield , considering that the infection by a pathogen consumes host resources . For rust pathogens of cereal crops , this assumption seems reasonable since it has been shown that manual defoliation of wheat leaves decreases yield less than infection by Puccinia striiformis [73] . Nevertheless , this assumption can easily be changed by including hosts at different sanitary stages ( e . g . latently infected but not yet diseased ) in the logistic equation of host growth and in computation of the GLA . In this study , we present an initial exploration of the model where we evaluate the potential of combining qualitative and quantitative resistance to control rust of cereal crops . We note that in this exploration some parameters ( in particular mutation probabilities , see S1 Text ) were arbitrarily fixed to study a simple and theoretical situation where a resistant cultivar carrying a single major gene would be rapidly overcome following deployment . This is because our intent in this case was to compare different resistance combinations rather than provide an absolute prediction of the durability and efficiency of a particular strategy . Regardless , altering the mutation probabilities , as long as they are the same for all infectivity genes and aggressiveness components , changes our results quantitatively but not qualitatively ( see S1 Text and the results of simulation performed with smaller mutation probabilities in S5 Fig ) . In a recent review dealing with the combination of qualitative and quantitative resistances , Pilet-Nayel et al . [143] wrote “There is still a need to adequately choose resistance QTLs to create optimal combinations and limit QTL erosion” . The present work highlights that longer latent periods may be one promising target . Nevertheless , the general features of the model could be used to test if our conclusions hold in different contexts . Our simulations showed that the efficiency of quantitative resistance ( ρw ) has a strong impact on the durability and epidemiological control of plant resistance . Therefore , in future studies we plan to assess the influence of the efficiency of qualitative resistance by including partially effective major genes ( ρg<1 , [3] ) . We also envisage varying the mutation probabilities ( τg and τw ) , cost of infectivity ( θg ) , cost of aggressiveness ( θw ) , and number of steps to erode a trait for quantitative resistance ( Qw ) , to simulate various possible choices of major genes and traits for quantitative resistance . It would also be of interest to simulate and investigate the potential for combinations of several sources of quantitative resistance , whose performance on disease severity has been experimentally demonstrated [144] . Finally , we will also explore different spatiotemporal strategies to deploy plant resistance to pathogens . In particular , we plan to compare mosaics , mixtures , rotations and pyramids of genetic resistances with respect to their epidemiological and evolutionary outcomes . The challenge is to identify strategies for a given host-pathogen interaction that are not only durable and efficient , but also feasible and likely to be adopted . In this context , promising strategies identified with the simulation model could be experimentally tested in the field . In addition , broader consideration of the economic context will also help maximise the chances that identified strategies are considered for real deployment . | There are many recent examples which demonstrate the evolutionary potential of plant pathogens to overcome the resistances deployed in agricultural landscapes to protect our crops . Increasingly , it is recognised that how resistance is deployed spatially and temporally can impact on rates of pathogen evolution and resistance breakdown . Such deployment strategies are mainly based on the combination of several sources of resistance at different spatiotemporal scales . However , comparison of these strategies in a predictive sense is not an easy task , owing to the logistical difficulties associated with experiments involving the spread of a pathogen at large spatio-temporal scales . Moreover , both the durability of a strategy and the epidemiological protection it provides to crops must be assessed since these evaluation criteria are not necessarily correlated . Surprisingly , no current simulation model allows a thorough comparison of the different options . Here we describe a spatio-temporal model able to simulate a wide range of deployment strategies and resistance sources . This model , implemented in the R package landsepi , facilitates assessment of both epidemiological and evolutionary outcomes across simulated scenarios . In this work , the model is used to investigate the combination of different sources of resistance against fungal diseases such as rusts of cereal crops . | [
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] | 2018 | Assessing the durability and efficiency of landscape-based strategies to deploy plant resistance to pathogens |
Recently , a number of advanced screening technologies have allowed for the comprehensive quantification of aggravating and alleviating genetic interactions among gene pairs . In parallel , TAP-MS studies ( tandem affinity purification followed by mass spectroscopy ) have been successful at identifying physical protein interactions that can indicate proteins participating in the same molecular complex . Here , we propose a method for the joint learning of protein complexes and their functional relationships by integration of quantitative genetic interactions and TAP-MS data . Using 3 independent benchmark datasets , we demonstrate that this method is >50% more accurate at identifying functionally related protein pairs than previous approaches . Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes , a number of which are novel or have been substantially expanded by addition of new subunits . Interestingly , we find that complexes that are enriched for aggravating genetic interactions ( i . e . , synthetic lethality ) are more likely to contain essential genes , linking each of these interactions to an underlying mechanism . These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function .
Genetic interactions are logical relationships between genes that occur when mutating two or more genes in combination produces an unexpected phenotype [1]–[3] . Recently , rapid screening of genetic interactions has become feasible using Synthetic Genetic Arrays ( SGA ) or diploid Synthetic Lethality Analysis by Microarray ( dSLAM ) [4] , [5] . SGA pairs a gene deletion of interest against a deletion to every other gene in the genome ( in turn ) . The growth/no growth phenotype measured over all pairings defines a genetic interaction profile for that gene , with no growth indicating a synthetic-lethal genetic interaction . Alternatively , all combinations of double deletions can be analyzed among a functionally-related group of genes [6]–[8] . A recent variant of SGA termed E-MAP [9] has made it possible to measure continuous rates of growth with varying degrees of epistasis ( based on imaging of colony sizes ) . “Aggravating” interactions are indicated if the growth rate of the double gene deletion is slower than expected , while for “alleviating” interactions the opposite is true [10] , [11] . One popular method to analyze genetic interaction data has been to hierarchically cluster genes using the distance between their genetic interaction profiles . Clusters of genes with similar profiles are manually searched to identify the known pathways and complexes they contain as well as any genetic interactions between these complexes . This approach has been applied to several large-scale genetic interaction screens in yeast including genes involved in the secretory pathway [8] and chromosome organization [6] . Segré et al . [12] extended basic hierarchical clustering with the concept of “monochromaticity” , in which genes were merged into the same cluster based on minimizing the number of interactions with other clusters that do not share the same classification ( aggravating or alleviating ) . Another set of methods has sought to interpret genetic relationships using physical protein-protein interactions [13] . Among these , Kelley and Ideker [14] used physical interactions to identify both “within-module” and “between-module” explanations for genetic interactions . In both cases , modules were detected as clusters of proteins that physically interact with each other more often than expected by chance . The “within-module” model predicts that these clusters directly overlap with clusters of genetic interactions . The “between-module” model predicts that genetic interactions run between two physical clusters that are functionally related . This approach was improved by Ulitsky et al . [15] using a relaxed definition of physical modules . In related work , Zhang et al . [16] screened known complexes annotated by the Munich Information Center for Protein Sequences ( MIPS ) to identify pairs of complexes with dense genetic interactions between them . One concern with the above approaches , and the works by Kelley and Ulitsky in particular , is that they make assumptions about the density of interactions within and between modules which have not been justified biologically . Ideally , such parameters should be learned directly from the data . Second , between-module relationships are identified by separate , independent searches of the network seeded from each genetic interaction . This “local” search strategy can lead to a set of modules that are highly overlapping or even completely redundant with one another . Finally , genetic interactions are assumed to be binary growth/no growth events while E-MAP technology has now made it possible to measure continuous values of genetic interaction with varying degrees of epistasis . Here , we present a new approach for integrating quantitative genetic and physical interaction data which addresses several of these shortcomings . Interactions are analyzed to infer a set of modules and a set of inter-module links , in which a module represents a protein complex with a coherent cellular function , and inter-module links capture functional relationships between modules which can vary quantitatively in strength and sign . Our approach is supervised , in that the appropriate pattern of physical and genetic interactions is not predetermined but learned from examples of known complexes . Rather than identify each module in independent searches , all modules are identified simultaneously within a single unified map of modules and inter-module functional relationships . We show that this method outperforms a number of alternative approaches and that , when applied to analyze a recent EMAP study of yeast chromosome function , it identifies numerous new protein complexes and protein functional relationships .
We first sought to quantitatively confirm whether , and to what degree , physical and genetic interactions could indicate common membership in a protein complex . To provide genetic data for analysis , we obtained the previously-published results from a large E-MAP of yeast chromosomal biology [6] . This study consisted of genetic interactions measured among 743 genes ( including 74 essential genes ) , yielding quantitative values for 182 , 669 gene pairs ( 66% of all possible pair-wise measurements ) . Each gene pair was assigned an S-score , where positive scores indicate protein pairs for which the double mutant grows better than expected ( i . e . , an alleviating interaction ) and negative scores indicate pairs for which the double mutant grows worse than expected ( i . e . , a synthetic sick/lethal or aggravating interaction ) where the expectation is that the double-deletion of unrelated proteins will have a growth rate equivalent to the multiplicative product of the two individual growth rates [17] . In all , 14 , 237 gene pairs ( 8% ) showed strong genetic interactions with |S|>2 . 5 . Physical interactions were taken from a recent computational integration of two large datasets measured by co-immunoprecipitation followed by mass spectrometry [18] . This study assigned to each pairwise interaction a Purification Enrichment ( PE ) score , with larger values representing a greater likelihood of true binding . Figure 1A confirms that protein pairs with higher PE-scores are more likely to operate in a known small-scale protein complex recorded in the MIPS database [19] ( versus protein pairs chosen at random ) . This result is expected considering that PE-scores were trained based on these complexes [18] . Figure 1B shows that protein pairs with both positive and negative S-scores are more likely to operate within a known complex . Positive ( alleviating ) interactions are well-known to occur between subunits of a complex [6] . Negative ( aggravating ) interactions are to a lesser degree so , although the mechanism has not been as clear as for the alleviating case [20] . By comparing the magnitudes of enrichment between Figures 1A and 1B , it is apparent that extreme S-scores are at least as indicative of co-complex membership as strong PE-scores , if not more so ( ∼100-fold enrichment versus ∼50-fold enrichment , respectively ) . Together , these exploratory findings suggest that the physical and genetic information can indeed provide a basis for the identification of protein pairs involved in the same complex . To capture these trends , we formulated an approach to classify a protein pair as operating either within the same module or between functionally related modules given its genetic and physical interaction scores . This approach seeks to categorize interactions supported by both strong genetic and physical evidence as operating within a module ( i . e . , complex ) . Interactions with a strong genetic but weak physical signal are better characterized as operating between two functionally related modules . Given within-module and between-module likelihoods for individual interactions , an agglomerative clustering procedure seeks to merge these interactions into increasingly larger modules and to identify pairs of modules interconnected by bundles of many strong genetic interactions ( Figure 1C ) . Full details are provided in Methods . Applying this method , we identified 91 distinct modules with an average size of 4 . 1 proteins per module . Figure 2 gives an overview of a subset of the identified modules and inter-module links . Complete results are catalogued at http://www . cellcircuits . org/Bandyopadhyay2008/html/ . Overall , these results suggest ten novel complexes not recorded in either the small-scale or high-throughput MIPS compendium , covering 23 proteins in total . The results also identify 84 new subunits of known complexes ( Dataset S1 ) . Through permutation testing , 19 versus 9 of the identified modules could be categorized as enriched for alleviating or aggravating genetic interactions , respectively . A total of 313 significant genetic relationships were identified between modules , 94 versus 219 of which were enriched for alleviating or aggravating interactions . The method of choice for interpreting quantitative genetic interactions has been hierarchical clustering ( HCL ) of genes based on pair-wise distances between their genetic interaction profiles [6] , [8] . We compared the clusters obtained using HCL to the modules obtained with our present approach ( Bandyopadhyay et al . ) using three gold-standard metrics: gene co-expression ( Figure 3A ) , co-functional annotation ( Figure 3B ) , or membership in the same previously-identified complex ( Figure 3C ) . To ensure a fair comparison between the two approaches , HCL and Bandyopadhyay et al . were evaluated across a range of coverages ( number of gold-standard gene pairs recovered by the predicted clusters/modules; see Methods ) . For all three benchmarks , our performance was substantially higher than that of the HCL-based approach at most levels of coverage ( and at a level of coverage corresponding to the 91 modules reported above; dotted vertical line in Figure 3 ) . We considered that one reason why HCL performed less favorably might be that it was not given access to the same information ( i . e . , the physical network ) . This is especially true for the metric based on previously identified complexes , in which complexes were annotated based on the same high-throughput protein interactions used here . To investigate this possibility , we extended HCL to incorporate physical interactions in a straightforward fashion , by merging only those clusters which share a physical interaction between them ( HCL-PE ) . Although this approach outperformed hierarchical clustering without physical interactions , it was outperformed by the present approach by at least 50% across the three metrics . Finally , our method also shows improvement over the previous approach of Kelley and Ideker [14] for integrating genetic and physical interactions ( Figure 3 ) . Nineteen versus nine of the learned modules had significant enrichment for alleviating versus aggravating genetic interactions , respectively . Identification of “alleviating” modules is expected , since subunits of a complex operate together and the phenotypic effect of removing any pair of proteins in a complex should be no worse than removing any single protein individually . The presence of aggravating interactions within modules was more intriguing . One way in which aggravating interactions could occur among the subunits of a complex is if its function is essential , i . e . , the loss of the complex's function causes a lethal phenotype . In these cases , some protein subunits should be encoded by essential genes , while other subunits might be redundant and thus essential in pairwise combinations [20] . To test the hypothesis that essential genes are more likely in aggravating modules , we analyzed both MIPS small-scale complexes and our learned modules for the presence of essential genes ( Figure 4 ) . We found that 80% of aggravating MIPS complexes contained an essential gene , compared to only 20% of alleviating MIPS complexes ( a four-fold increase ) . Similarly , of the aggravating modules determined by our approach , 55% contained an essential gene compared to only 21% of alleviating modules ( a 2 . 6-fold increase ) . These results are not correlated with module size , as the median size of aggravating learned modules is less than the median size of alleviating learned modules . They suggest that , regardless of the technique for identifying complexes , those containing essential genes tend to be composed of primarily aggravating genetic interactions . Mechanistically , this might occur through a variety of means , including proteins with separate but functionally redundant roles in maintaining complex integrity , or subunit substitution by paralogous proteins .
Figure 5 presents detailed diagrams of example functional relationships elucidated by our module mapping method . Figure 5A shows the alleviating relationship between the RTT109-VPS75 histone acetyltransferase complex [6] , [21] , [22] and Elongator , a complex that is associated with RNA Polymerase II and is involved in transcriptional elongation [23] . Since several subunits both of Elongator and RTT109/VPS75 have been shown to be involved in histone acetylation levels [22] , [24] , these two complexes may operate together to effectively clear histones from actively transcribed regions . To identify further mechanisms of their cooperation , future studies may search for specific residues of histone H3 whose acetylation levels are modulated by both complexes . This example highlights the utility of an integrated approach , since although RTT109 and VPS75 are known to form a complex their genetic interaction profiles are not congruent ( correlation of profiles of −0 . 1 ) and had been missed by hierarchical clustering . Figure 5B highlights non-essential components ( LRP1 and RRP6 ) of the exosome , which contributes to the quality-control system that retains and degrades aberrant mRNAs in the nucleus [25] . These components have alleviating interactions with a complex composed of Lsm proteins involved in mRNA decay . Figure 5C centers on BRE1/LGE1 , subunits of the Rad6 Histone Ubiquitination Complex ( RAD6-C; the Rad6 protein itself was not covered by the original E-MAP screen ) [26] , [27] . RAD6-C is functionally connected with two other complexes , SWR-C and COMPASS . SWR-C functions to regulate gene expression through the incorporation of transcriptionally active histone variant H2AZ [28]–[30] , while COMPASS is involved in mediating transcriptional elongation and silencing at telomeres through methylation of histone H3 [31] . Interactions between RAD6-C and SWR are aggravating , suggesting synergy or redundancy towards an essential cellular function . Interactions between RAD6-C and COMPASS are alleviating , suggesting they operate in a potentially serial fashion . Consistent with this analysis , it has been shown that histone H2B ubiquitination by RAD6-C is a prerequisite for histone H3 methylation by COMPASS [32] , [33] . Several trends emerge from the performance analysis in Figure 3 . First , genetic interaction data alone can yield substantial information about molecular pathways . Functionally similar proteins often have similar profiles of genetic interaction , a feature we have previously exploited to identify functional interactions between complexes as well as to identify new members of complexes based on a combination of weak physical and genetic data [14] . On the other hand , the ability to detect complexes can be greatly improved by adding information about protein physical interactions . Even the straightforward HCL-PE method was able to greatly improve the accuracy and coverage according to most metrics , while the greatest performance was achieved by the improved probabilistic framework we have presented in this study . This framework has led to the inclusion of YKL023W as a potential new member of the SKI complex and YGR071C in a complex with VID22/TBF1 ( Figure 2 ) , for a total of 84 novel protein subunit assignments to complexes ( Dataset S1 ) . Both of these examples have both physical and genetic support and would have been missed by an approach based on either type of interaction alone . Future work may seek to incorporate yet additional types of linkages such as protein-DNA interactions [34] , [35] , kinase-substrate phosphorylations [36] , or other genetic perturbation data such as eQTLs [37] . There are also opportunities to refine the modeling framework further . Here , a gold-standard set of complexes was used to explicitly learn the relationship between physical interactions , genetic interactions , and module membership . This supervised approach could be extended to also learn which features best indicate the inter-module functional relationships , perhaps through curation of a gold-standard set of interacting complexes .
We analyze the interaction data to infer a set of protein modules and a set of inter-module links ( Figure 1C ) . A protein module is defined as a set of proteins that are connected through protein-protein interactions and are likely to represent a protein complex with a coherent cellular function . Inter-module links capture functional relationships between modules and may be of two types , aggravating or alleviating . The complete state of the system is described by a set M of modules , each module defining a set of proteins , and a set N of pairs of modules that are functionally linked . For each pair of proteins ( a , b ) we compute a log ratio W of the likelihood that a and b fall within the same module versus the likelihood that they are unrelated ( i . e . , occur in the background ) . The function uses two sources of information that are indicative of protein complex co-membership: the strength of protein-protein physical interaction ( PE ) and the strength of genetic interaction ( S ) : ( 1 ) For a given data type ( PE or S ) the log likelihood ratio ( LLR ) is defined as: ( 2 ) The probability Pwithin is determined using logistic regression training on 217 complexes curated from small-scale studies in MIPS [19] . Pbackground is the probability of randomly observing the observed value ( PE or S ) for the pair ( a , b ) in the background of all gene pairs . As shown in Figure 1A and 1B , it is clear that higher values of PE are predictive of MIPS complex membership . As both positive and negative values of S are predictive , LLRS ( a , b ) is trained on the absolute value of S . A third predictor based on the correlation of genetic interaction profiles was also evaluated but did not result in any gain in performance ( Figure S1 ) . A similar function B ( ) is formulated to assess the likelihood that two proteins fall between modules that are functionally linked . The function inputs the same two sources of information on protein-protein and genetic interactions ( PE and S ) . Unfortunately , there is no curated set of functionally related complexes that can be used as positive training examples for regression . Instead , B ( ) is derived from the within-module LLRs , assuming that between-module interactions have a similar pattern of genetic interactions but lack physical interactions: ( 3 ) This function captures both aggravating and alleviating genetic interactions between two functionally-related modules . It also ensures such modules are physically separate—if not , they would be better considered as a single module . Given the above functions W ( ) and B ( ) , we compute the likelihood of the complete system ( i . e . , given a particular choice M of modules and N of inter-module links ) : ( 4 ) The first term accumulates the within-module scores among gene pairs assigned to the same module . The second term accumulates the inter-module scores for gene pairs spanning any two modules . Gene pairs spanning unlinked modules do not contribute to L . The final term is a tunable reward which scales with module size . Larger values of α result in fewer , larger complexes . The final module map shown in Figure 2 was generated using α = 1 . 6 , based on its good coverage and performance across all three metrics in Figure 3 . Assignment of gene to modules and of inter-module links is performed using a simple variant of UPGMA hierarchical clustering [38]: ( a ) Initially , each gene is assigned to a separate module; ( b ) Each pair of modules ( m1 , m2 ) is evaluated for merging into a single module m = m1∪m2; the pair-wise merging that results in the largest increase in L is chosen; ( c ) Repeat step b until no module merge operation increases L . At each iteration of step b , L is optimized over all possible ways of assigning inter-module links ( i . e . , module pairs are linked whenever the second term in Equation 4 is positive ) . Because each inter-module link is scored independently , additions or deletions of links from the system need only be evaluated for modules that are under evaluation for merging . Subsequent to the above procedure , each between-module link is evaluated to assess its significance and whether it represents predominantly aggravating or alleviating genetic interactions . A two-tailed p-value is computed by indexing the sum of S-scores for gene pairs falling across the two modules against a distribution of 106 sums of equal numbers of S-scores drawn from random gene pairs . To account for multiple testing , we use the distribution of between-module p-values to compute a local false discovery rate ( FDR ) [39] . All reported between-module links have an inferred FDR of <10% with the global map in Figure 2 constrained to links with an FDR of <1% . Module maps in Figure 2 and Figure 5 are visualized using the Cytoscape package [40] , [41] . To label modules as “aggravating” or “alleviating” ( Figure 2 ) , the sum of S-scores for gene pairs assigned to the same module is compared to a distribution of sums of equal numbers of randomly drawn S-scores . Modules with a two-tailed p-value<0 . 05 are labeled as either alleviating ( right tail ) or aggravating ( left tail ) . Co-expressed gene pairs were defined using gene expression datasets culled from the Stanford Microarray Database covering ∼790 conditions [42] . The validation set was taken as the top 5% ( 13 , 014 ) of pairs ranked by Pearson correlation coefficient . The co-function set was based on yeast Gene Ontology annotations from November 2005 which predates the publication of large scale TAP-MS studies that were used to generate the PE-score [43] . This set was taken as the top 5% ( 13 , 052 ) most functionally similar gene pairs covered in the E-MAP . Functional similarity was determined by comparison to the background probability of picking two genes with the same shared functional annotation from the entire yeast genome ( via a hypergeometric test ) . Similar analysis using current Gene Ontology annotation was also performed ( Figure S2 ) . The co-complex validation set was defined as gene pairs from 846 MIPS complexes annotated using high-throughput approaches ( with interactions also appearing in small-scale studies removed ) for a total of 2 , 885 gold-standard pairs . The size and number of final modules was varied by altering the α parameter ( see above ) . To assess performance at low coverage we ran the method with no reward contribution ( remove the third term in Equation 4 by setting α = −∞ ) and plotted the performance of the algorithm at each merge step , which ultimately connects with the performance of the method as α is increased . For HCL and HCL-PE methods , the size and number of modules were varied by changing the level at which the hierarchy was cut . | Biologists are currently producing large amounts of data focused on physical and genetic protein interactions . Physical interactions dictate the architecture of the cell in terms of how direct associations between molecules constitute protein complexes , while genetic interactions define functional relationships through cause-and-effect relationships between genes . Both of these types of interactions can indicate shared protein functions; however , these two types of interactions are commonly non-overlapping , making their interpretation difficult . Along these lines , it has been noted that genetic interactions commonly occur between members of the same protein complex as well as between functionally related complexes . Here , we present an integrated framework that incorporates both types of interactions to generate large maps of protein complexes as well as highlight connections between related complexes . The ability to rapidly integrate these two types of data in an automated fashion can accelerate the discovery of new members of protein complexes as well as identify functionally related cellular components . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"computational",
"biology/systems",
"biology",
"genetics",
"and",
"genomics/chromosome",
"biology"
] | 2008 | Functional Maps of Protein Complexes from Quantitative Genetic Interaction Data |
Transmission of the malaria parasite to its vertebrate host involves an obligatory exoerythrocytic stage in which extensive asexual replication of the parasite takes place in infected hepatocytes . The resulting liver schizont undergoes segmentation to produce thousands of daughter merozoites . These are released to initiate the blood stage life cycle , which causes all the pathology associated with the disease . Whilst elements of liver stage merozoite biology are similar to those in the much better-studied blood stage merozoites , little is known of the molecular players involved in liver stage merozoite production . To facilitate the study of liver stage biology we developed a strategy for the rapid production of complex conditional alleles by recombinase mediated engineering in Escherichia coli , which we used in combination with existing Plasmodium berghei deleter lines expressing Flp recombinase to study subtilisin-like protease 1 ( SUB1 ) , a conserved Plasmodium serine protease previously implicated in blood stage merozoite maturation and egress . We demonstrate that SUB1 is not required for the early stages of intrahepatic growth , but is essential for complete development of the liver stage schizont and for production of hepatic merozoites . Our results indicate that inhibitors of SUB1 could be used in prophylactic approaches to control or block the clinically silent pre-erythrocytic stage of the malaria parasite life cycle .
Transmission of the malaria parasite to a vertebrate host is initiated by the bite of an infected Anopheline mosquito . The inoculated sporozoites migrate from the site of inoculation , enter the circulation , and are arrested in liver sinusoids where they traverse the vascular endothelium and invade hepatocytes , coming to rest within an intracellular membrane-bound parasitophorous vacuole ( PV ) [1] , [2] . After an initial period of non-replicative development , which lasts around 24 h in the rodent malaria species Plasmodium berghei , the intracellular parasite - now known as an exoerythrocytic form ( EEF ) - initiates an asexual replicative program . This begins with several rounds of nuclear division to form a multinucleated syncytium or schizont , concomitant with a large increase in the size of the PV to accommodate the growing parasite . Approximately 55 h following hepatocyte invasion ( in hepatoma cells ) the single plasma membrane of the schizont begins to invaginate around groups of parasite nuclei to form the so-called cytomere stage [3] , [4] . Subsequent further invagination of the parasite plasma membrane produces clearly defined individual merozoites tightly packed within the PV . Shortly thereafter , the PV membrane ( PVM ) disintegrates , releasing the merozoites to move freely within the host cell cytoplasm . PVM rupture triggers an unusual form of cell death in the host cell , involving DNA condensation , disintegration of host cell mitochondria and loss of plasma membrane proteins , but lacking certain other classical features of apoptosis such as caspase activation and loss of host plasma membrane phospholipid asymmetry [4] , [5] , [6] . In vitro , infected hepatoma cells such as HepG2 cells round up at this point and detach from their monolayers to float freely in the cultures [4] , [5] . Just prior to detachment , merozoite-filled vesicles called merosomes , each surrounded by membrane of host cell origin , are extruded from the host cells . In vivo , these enter the lumen of the liver sinusoids from where they are carried to the pulmonary microvasculature to rupture , allowing egress of their merozoite cargo [4] , [5] , [6] . The merozoites invade erythrocytes to initiate the asexual blood stage cycle . The entire liver stage has a duration of between 2 and 15 days [7] , [8] , depending on the Plasmodium species , and culminates in the production and release of thousands of hepatic merozoites from each infected hepatocyte . Whilst not itself associated with any pathology , the liver stage and other pre-erythrocytic stages are a prerequisite to the asexual blood-stage cycle in a natural malarial infection , and so are potential targets for prophylactic immune-based or chemotherapeutic interventions designed to prevent disease . Compared to Plasmodium asexual blood stages , liver stage malaria parasites are relatively difficult to access [7] , [8] and so , despite these elegant and detailed morphological descriptions of the hepatic malaria life cycle , little is known of the signals and molecular players involved in liver stage merozoite development , PVM rupture , merosome formation and merozoite egress . The limited available data suggest that in many respects liver stage merozoites are probably very similar in makeup to their well-studied blood stage counterparts [9] . Elements of merozoite morphogenesis and egress are therefore likely shared between the liver and blood stages . As an example of this , treatment of mature hepatic or erythrocytic schizonts with the cysteine protease inhibitor E64 prevents PVM rupture [5] , [10] , [11] , implicating a common role for cysteine protease ( s ) in merozoite release . The effects of E64 may result from inhibition of host cell calpain-1 activity , which has been implicated in egress [12] , as well as of host cell cysteine proteases implicated in the parasite-induced cell death [13] . Alternatively or in addition , the target ( s ) of E64 may include members of the parasite serine repeat antigen ( SERA ) family , which are expressed in mature stages of blood schizonts [14] , [15] , [16] , [17] . SERA proteins may play a role in egress [18] , [19] , and some of them have E64-sensitive cysteine protease activity [15] . In blood stages some or most SERA proteins are substrates of a conserved Plasmodium subtilisin-like serine protease called SUB1 that is discharged from specialised secretory organelles called exonemes into the PV lumen minutes before egress [20] , [21] , [22] , [23] . SUB1 cleaves the SERA proteins to release their central papain-like domain [15] , [20] . SUB1-mediated cleavage of P . berghei SERA3 ( PbSERA3 ) has been shown to activate its protease activity [15] , suggesting that one important role of SUB1 may be to initiate a protease cascade that leads to egress . Discharge of SUB1 into the PV also allows it to modify several other important merozoite proteins , including the major glycolipid-anchored merozoite surface protein MSP1 [24] , [25] , which is thought to act as an erythrocyte binding ligand [26] , [27] , [28] . SUB1 therefore likely plays a central role in both development and egress of blood stage schizonts . Intriguingly , whereas both MSP1 and members of the SERA protein family are expressed in liver stage schizonts [9] , [11] , it is not known whether SUB1 is expressed in liver stages , or whether it has a similarly important role in maturation and release of liver stage merozoites . The study of genes in liver stages that are essential during the asexual erythrocytic phase of the life cycle requires an inducible or stage specific system for gene disruption . The site specific recombinase Flp is currently the only validated system [29] to knock out or knock down essential genes in liver stages . A panel of highly efficient deleter lines expressing a thermosensitive variant of the recombinase , FlpL , under different sporozoite specific promoters is now available and these have been used to characterise essential malarial gene functions in liver stages , including that of MSP1 [30] and the parasite cyclic GMP ( cGMP ) -dependent protein kinase , PKG [31] . To control a gene through Flp or FlpL it is necessary to introduce two 34 bp flippase recognition target ( FRT ) sites into the genome such that they flank a crucial part of the target gene , which becomes excised when Flp is expressed , thereby inactivating the gene of interest . Placing FRT sites in a genetic modification vector remains a major challenge . To achieve a complete gene knock out upon activation of Flp , it would be desirable to flank the entire target gene with FRT sites . This requires large allelic exchange vectors with at least one very long homology arm comprising the target gene plus an additional 1 kb or more of upstream homologous sequence to achieve genomic integration of the FRT site most distant to the selection cassette . Constructing such large vectors in E . coli can be difficult , or even impossible , due to the high ( >77% ) AT content and repetitive nature of genomic DNA ( gDNA ) of most Plasmodium species , which causes instability in circular high-copy plasmids . Recently a P . berghei gDNA library with high sequence integrity , relatively large inserts ( averaging ∼9 . 0 kb ) and covering now >85% of genes was generated in a linear , low copy plasmid in E . coli [32] . Here we present molecular tools and protocols that exploit the efficiency , speed and robustness of recombinase mediated engineering in E . coli to convert a gDNA library clone with a 9 . 6 kb genomic insert containing the P . berghei sub1 ( pbsub1 ) gene into a complex allelic exchange vector for the Flp mediated deletion of the entire pbsub1 gene . We generate a conditional knock out parasite in which we examine the expression and function of SUB1 in liver stages of P . berghei . We show that SUB1 is expressed in subcellular organelles of the liver stage schizont that resemble exonemes , and that expression of the protease is indispensable for completion of the liver stage of the parasite life cycle . Unexpectedly , parasites lacking SUB1 exhibit a defect in development prior to egress , indicating a hitherto unappreciated role for SUB1 in intracellular parasite growth .
A previous transcriptomic and proteomic analysis of the rodent malaria species P . yoelii indicated the presence of P . yoelii sub1 mRNA in liver stages but detected no SUB1 protein by mass spectrometry [33] . However , mass spectrometric analysis of blood-stage schizonts of the human malaria pathogen P . falciparum has detected only between 1 and 8 peptides [34] , [35] , [36] , suggesting that – as with many enzymes - SUB1 is likely a poorly abundant constituent of the total proteome . To address the question of whether SUB1 is expressed in P . berghei liver stages , we produced a rabbit polyclonal antibody specific for the catalytic domain of P . berghei SUB1 ( PbSUB1 ) . Examination of P . berghei blood stage schizont extracts by Western blot using the antibody produced signals likely corresponding to the full-length and processed ( mature ) forms of PbSUB1 ( Supplemental Figure S1A in Text S1 ) , by analogy with the maturation profile previously observed with recombinant forms of SUB1 from P . berghei and three other Plasmodium species [37] , [38] , [39] . The anti-PbSUB1 antibodies were then used to examine P . berghei liver stage EEFs by immunofluorescence assay ( IFA ) . Hepatoma cells infected with sporozoites of a drug selectable marker-free transgenic P . berghei clone that constitutively expresses GFP [40] were fixed 64 h post infection and probed with the antibodies . As shown in Figure S1B in Text S1 , a punctate signal was obtained that is highly reminiscent of the exoneme-specific pattern previously observed in P . falciparum blood stage schizonts probed with polyclonal or monoclonal antibodies ( mAb ) against P . falciparum SUB1 [20] , [22] . This suggested that PbSUB1 may be localised in similar subcellular organelles in liver schizonts . To further test this interpretation , we generated a P . berghei line expressing epitope-tagged PbSUB1 ( called PbSUB1-HA ) using homologous recombination to modify the endogenous pbsub1 gene ( PBANKA_110710 ) in the GFP-expressing parasite background ( Figure S2 in Text S1 ) . Western blot analysis of blood stage schizont extracts from PbSUB1-HA parasites using an anti-HA mAb detected a strong double band migrating slightly more slowly than that detected by the anti-PbSUB1 rabbit antibodies ( Figure S3A in Text S1 ) , consistent with the expected small increase in mass of the epitope-tagged PbSUB1 as a result of its fusion to the HA epitope tag . In contrast , Western blot analysis of PbSUB1-HA salivary gland sporozoite extracts with the anti-HA antibody , or with the anti-PbSUB1 antisera , detected no specific signal ( not shown ) . IFA analysis of mature blood stage ( Figure S3B in Text S1 ) or mature liver stage ( Figure 1 and Figure S4 in Text S1 ) PbSUB1-HA schizonts with the anti-HA mAb again produced a clear punctate signal . The foci were associated with but distinct from individual merozoite nuclei , and again similar to the exoneme-specific signal previously observed in P . falciparum blood stage schizonts . No PbSUB1-HA IFA signal was detected in salivary gland sporozoites or early liver stage schizonts ( Figure S3C in Text S1 , Figure 1 top row and Figure S4 in Text S1 ) or at the cytomere stage when the parasite plasma membrane is just beginning to invaginate to surround groups of nuclei ( Figure S5 in Text S1 ) . Collectively , these results convincingly demonstrate that PbSUB1 is not expressed in sporozoites or early EEFs , but is expressed in mature liver stage schizonts , where it likely accumulates in subcellular organelles similar to the exonemes previously described in blood stage schizonts . In our previous work on P . falciparum SUB1 [20] we were unable to obtain viable parasites in which the pfsub1 gene was disrupted , suggesting an essential role in blood stages . Attempts to disrupt the pbsub1 gene in P . berghei blood stages similarly failed ( S . Yeoh , R . Tewari , O . Billker and M . Blackman , unpublished ) , suggesting that PbSUB1 too is indispensable in the erythrocytic parasite life cycle . To study the role of the pbsub1 gene in liver stages we therefore decided to exploit a recently-described conditional deletion approach [30] , [41] in which stage-specific expression of the Saccharomyces cerevisiae site-specific recombinase Flp ( or its thermosensitive variant FlpL ) is employed to disrupt a target gene in the late mosquito stages of the parasite life cycle . Working from a P . berghei genomic DNA library clone from the PlasmoGEM resource ( http://plasmogem . sanger . ac . uk/ ) containing 9 . 6 kb of the pbsub1 locus and neighbouring genes , we constructed allelic exchange vectors designed to flank the entire pbsub1 coding sequence with FRT sites , while at the same time inserting a C-terminal HA epitope tag into pbsub1 ( Figure 2A ) . The 5′ FRT site was introduced into one of two alternative positions in the large upstream intergenic region together with a constitutive promoter sequence of the P . berghei hsp70 gene . A promoterless GFP coding sequence was positioned immediately downstream of the second FRT site ( Figure 2A and Figure S6 in Text S1 ) . Precise placement of the FRT sites and other exogenous sequence both upstream and downstream of pbsub1 was achieved in 4 steps by recombinase mediated genetic engineering in E . coli , using both the improved Red/ET recombinase system of lambda phage [42] and transient expression of Flp as described in Supplemental Methods and Figure S6 in Text S1 . The constructs were designed such that , following integration by ends-out homologous recombination into the parasite genome , correct recombinase-mediated excision of the sequence lying between the FRT sites ( which included the epitope-tagged pbsub1 gene ) would reposition the GFP reporter adjacent to the hsp70 promoter , driving constitutive GFP expression only in those parasites in which deletion of the pbsub1 gene had occurred ( Figure 2A and Figure S6 in Text S1 ) . To prevent the FRT site from interfering with initiation of translation [30] , the start codon for GFP expression was placed just upstream of the 5′ FRT site , such that after excision the FRT sequence would be translated into a 12 residue N-terminal extension of GFP . The final constructs , called pJazz-FRTed-pbsub1 and pJazz-FRTed-pbsub1short ( which differed only in the placement of the 5′ FRT site at either ∼2 . 3 kb or ∼1 . 8 kb respectively upstream of the start ATG of the pbsub1 gene ) contained ∼7 kb and 700 bp regions of homology respectively at their 5′ and 3′ ends for homologous integration into the P . berghei genome . The constructs were transfected into the P . berghei UIS4/FlpL deleter clone [30] which expresses FlpL under control of the sporozoite stage-specific uis4 promoter . Transfected parasites were expanded under pyrimethamine treatment and cloned by limiting dilution to obtain 2 independent parasite clones ( named condSUB1 clone A and B ) transfected with the pJazz-FRTed-pbsub1 construct , and a single parasite clone transfected with the pJazz-FRTed-pbsub1short construct ( called condSUB1short ) . The expected homologous integration event in each of the parasite clones was confirmed by diagnostic PCR , Southern blot and pulse-field gel analysis ( Figure S6 in Text S1 ) . The condSUB1short and condSUB1 parasite clones did not express GFP , as expected , and exhibited no growth phenotype in blood stages ( data not shown ) , indicating that the modifications to the pbsub1 locus resulting from integration of the targeting constructs did not affect parasite viability . To initially assess stage-specific deletion of the modified pbsub1 gene in the condSUB1 clones , Anopheles stephensi mosquitoes fed on mice infected with the condSUB1 clone A parasites were subjected to a temperature shift to 25°C at 18 days following transmission in order to enhance activity of FlpL . At day 26 post-transmission dissected midguts , salivary glands and salivary gland sporozoites were examined microscopically . GFP expression was observed in 65±19% of the oocysts as well as in the majority of sporozoites recovered from the condSUB1 clone A-infected insects , consistent with the expected FlpL-mediated excision event ( Figure 2B ) . There was some variation between individual experiments ( n = 4 ) , but visual microscopic examination of the isolated day 26 condSUB1 sporozoites showed that the proportion of GFP-positive sporozoites was usually ∼90% ( though this varied somewhat in subsequent experiments; see below ) , similar to previous findings of others using the Flp/FRT system in P . berghei [30] , [41] , [43] . Analysis by genotyping PCR of sporozoites collected at day 18 and day 26 following transmission ( Figure 2C ) confirmed efficient , time-dependent excision of the flirted pbsub1 gene , with undetectable levels of the non-excised pbsub1 locus in the day 26 sporozoites . To examine the behaviour of the pbsub1-deficient parasites throughout their lifecycle , condSUB1-infected mosquitoes were allowed to feed on naïve mice at 26 days following transmission ( ‘bite-back’ infection ) , by which point most of the sporozoites observed in the insect salivary glands were expressing GFP . The bite-back mice were monitored for the appearance of blood-stage parasites , which were then recovered and analysed by genotyping PCR . As shown in Figure 2C , despite the predominance of the excision event in the previous mosquito stages , only the non-excised pbsub1 locus was detectable in the erythrocytic parasites that appeared in the bite-back mice ( observations from n = 7 independent experiments , each using 3–5 mice ) . These parasites did not express GFP ( not shown ) . Identical results were obtained in an independent experiment with the condSUB1 clone B as well as the condSUB1short parasites ( Figure S7 in Text S1 ) . These results strongly suggested that those sporozoites in which the pbsub1 gene had been deleted were incapable of successfully establishing a blood stage infection . To gain more insight into the nature of the defect resulting from pbsub1 deletion , we next examined whether the inability of PbSUB1-deficient sporozoites to establish a blood stage infection was a result of compromised hepatocyte invasion , although we considered this unlikely given our previous evidence that PbSUB1 is not expressed in sporozoites . To investigate this , we incubated hepatoma cells in vitro with 26 day condSUB1 clone A or clone B sporozoites and assessed their capacity to invade the cells . For this we used a modified differential staining assay [44] that distinguishes extracellular ( i . e . residual cell surface-bound ) sporozoites from intracellular parasites , combined with automated microscopy and high content image analysis software . Extracellular sporozoites were detected with an antibody against the circumsporozoite protein ( CSP ) on the parasite surface , whilst an antibody against GFP was used to detect all excised condSUB1 sporozoites . As a control for these assays we used sporozoites of the P . berghei UIS4/FlpL-F clone [41] , which constitutively expresses GFP under the same hsp70 promoter as used in the condSUB1 and condSUB1short clones . These sporozoites were also obtained from insects that had been placed at 25°C at 18 days following transmission . As shown in Figure 3A , no significant differences were observed in the proportions of GFP-positive infected host cells detectable 2 h following addition of control or condSUB1 sporozoites , indicating equivalent invasive capacity . This observation was confirmed under in vivo conditions by using qRT-PCR to quantify parasite liver loads following infection of mice with condSUB1 or control sporozoites . As shown in Figure 3B , there was no significant difference in parasite liver loads measured 40 h after intravenous inoculation of 20 , 000 condSUB1 or UIS4/FlpL-F sporozoites . These results showed that condSUB1 sporozoites are fully competent to initiate and establish a liver infection . Having determined that pbsub1-null sporozoites display no invasion phenotype in vitro or in vivo and are able to efficiently initiate intrahepatic growth , we next addressed whether the transmission defect observed in the excised condSUB1 parasites was due to a defect in subsequent liver-stage replication . Development of hepatic EEFs comprises a well-described set of morphological transitions , in which an early schizont gradually increases in size and passes through cytomere and merozoite formation stages before rupture of the PVM to allow release of the mature merozoites into the host cell cytosol . To initially assess expression of epitope-tagged PbSUB1 in the condSUB1 parasites and to attempt to confirm loss of PbSUB1 expression upon excision , we used IFA to compare the GFP-positive ( excised ) and GFP-negative ( non-excised ) EEFs obtained following infection of hepatoma cell cultures with condSUB1 clone A or B sporozoites , using antibodies against GFP and the HA epitope tag fused to PbSUB1 . Whilst mature GFP-negative ( non-excised ) condSUB1 liver stage schizonts displayed the expected punctate IFA signal , as observed previously with the PbSUB1-HA clone , we were able to detect only immature forms of GFP-positive ( excised ) condSUB1 EEFs ( Figure 4 ) , with no mature forms visible . This unexpected result was explained by subsequent findings described below . To investigate the intrahepatic development of PbSUB1-deficient parasites , the size of GFP-positive condSUB1 EEFs in infected hepatoma cell cultures was analysed at 28 h and 48 h post infection , comparing them with non-excised condSUB1 and control UIS4/FlpL-F EEFs using automated microscopy and image analysis software . Parasite identification in this case was achieved using antibodies to the P . berghei HSP70 heat-shock protein , whilst GFP expression was used to discriminate excised PbSUB1-deficient ( GFP-positive ) condSUB1 parasites from the minority of non-excised condSUB1 parasites . No significant differences in EEF dimensions were seen at the 28 h time point ( data not shown ) . However , as shown in Figure 5 , a small but significant reduction in the mean surface area ( 28±3% ) of the PbSUB1-deficient parasites was observed at 48 h post infection compared to both controls , indicating a subtle defect in schizont development . For a more detailed analysis of this phenotype , we investigated the appearance throughout EEF maturation of three parasite marker proteins with distinct subcellular locations . Starting from 24 h post invasion , infected hepatoma cells were examined by IFA using antibodies against the PVM protein EXP1 , the soluble PV protein PbSERA3 ( a late liver stage marker expressed from cytomere stage onwards that is eventually released into the host cell cytosol; [11] ) , and the plasma membrane protein MSP1 ( present from cytomere stage onwards and involved in the formation of hepatic merozoites [30] ) . At time points up to and including cytomere stage , expression and localisation of EXP1 and PbSERA3 was normal in the PbSUB1-deficient parasites ( Figure S8 in Text S1 ) . However , at very late time points ( from around 64 h onwards ) , whilst normal rupture of the PVM and associated release of PbSERA3 into the host cell cytoplasm was evident in the majority of the control infected cells , it was only very rarely detected in the PbSUB1-deficient parasites ( Figure 6 ) . Equally strikingly , whereas control parasites displayed as expected a clear MSP1 signal from cytomere stage onwards , which subsequently translocated to surround individual merozoites , the majority of the PbSUB1-deficient parasites completely lacked a detectable MSP1 signal ( Figure 6 lower panels and Figure 7 ) and showed no signs of correct merozoite formation . Multiple nuclei were observed in the early PbSUB1-deficient schizonts ( Figure 7 ) , indicating normal nuclear replication , but in later stages many of the schizont nuclei appeared condensed and abnormal . To produce a quantitative description of these observations , we used confocal microscopy in two independent experiments to categorise a total of 40–60 individual parasitised hepatoma cells at each of several time points following infection with either the excised condSUB1 parasites or the control UIS4/FlpL-F parasites ( Figure 8 ) . This analysis confirmed no significant difference in expression and localisation of the 3 marker proteins prior to 52 h post infection . Subsequent to this , however , the PbSUB1-deficient parasites began to display clear differences in the levels or expression pattern of the marker proteins , culminating in a nearly complete absence of formation of daughter merozoites . Merozoite egress from infected hepatocytes is via extrusion of merosomes , vesicles filled with mature invasive merozoites . To address the consequences of PbSUB1 depletion specifically on merosome formation , hepatoma cell monolayers infected in vitro with condSUB1 or control UIS4/FlpL-F sporozoites were cultured to allow complete parasite development and then cell supernatants were repeatedly harvested between 62–70 h post infection . At these time points , the supernatants normally contain non-adherent rounded-up infected cells as well as merosomes ( Figure S9 in Text S1 ) . The detached cells were collected , fixed in suspension , gently centrifuged and processed for IFA , then counted . As shown in Figure S10 in Text S1 , a clear deficiency in formation of detached cells and merosomes was observed in the case of the GFP-positive ( excised ) condSUB1 parasites . In contrast , the fraction of condSUB1 parasites in which pbsub1 excision had not occurred ( and which therefore did not express GFP ) produced ∼10-fold more merosomes despite representing only ∼20% of the input condSUB1 parasite population , providing an internal control demonstrating that pbsub1 deletion rather than the presence of the modified pbsub1 locus was responsible for the defect in merosome formation . To exclude the possibility that the excised condSUB1 parasites might simply exhibit a delay in merosome formation , cultures were further monitored for up to 85 h post infection; however , even following such prolonged culture , no GFP-positive merosomes were observed in the condSUB1-infected hepatoma cell supernatants ( data not shown ) . Injection of the condSUB1 merosome preparations into naïve mice resulted in a blood-stage infection which contained only non-excised parasites ( data not shown ) , mirroring the bite-back data described in Figure 2 and Figure S7 in Text S1 . These results unambiguously confirmed that PbSUB1 is required for completion of liver stage development and formation of infectious liver stage merozoites .
Studying essential blood stage genes in Plasmodium liver stages requires regulatable genetic approaches . A tetracycline repressible transactivator has recently been shown to allow dynamic gene regulation in P . berghei blood stages in vivo and in liver stages in vitro [45] , but it remains to be tested whether it can be used successfully on essential liver stage genes . While the potential to use the same transgenic parasite for studying essential genes at different life cycle stages would clearly be an attraction of a tetracycline regulatable system , the sequence-specific Flp recombinase system applied here already provides a powerful tool to study essential genes specifically in the sporozoite and liver stage . However , precise placement of FRT sites has remained a major challenge . In the mouse , target sites for recombinases can often be designed to flank a critical exon excision of which results in effective gene disruption ( e . g . [46] ) . In malaria parasites , in contrast , where genes are relatively short and only around half have introns , flanking the entire protein coding sequence of a gene with FRT sites would be a good default strategy . However , the high AT content and repetitive nature of P . berghei genomic DNA has until now made it impractical to construct such genetic modification vectors for larger genes , since long genomic fragments are unstable in conventional circular high-copy plasmids in E . coli . Matters are further complicated by the observation that FRT sites positioned within 100 bp upstream of a start codon can interfere with gene expression in P . berghei [30] . As a consequence , recent studies have resorted to excising only the 3′ regulatory sequence of a gene [30] , [41] , [43] , resulting in gene silencing due to destabilisation of the mRNA , rather than achieving a complete gene knock out . Whilst this approach can work well , it is not uniformly successful due to the potential for cryptic polyadenylation sites downstream of the modified gene that act to stabilise the mRNA [47] , [48] . Furthermore , it is a drawback of this strategy that it sacrifices the key advantage of recombinases in providing the very tight regulation that stems from complete removal of a target gene . We here have demonstrated that a large genomic insert from a P . berghei genomic DNA library in a low copy linear pJAZZ vector can be manipulated successfully within E . coli using sequence-specific and lambda Red/ET recombinases . This approach , which is well established for generating conditional knock out alleles for the mouse ( e . g . [46] ) , allows FRT sites to be placed almost at will and without the need to manipulate Plasmodium DNA by conventional restriction/ligation cloning . We also generated some of the PCR templates and a Gateway donor cassette that can form part of a more generic strategy for turning large gDNA inserts into complex conditional knock out alleles for P . berghei genes . In the current study , to ensure positioning of the upstream FRT site well away from potentially important flanking promoter elements we chose a position ∼2 . 3 kb or ∼1 . 8 kb away from the pbsub1 ATG start codon , distances substantially larger than most Plasmodium promoter sequences mapped to date . We also chose to integrate a strong promoter 5′ to the FRT site in the upstream intergenic region of the target gene . This successfully allowed us to use expression of GFP to monitor excision of the pbsub1 gene in individual liver stage parasites . As a result , in our microscopic analyses of the pbsub1 knock out phenotype we were able throughout to distinguish excised parasites clearly from the minority of parasites in which excision had failed . While this strategy proved useful in the current study , where a large upstream intergenic region was available , for other genes integrating a long promoter sequence may interfere in unpredictable ways with expression either of the target gene itself , or of a neighbouring gene . In such cases it will be possible to omit the hsp70 promoter from the PCR amplicon in Step 1 , leaving behind only the 34 bp FRT site after Step 2 . Importantly , with the tools described here , constructs similar to that described can be produced and quality-control evaluated within as little as 16 working days . The robustness and speed of recombinase mediated engineering means it can be carried out in continuous liquid culture on 96 well plates . Insertion of an upstream FRT site in Steps 1 and 2 in such an optimised protocol could probably be completed within a week . Steps 3 and 4 can be adapted to use our existing pipeline for Red/ET mediated engineering on 96-well plates [32] . Such an optimised recombinase based strategy would greatly ease the generation of complicated conditional alleles and the study of essential genes in liver stages . Excision of the flirted pbsub1 gene , as indicated by expression of the GFP reporter in our transgenic parasites , occurred efficiently at the oocyst stage despite expression of FlpL under control of the uis4 ( maximally upregulated in infectious sporozoites ) promoter . We were not overly surprised by this observation , since although this promoter is maximally active in salivary gland sporozoites [49] we are not aware of any previous evidence that it is entirely ‘off’ in oocysts . It is important to note that GFP expression in an oocyst does not imply that all the resident sporozoites have undergone excision , only that some have done so . On the other hand it is also conceivable that - given the mode of parasite replication in oocysts , in which sporozoites bud from a syncytial sporoblast containing multiple nuclei [50] - GFP produced by excised parasites could be incorporated into the cytosol of non-excised sporozoites developing within the same oocyst . This could lead to a degree of over-estimation of the excision rate in our study , which may explain the apparent small mismatch between the proportions of GFP-positive ( excised ) input sporozoites and GFP-negative ( non-excised ) EEFs observed in some of the in vitro hepatocyte infection experiments ( e . g . Figure 4 ) . Importantly , the stage specificity of the uis4 promoter in this system is supported by the fact that the transgenic condSUB1 and condSUB1short parasites showed no growth defect in the asexual blood stages , and no GFP expression was observed in those stages ( although this does not rule out low level ‘leaky’ FlpL expression since excision of pbsub1 in asexual blood stages would likely produce non-viable parasites ) . Our finding that hepatocyte invasion , and the early stages of liver stage growth were all normal in the PbSUB1-deficient condSUB1 parasites confirms that PbSUB1 does not play an important role in these phases of the parasite life cycle; indeed , an effect on hepatocyte invasion was not expected anyway since PbSUB1 expression was undetectable in salivary gland sporozoites , either using our polyclonal anti-PbSUB1 antibodies or by epitope-tagging . In contrast , investigation of more mature liver stages of the PbSUB1-deficient parasites revealed a clear defect in merozoite formation . Unexpectedly , this was associated with an apparent absence of expression of the glycolipid-anchored major plasma membrane protein MSP1 . MSP1 is an established SUB1 substrate , but our previous studies in blood stages [20] , [22] have demonstrated that SUB1-mediated proteolysis of MSP1 requires discharge of SUB1 from exonemes , consistent with the topology of MSP1 on the merozoite surface where it is effectively a component of the PV lumen . We therefore had no a priori reason to predict that an absence of SUB1 should affect MSP1 expression or trafficking . Similarly unexpected was the observation that the MSP1 expression defect in PbSUB1-deficient parasites , as well as a small but significant effect on the size of the intracellular EEFs , was evident well before the point at which expression of PbSUB1 was detectable by IFA . We interpret this result as indicating that PbSUB1 plays an important role in EEF development before significant accumulation of the protease in exonemes . It is conceivable that an absence of SUB1 impacts on EEF exoneme biogenesis , or even that SUB1 plays a general processing role in parasite protein trafficking , analogous to that of certain members of the subtilisin-like prohormone convertase family [51] . Further work will be required to explore this possibility . Whether these additional roles for SUB1 also operate in blood stages will require the application of a conditional expression strategy suitable for use in Plasmodium blood stages , such as the recently-described DiCre system [48] , and this work is underway . MSP1 has previously been shown to be important for merozoite development [30] . It is perhaps unsurprising then , that a block in merosome formation was evident in the PbSUB1-deficient parasites; this is likely a direct result of the defect in merozoite formation . It was also accompanied by a block in PVM rupture . In blood stages , members of the SERA family have been implicated in egress . Many or most blood stage SERA family members are substrates of SUB1 [20] , [25] , [38] , and moreover at least one member of the SERA protein family in P . berghei , PbSERA3 , undergoes proteolytic processing in liver stages [11] . It is therefore possible that the observed defects in PVM rupture and egress are due to an absence of PbSUB1-mediated processing of PbSERA3 and/or other SERA proteins . Of the two other liver stage parasite proteins that have previously been implicated in egress using gene disruption approaches - liver-specific protein 1 ( LISP1; [52] ) and the parasite cyclic GMP-dependent protein kinase ( PKG; [31] ) – the latter has recently been shown to play a key role in regulating discharge of SUB1 into the PV in blood stage schizonts [22] . Given our definitive evidence here for expression of SUB1 in liver stages and its essential role , it is tempting to speculate that the egress defect observed in the PKG knockout reported by Falae et al . [31] is at least in part due to a resulting block in SUB1 discharge . Future work will focus on the fate of SUB1 in PKG knockout EEFs . In conclusion , we have combined cutting-edge molecular tools with a conditional gene deletion strategy to obtain the first complete conditional deletion of a liver stage Plasmodium gene . The molecular strategies described here will render stage-specific regulation of gene expression in Plasmodium more accessible to the research community . Despite the absence of associated pathology , the liver stage of the malaria parasite life cycle acts as an important amplification step in the infection pathway and so has long been considered an attractive target for vaccine or drug-mediated prophylactic approaches to disease control . Our results show that inhibitors of SUB1 could be used in prophylactic approaches to control or block the pre-erythrocytic stage of the malaria parasite life cycle .
Research using animals was approved by the Ethical Review Committee of the Wellcome Trust Sanger Institute and was conducted in accordance with the UK Animals ( Scientific Procedures ) Act 1986 , under licence number PPL 80/2158 issued by the UK Home Office . Tuck-Ordinary outbred mice and C57BL/6N inbred mice were used as 6–8 week old females . All animal procedures were carried out in accordance with a valid UK Home Office project licence . Anopheles stephensi strain SD500 mosquitoes were allowed to feed on mice 3–4 days after injection of infected blood , then maintained on fructose at 20°C . Sporozoite numbers were determined from day 21 by homogenising dissected salivary glands and counting released sporozoites using a haemocytometer . For conditional gene disruption experiments , mosquitoes were placed at 25°C from day 17–18 post infection and sporozoite isolation was performed from day 26 post infection . Parasite transfection experiments used the GFP-expressing P . berghei ANKA clone 507m6cl1 [40] , [53] ( kindly provided by Chris Janse and Shahid Khan , University of Leiden ) for epitope-tagging of pbsub1 , and the P . berghei NK65 UIS4-FlpL deleter clone [30] ( a kind gift of Robert Menard , Institut Pasteur , Paris ) for generation of the condSUB1 clones . The P . berghei NK65 UIS4-FlpL-F clone [41] , which constitutively expresses GFP under the control of the P . berghei hsp70 promoter , was used as a control for experiments with the condSUB1 clones; prior to isolation of control UIS4-FlpL-F sporozoites for hepatocyte infection experiments , mosquitoes infected with this line were also subjected to a temperature shift as described above for the condSUB1 parasites . Transfection of targeting constructs followed standard methodology [40] . Transgenic parasites were selected using pyrimethamine and cloned by limiting dilution . DNA encoding the predicted catalytic domain ( residues Ser196-Asn599 ) of PbSUB1 ( PBANKA_110710 ) was amplified by PCR using primers F_CatDPbSUB1synth_BamHI and R_CatDPbSUB1synth_XhoI ( Table S1 ) and cloned into the bacterial expression vector pGEX-His ( a kind gift of Dominique Soldati-Favre , University of Geneva , Switzerland ) for expression as a recombinant hexahistidine-tagged protein in E . coli BL21-Gold DE3 cells ( Stratagene ) . Recombinant product was purified by nickel chelate chromatography and used to immunise a rabbit . Before use , sera were adsorbed against E . coli acetone powder ( 40 mg/ml serum ) to deplete antibodies against bacterial proteins . Other primary antibodies used were: the anti-HA . 11 mouse mAb 16B12 ( Covance ) ; the anti-HA rat mAb 3F10 ( Roche ) ; the P . berghei MSP1-specific mAb 25 . 1 [54] ( a gift from Tony Holder , NIMR , London , UK ) ; a polyclonal antiserum against the PVM protein EXP1 [55] , [56] ( a kind gift from Volker Heussler , University of Bern , Switzerland ) ; a polyclonal anti-HSP70 mouse antibody ( a gift from Kai Matuschewski , Max Planck Institute , Berlin , Germany ) ; a polyclonal antibody specific for PbSERA3 , raised against a recombinant fragment of PbSERA3 called PbS3C1 [15]; and chicken and rabbit polyclonal anti-GFP sera ( Abcam ) . Western blot analysis of P . berghei schizont SDS extracts and IFA analysis of paraformaldehyde-fixed , permeabilized blood stage parasites were performed as described previously [15] , [57] . For IFA of parasitised hepatoma cells , infected cells were fixed for 15 min in 3% paraformaldehyde in phosphate-buffered saline ( PBS ) , then quenched in 50 mM ammonium chloride in PBS . Samples were permeabilized with 0 . 1% ( v/v ) Triton X-100 , washed , then incubated for 1 h in blocking solution ( 2% ( w/v ) BSA in PBS ) . Samples were probed with relevant primary antibodies diluted in blocking solution for 1 h , then with appropriate secondary antibodies before counterstaining with DAPI and observation using a laser scanning confocal microscope ( LSM510 , Zeiss ) . Primary antibodies were used for IFA at dilutions ranging from 1∶400–1∶1000 . Secondary antibodies ( usually used at a 1∶1000 dilution ) were Alexa Fluor 488 , 555 or 633-conjugated antibodies against mouse , rabbit or chicken IgG ( Invitrogen ) . A 1 , 248 bp sequence corresponding to the 3′ coding sequence of the pbsub1 gene was amplified from P . berghei gDNA using primers F_KI_XhoI and R_KI_ApaI ( Table S1 ) and cloned into the transfection vector pSD278-HA ( a kind gift of Dominique Soldati-Favre , University of Geneva , Switzerland ) , to produce an in-frame fusion to a single HA epitope tag , followed by the pbdhfr-ts 3′ UTR and a hDHFR drug selection cassette . Before transfection , the plasmid was linearised at a unique Hind III restriction site that lies 682 bp upstream of the stop codon of the pbsub1 gene . To construct a conditional deletion vector for pbsub1 we first identified a clone , PbG01-2474a09 , from an arrayed and end-sequenced library of P . berghei ANKA cl15cy1 genomic DNA in E . coli [32] that carried pbsub1 ( PBANKA_110710 ) and flanking genes on a 9 . 6 kb insert in a low copy linear plasmid [58] . This clone served as starting point for engineering an allelic exchange vector using a combination of site specific and lambda phage recombinases in four steps ( Figure S11 in Text S1 ) , which used protocols essentially as described [59] . Each intermediate product was fully sequenced before moving on to the next stage . For Step 1 ( Figure S11 in Text S1 ) we used lambda Red/ET mediated recombination with zeocin selection to insert a PCR amplicon ∼2 . 3 kb or ∼1 . 8 kb upstream of the pbsub1 start codon that comprised an hsp70 promoter followed by a zeo-pheS cassette for positive and negative selection in E . coli . The zeo-pheS cassette was flanked by two FRT sites in the same orientation and its insertion point was still 800 bp away from the start codon of the upstream gene . Primers used were F_Step1_rec and R_Step1_rec or F_Step1short_rec and R_Step1short_rec ( Table S1 ) and plasmid pColE1 5′hsp70-ATG-FRT-zeo-pheS-FRT served as a template ( see Supplemental Methods ) . Multiple stop codons were present downstream of the inserted sequence . In Step 2 , the FRTed zeo-pheS cassette was excised by inducing Flp-e recombinase expression in E . coli under negative selection against pheS , leaving behind only the hsp70 promoter and one FRT site in the upstream intergenic region . In Step 3 , Red/ET mediated recombination was used under zeocin selection to insert immediately upstream of the pbsub1 stop codon a DNA fragment that introduced a single HA epitope tag followed by a generic 3′ UTR from the pbdhfr-ts gene as well as a zeo-pheS cassette flanked by attR recognition sites for Gateway clonase . The DNA fragment used in this step was release by a Hind III digest from plasmid pColE1 sub1-HA-attR1-zeo-pheS-attR2-3′sub1 ( see Supplemental Methods ) . Finally , the product of Step 3 was subjected to an in vitro Gateway reaction under negative selection against pheS , which replaced the bacterial selection cassette with an FRT site immediately followed by a GFP coding sequence , an hsp70 terminator sequence and an expression cassette for hdhfr-yfcu for selection in P . berghei . In the final construct , FRT sites were positioned such that excision of pbsub1 would bring an ATG start codon 5′ of the upstream FRT site in frame with the gfp coding sequence , allowing expression of the fluorescent marker protein from the hsp70 promoter . The construct was tested by Flp-e activation in E . coli TSA cells , followed by PCR genotyping of the excised vector . See Supplemental Methods for more details on vector generation . Genomic DNA was extracted from blood stage parasites , infected mosquito midguts or infected salivary glands . For Southern blot analysis , the DNA was digested with suitable restriction enzymes and separated by gel electrophoresis . Transfer of the DNA to a nitrocellulose membrane and hybridisation with gene-specific probes was performed according to standard procedures . Probes were labelled with α-[32P] adenosine triphosphate ( Amersham Biosciences ) by random priming using a Prime-It Random Prime kit ( Stratagene ) . To detect integration of the transfected pPbSUB1-HA construct , primers Fprom_PbSUB1 , F2_PbSUB1and R_HA were used . The size of the expected PCR products - which could only be produced if integration occurred as predicted - were 1 , 942 bp and 1 , 288 bp respectively . Two other control PCRs were generated using primers F1_PbSUB1 , Fprom_PbSUB1 , R1_3′utr . These were expected to produce 1 , 937 bp and 2 , 036 bp products only from the unmodified wild type pbsub1 locus . For Southern blot analysis , parasite genomic DNA was digested with Pml I and Nhe I . An 841 bp probe annealing to the 5′ flanking sequence of pbsub1 was generated with primers F_prom_probe and R_prom_probe . This allowed detection of a 4 . 6 kb band for the wild type pbsub1 locus and a 10 . 6 kb band for the expected integration event . To detect integration of the pJazz-FRTed-pbsub1 constructs , primer F_selection ( forward ) was used with reverse primers R_ext1 , R_ext2 , or R_ext3 to produce PCR products of 1 , 471 bp , 2 , 034 bp and 2 , 617 bp respectively . As control PCRs for detection of the unmodified pbsub1 locus primers F2_PbSUB1 or F3_PbSUB1 were used together with R1_3′utr . These were predicted to produce products of 1 , 382 bp and 598 bp only from the unmodified pbsub1 locus . For Southern blot analysis , parasite genomic DNA was digested using StuI and NciI , and a 1 , 246 bp probe annealing to the pbsub1 gene was generated with primers F_KI_XhoI and F_KI_ApaI . This allowed detection of a 3 kb band for the unmodified pbsub1 locus and an 8 . 4 kb band for P . berghei condSUB1 and condSUB1short parasites . Intact P . berghei chromosomes were separated by pulsed-field gel electrophoresis on 0 . 8% agarose gels as described previously [32] . Gels were blotted and hybridised using the standard Southern blot protocol . For detection of integration in the correct chromosome a 452 bp probe hybridising to the pbdhfr-ts 3′ UTR was generated with primers F_3′utr_pbdhfr_probe and R_3′utr_pbdhfr_probe ( Table S1 ) . Hepa1-6 mouse or HepG2 human hepatoma cells were cultured in Dulbecco's Modified Eagle Medium high glucose ( DMEM; Gibco/Invitrogen ) plus 10% ( v/v ) fetal calf serum ( Invitrogen ) . For sporozoite infection rate assays and determination of parasite size at 28 and 48 h post infection , cells were seeded in 96-well culture plates ( 10 , 000–15 , 000 cells per well ) . The next day , 20 , 000 or 50 , 000 control or condSUB1 sporozoites were added to each well in complete medium containing 1% penicillin/streptomycin . Following incubation for 2–48 h , cells were washed four times with PBS then fixed and processed for IFA . For differential extracellular/intracellular staining ( in/out assay ) , staining was performed as described previously [44] using a Cy3 conjugated mouse anti-CS antibody , a rabbit Alexa Fluor 488 anti-GFP antibody ( Invitrogen ) and Hoechst 33342 ( Molecular Probes/Invitrogen ) . For the in/out assay and the determination of parasite area at 28 h and 48 h post invasion , images of fixed , stained wells were automatically acquired with a Cellomics ArrayScan VTI HCS Reader ( 20× magnification; 100 or 155 fields per well of a 96-well plate ) and analysed using the Colocalization BioApplication software ( ThermoFisher ) . For the in/out assay , object recognition was based on the GFP staining and average intensity of the CSP-specific signal was used to distinguish extracellular ( high ) and intracellular ( low ) sporozoites . The number of cell nuclei was determined based on the Hoechst signal and used to calculate invasion rates . For the parasite size assay , number of cell nuclei and number and area of parasites were determined . Object recognition was based on the HSP70-specific staining , applying a minimum object size of 15 µm2 at 28 h post invasion and 50 µm2 at 48 h post invasion . The average intensity of the GFP staining was used to discriminate excised and non-excised condSUB1 parasites . The cell nuclei were counted with help of the Hoechst signal and numbers used to calculate infection rates . For each comparison , Student's t-test was performed and for the invasion rates the threshold of the p value was set to 0 . 05 , whereas for parasite sizes a Bonferroni corrected threshold of the p value was chosen at 0 . 0167 ( * ) or 0 . 0033 ( ** ) . To assess release of merosomes and detached cells in hepatoma cultures , 40 , 000–80 , 000 cells were seeded into 24-well plates one day before sporozoites were added at a multiplicity of infection of 1 . Merosomes were collected from the culture supernatants between 65 and 69 h post infection , fixed in suspension , pelleted , resuspended in a small volume of medium , fixed onto poly-L-lysine coated microscope slides ( Menzel-Gläser ) , and stained with anti-GFP , anti-MSP1 , anti-EXP1 antibodies and DAPI before analysis by fluorescence microscopy . C57BL/6N mice were injected intravenously with 20 , 000 P . berghei sporozoites and livers were collected at 40 h post infection . Livers were mechanically homogenized on ice with a Tissue Tearor ( IKA Ultra Turrax T-10 ) in 4 ml denaturing solution ( 4 M guanidine thiocyanate , 25 mM sodium citrate pH 7 , 0 . 5% N-Laurosyl-sarcosine , 0 . 1% β-mercaptoethanol ) total RNA extracted using an RNeasy Mini Kit ( Qiagen ) . Samples were treated with Turbo DNAse ( Ambion ) according to the manufacturer's instructions . One microgram of total RNA was reverse-transcribed using a Transcriptor First Strand cDNA Synthesis kit ( Roche ) . Parasite 18S ribosomal RNA and mouse hypoxanthine guanine phosphoribosyltransferase ( HPRT ) cDNAs obtained from the reaction were quantified by real-time quantitative fluorogenic PCR using previously described primers respectively F_Pb18S and R_Pb18S for P . berghei 18S ribosomal RNA , and F_HPRT and R_HPRT for the Mus musculus housekeeping gene HPRT gene . To quantify gene expression , Power SYBR Green PCR Master Mix ( Applied Biosystems ) was used according to the manufacturer's instructions . The reaction was performed in an ABI Prism 7000 sequence Detection System ( Applied Biosystems ) with 2 µl of cDNA in a total volume of 25 µl and the following reaction conditions: 1 cycle of 2 min at 50°C , 1 cycle of 10 min at 95°C , 50 cycles of 15 sec at 95°C and 1 min at 60°C . Each sample was assayed in triplicate . Relative amounts of RNA were calculated using the ABI Prism 7000 SDS 1 . 2 . 3 Software , and normalised against expression levels of the mouse HPRT mRNA . | Malaria is caused by a single-celled parasite and is transmitted by the bite of an infected mosquito . The inoculated sporozoite forms of the parasite invade liver cells where they replicate , eventually releasing thousands of merozoites into the bloodstream to initiate the blood stage parasite life cycle which causes clinical malaria . The liver stage of the parasite life cycle is asymptomatic , so it is widely considered a potential target for prophylactic vaccine- or drug-based approaches designed to prevent infection . In this study , we use a robust , highly efficient gene engineering approach called recombineering , combined with a conditional gene deletion strategy to examine the function in liver stages of a parasite protease called SUB1 , previously implicated in release of blood stage parasites . We show that SUB1 is expressed in the liver stage schizont and that the protease is essential for production of liver stage merozoites . Our results enhance our understanding of malarial liver stage biology , provide new tools for studying essential gene function in malaria , and suggest that inhibitors of SUB1 could be used as prophylactic drugs to prevent clinical malaria . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | The Malarial Serine Protease SUB1 Plays an Essential Role in Parasite Liver Stage Development |
Chromatin undergoes major remodeling around DNA double-strand breaks ( DSB ) to promote repair and DNA damage response ( DDR ) activation . We recently reported a high-resolution map of γH2AX around multiple breaks on the human genome , using a new cell-based DSB inducible system . In an attempt to further characterize the chromatin landscape induced around DSBs , we now report the profile of SMC3 , a subunit of the cohesin complex , previously characterized as required for repair by homologous recombination . We found that recruitment of cohesin is moderate and restricted to the immediate vicinity of DSBs in human cells . In addition , we show that cohesin controls γH2AX distribution within domains . Indeed , as we reported previously for transcription , cohesin binding antagonizes γH2AX spreading . Remarkably , depletion of cohesin leads to an increase of γH2AX at cohesin-bound genes , associated with a decrease in their expression level after DSB induction . We propose that , in agreement with their function in chromosome architecture , cohesin could also help to isolate active genes from some chromatin remodelling and modifications such as the ones that occur when a DSB is detected on the genome .
DNA packaging into chromatin hinders detection and repair of DNA Double Strand Breaks ( DSBs ) , and therefore DSB repair occurs simultaneously with multiple chromatin modifications , including histone acetylation , ubiquitylation and phosphorylation , as well as ATP dependant nucleosome remodelling and chromatin protein deposition or exclusion ( for review [1] , [2] ) . These chromatin changes not only generate a chromatin state permissive to DNA repair , but also contribute to DSB signalling and checkpoint activation . Phosphorylation of H2A in yeast or H2AX in mammals ( referred to γH2AX ) occurs rapidly , within a few minutes , and is considered to be one of the first DSB-induced chromatin modifications . While γH2AX is not required for the initial recruitment of repair proteins onto DSBs , it is necessary for the proper assembly of repair foci ( also called IRIF , for IRradiation Induced Foci ) and full activation of the DNA Damage Response ( DDR ) [3] , [4] . H2AX deficient mice are radio-sensitive and subject to increased genomic instability [5] , highlighting the critical function of γH2AX in vivo . Remarkably , γH2AX spreads across large chromatin domains surrounding DSBs , around 50 kb in yeast [6] and up to 2 Mb in vertebrate cells [7]–[11] . Until recently , the mechanism ( s ) underlying such wide spreading , as well as its consequences on chromatin activity and gene transcription were unclear . Indeed , several lines of evidence indicated that DSB generation triggers RNA Pol II and Pol I exclusion/pausing at break sites and inhibits transcription of proximal genes in an ATM dependent manner [12] , [13] . However , whether and how transcription was affected further distally from the break in γH2AX domains remained elusive [6] , [14] . Recently , we developed a stable human cell line , designed for controlled , sequence-specific DSB induction , based on the expression of an 8 bp restriction enzyme ( AsiSI ) fused to the oestrogen receptor ligand binding domain . Using this system , we monitored γH2AX distribution and changes in transcription , around more than 20 DSBs located on chromosomes 1 and 6 using ChIP-chip [10] . We uncovered that γH2AX spreads unevenly over megabases of surrounding chromatin , avoiding transcribed genes . Within γH2AX domains , we found that gene transcription remained unchanged upon DSB induction [10] . We suggested that the γH2AX profile reflects the spatial organisation of chromatin and proposed a 3-dimensional model , which accounts for the accurate maintenance of gene transcription proximal to DSBs via their exclusion outside of γH2AX foci . In addition to γH2AX , evidence suggests that cohesin plays a critical role in DSB repair ( for review [15] , [16] ) . Cohesin is a multi-subunit complex , thought to embrace DNA as a ring-shaped structure , that mediates sister chromatin cohesion and ensures accurate chromosome segregation . It consists of the proteins SCC1 ( also termed Rad21/Mcd1p ) , SCC3 ( SA1 and SA2 in human somatic cells ) and the heterodimer SMC3/SMC1 . In yeast , cohesin is recruited over a 50 kb chromatin domain surrounding an HO-induced break [17]–[19] . In vertebrate cells , cohesins are targeted to chromatin upon ionizing radiation [20] and to DSBs induced by X ray stripes and laser tracks during G2 [21]–[23] , although this may only occurs at very high power settings [22] . However , ChIP studies clearly showed that SMC1 and SCC1 are recruited to an I-SceI-induced DSB [24] , suggesting that loading of cohesin at DSBs also occurs in mammalian cells . Cohesin promotes equal homologous recombination between sister chromatids and prevents homologous recombination between repeats or homologous chromosomes [24]–[28] . In addition , its function in DSB repair depends upon cohesion establishment , a phenomena known as DIC ( Damage Induced Cohesion ) ( [29]–[32] for review [33] ) . This led to the proposal that cohesin may participate in post-replicative DNA repair by ensuring proper cohesion between sister chromatids thus facilitating homologous recombination with the sister locus . Importantly , beyond its role in DSB repair and sister chromatid cohesion , another function for cohesin has recently emerged . In vertebrates , the cohesin complex accumulates at specific loci , mainly enhancer/promoters and sites bound by the CTCF insulator protein [34]–[36] . There , it participates in the transcriptional control of neighbouring genes , most likely through its ability to mediate long-range interactions between chromatin fibers , thereby allowing enhancer/promoter interaction and/or insulation from the surrounding chromatin [34]–[37] . More generally , cohesins are now believed to play a critical role in genome organization , participating in loop formation and thus affecting various DNA-based processes such as transcription and replication [38] . Given the multiple roles of cohesin in DSB repair , higher-order chromatin structure and transcriptional control , we decided to characterize the cohesin profile around AsiSI-induced DSBs in order to both further refine its function in DSB repair and its potential impact on γH2AX spreading . Here we show that , in contrast to yeast , cohesin is only moderately recruited to AsiSI-induced DSBs in human cells and does not spread over more than 5 kb . Remarkably , cohesin binding antagonizes γH2AX accumulation within γH2AX domains . Depletion of the SCC1 cohesin subunit leads to both an increase in γH2AX and a DSB-dependent transcriptional downregulation of genes within γH2AX domains , suggesting that cohesins are , at least in part , responsible for the accurate transcriptional control observed in γH2AX domains . Finally , we also analyzed the consequences of cohesin depletion on the positions of γH2AX domain boundaries , and found that while most of these boundaries remained unaffected , at some genomic locations cohesin helped to confine γH2AX spreading .
We recently developed a human cell line that stably expresses an AsiSI-ER fusion restriction enzyme ( the AsiSI-ER-U20S cell line ) . Treatment with hydroxytamoxifen ( 4OHT ) triggers nuclear localisation of the enzyme and induces DSBs at defined genomic loci , enabling ChIP analyses of protein recruitment at DSBs [10] . In order to better understand the function of cohesin in DSB repair , we thus performed ChIPs against various human cohesin subunits before and after break induction . The specificity of homemade antibodies was first confirmed using western blot , immunoprecipitation and ChIP assays on a known cohesin-binding site [35] ( Figure S1 and Figure S2 . ) . We found that 4OHT treatment induced the targeting of SMC3 , SCC1 and SCC3 ( SA1 and SA2 ) at AsiSI-induced DSBs ( Figure 1A , 1B , 1C respectively ) indicating that the full complex is likely to be recruited at DSBs . Since it was previously reported that cohesins may target DSBs preferentially in the G2 phase of the cell cycle [21] , we monitored SCC1 recruitment in G2 arrested AsiSI-ER-U20S cells following a RO-3306 treatment . We did not find a major difference in loading of SCC1 onto DSBs when compared with asynchronous cells ( Figure S3A ) . In addition , we also used the AsiSI-ER-T98G cell line [10] , [11] that can easily be synchronized by serum starvation , to monitor cohesin recruitment in G1 and G2 synchronized cells . Again , SCC1 DSB-targeting was similar in G1 and G2 ( Figure S3B ) . A ChIP performed at 14 hour after 4OHT treatment ensured that SCC1 recruitment did not change drastically at a later time point ( Figure S4 ) . We therefore decided to perform SMC3 ChIP-chip experiments in asynchronous cells , before and after 4 hours of 4OHT treatment , using human Affymetrix tiling arrays covering chromosomes 1 and 6 , in order to simultaneously investigate the distribution of cohesins around multiple DSBs with high resolution . On these two chromosomes , the SMC3 distribution in untreated AsiSI-ER-U20S cells was similar to the distribution of SCC1 reported for HeLa cells [35] ( see examples Figure S5A ) . 37 . 6% of SMC3 binding sites identified in AsiSI-ER-U20S were also identified using the SCC1 dataset from HeLa cells . Both SCC1 and SMC3 signals showed a clear enrichment at transcription start sites ( TSS ) ( Figure S5B ) , consistent with the fact that a significant proportion of cohesin binding sites are located in close proximity to promoters [34]–[36] , results which confirm the validity of our ChIP-chip data . Strikingly , we found that recruitment of SMC3 at DSBs induced by 4OHT treatment was moderate and did not spread widely around the DSB to form a γH2AX-like domain , but rather localized within close proximity to the break ( Figure 1D ) . When averaged around the 24 AsiSI-induced DSBs on chromosomes 1 and 6 ( [10]; see Table S1 for a list of AsiSI sites ) , the SMC3 profile showed a weak increase upon 4OHT addition over a ∼5 kb region surrounding the DSB ( Figure 1E ) . Although weak , we found that this increase of SMC3 after 4OHT treatment at the vicinity of AsiSI sites ( on a 2 kb window ) was significant ( p<0 . 05 ) ( Figure 1F and Figure S6 ) . In order to confirm that cohesin did not spread around DSBs in our cell line , we performed ChIP followed by Q-PCR analyses using primer pairs located at various positions from a DSB . Both SMC3 and SCC1 showed a clear increase upon 4OHT treatment at the immediate vicinity of the break , recruitment that was undetectable further away from the DSB ( Figure 1G ) . Importantly , several labs previously reported an extended recruitment of cohesin over 50 kb domains around a single HO-induced DSB in yeast [17]–[19] . Since in our cell line , 4OHT treatment induced over a hundred DSBs [11] , we wondered whether the lack of cohesin spreading observed here could be due to a limiting amount of free cohesin or/and available cohesin loaders , for targeting at DSBs . In order to address this point , we first controlled the amount of soluble cohesin ( unbound to chromatin ) in the nucleus before and after 4OHT treatment . Both SCC1 and SMC3 were still present in the soluble fraction after DSB induction ( Figure S7A ) , indicating that free cohesins are not a limiting factor in these conditions . In addition , we also performed a SCC1 ChIP in an I-SceI-ER U20S cell line , in which one single DSB is induced upon 4OHT treatment . As observed on AsiSI-induced DSBs , we could detect a 4OHT-dependant increase of SCC1 at the I-SceI-induced DSB ( 300 bp ) , but not at 2 . 4 kb from the DSB ( Figure S7B ) . Thus the high amount of DSBs induced by AsiSI over the human genome is not responsible for the lack of spreading observed in human cells . Altogether , our data indicate that in human cells , cohesin is moderately targeted to DSBs and that it does not spread over wide chromosomal domains in contrast to yeast . During the course of previous studies , we noticed that γH2AX within domains tended to decrease on cohesin peaks identified in HeLa cells [35] ( Figure S8 ) . Thus we next compared the cohesin distribution obtained in our AsiSI-ER U20S cells in absence of DSB induction , with our previously reported γH2AX profile . Within γH2AX domains , areas showing low levels of γH2AX ( “holes” ) often coincided with peaks of SMC3 monitored before 4OHT treatment ( Figure 2A ) . We retrieved the γH2AX peak/hole positions within domains ( see Material and Methods ) and averaged the profile of SMC3 across their borders . γH2AX peak/hole transition coincided with a change in the SMC3 profile ( Figure 2B ) . In addition , we also found that the genes showing high levels of SMC3 rather harbour low level of γH2AX ( Figure S9 ) . In order to confirm these data we also profiled SCC1 in our cell line under normal conditions . Again , the SCC1 distribution in AsiSI-ER U20S cells was similar to the profile characterized in HeLa cells ( Figure S10A–S10B ) , and 43% of the binding sites in AsiSI-ER U20S cells , overlapped with binding sites in HeLa cells . We found that , as observed with SMC3 , SCC1 peaks coincided with γH2AX holes , and that SCC1 rich genes showed low levels of γH2AX ( Figure S11 ) . Altogether these results suggest that the cohesin present onto chromatin before any DSB induction antagonizes γH2AX establishment/maintenance . To test this hypothesis we analysed by ChIP-chip the γH2AX profile upon SCC1 depletion by siRNA . Depletion of this subunit has been shown to also trigger an almost complete disappearance of SMC3 from chromatin [35] . SCC1 siRNA [35] was highly efficient since both RNA and protein levels were strongly reduced ( Figure S12A–S12B ) . In addition , chromatin-bound SCC1 was also efficiently depleted by siRNA as shown by ChIP ( Figure S12C ) . We observed that within domains , γH2AX signals increased in SCC1 depleted cells when compared to cells transfected with control siRNA ( Figure 3A left panel , Figure S13 upper and middle panels , and Figure S14A ) . This was also confirmed by Q-PCR analyses of γH2AX ChIP in control and SCC1 depleted cells ( Figure S15 ) , using primers pairs located at various positions from the DSB in five different γH2AX domains . This increase was not detected elsewhere on the genome indicating that it was not due to an effect of SCC1 depletion on basal levels of γH2AX ( Figure 3A right panel , Figure S13 lower panels and S14B ) . Our cleavage assay indicated that SCC1 depletion did not change the efficiency of AsiSI site cutting ( Figure S16 ) . Therefore , the enhanced phosphorylation of H2AX observed in SCC1 depleted cells was not due to an increase in AsiSI-ER activity , but rather to some modification ( s ) of the establishment or maintenance of γH2AX on chromatin . Importantly , we could also detect this increase by immunofluorescence ( Figure S17 ) , and changes in γH2AX levels upon SCC1 depletion have also been observed by western blot using irradiated cells [39] , which further support our findings . Furthermore , we found that the γH2AX increase observed upon SCC1 siRNA transfection occurred preferentially on cohesin-bound chromatin ( Figure 3B ) . The ratio of γH2AX in SCC1-depleted versus control cells , averaged over an 80 kb window around each of the 24 AsiSI sites , correlates with the level of both SMC3 and SCC1 averaged over the same window ( Figure 3C and Figure S18 ) . This strongly suggests that the effect of cohesin on γH2AX is direct and mediated in cis in chromatin , rather than due to a global increase of signalling and kinase activity within the cell . We next examined in more detail the behaviour of γH2AX in SCC1 depleted cells , more specifically on the genes contained within γH2AX domains . We reported previously a decrease in γH2AX signal at Transcriptional Start Sites ( TSS ) within γH2AX domains [10] . This decrease was practically undetectable in SCC1-deficient cells , when compared to siRNA control cells ( Figure 4A ) . Accordingly , in cells transfected with SCC1 siRNA we could observe a significant increase of γH2AX at promoters compared to control cells , whereas this increase was much less pronounced upstream or downstream TSS ( Figure S19 ) . This indicates that SCC1 depletion triggers an abnormal accumulation of γH2AX at TSS . We observed that this behaviour preferentially affects genes normally bound by cohesin ( Figure 4B ) . For each of the 359 genes embedded in γH2AX domains , we calculated the SMC3 signal and the ratio of γH2AX in siRNA SCC1/siRNA CTRL transfected cells . When plotted against each other we could see a significant correlation ( Figure 4C ) . The same was true when SCC1 signal was plotted ( Figure S20 ) . Along the same line , genes on which γH2AX increased the most after SCC1 depletion , significantly showed more SMC3 ( upper panel ) and SCC1 ( lower panel ) ( Figure S21 ) . This strongly suggests that the presence of cohesin prevents γH2AX spreading on genes . We confirmed these data by Q-PCR on selected SMC3-bound ( ARV1 , CTNNBIP1 , GNAI3 , ATXN7L2 , and AMIGO1 ) and two SMC3-unbound ( GBP5 and GBP6 ) genes ( Figure S22 ) . Transfection with SCC1 siRNA increased γH2AX levels up to twofold on the SMC3-bound genes but did not affect the SMC3-unbound regions ( Figure 4D ) . Altogether our data indicate that cohesin directly controls the accumulation of γH2AX on chromatin and at promoters . Gene transcription remains unaffected within AsiSI-induced γH2AX domains and active genes harbour low levels of γH2AX [10] . Since cohesin depletion led to an increase in γH2AX at cohesin-bound genes , we wondered whether transcription was still maintained after DSB induction in this cohesin-deficient context . We performed RT-QPCR for eight genes located within γH2AX domains , before and after break induction in control and SCC1 depleted cells . As expected , since cohesin plays a role in transcriptional regulation , SCC1 depletion affected the transcription of some of the tested genes , without DSB induction ( Figure S23 ) . As previously reported [10] , 4OHT treatment did not alter gene expression in SCC1-proficient cells ( CTRL siRNA ) . In contrast , gene expression decreased after 4OHT treatment in an SCC1 depleted background ( Figure 5 ) , indicating that cohesin helps to ensure normal gene expression in γH2AX domains after DSB induction . Finally , we examined the behaviour of γH2AX upon SCC1 depletion at γH2AX domain boundaries . Cohesin has been proposed to mediate long range interactions and to play a role in chromosome looping and 3-dimensional organisation . Thus , it appears as an intriguing candidate for restraining γH2AX spreading within defined chromosomal domains . Using γH2AX domain boundaries identified in control transfected cells ( Table S2 ) , we observed a wider spreading of γH2AX in SCC1 depleted cells than in control cells ( Figure 6A ) . However , when we looked individually at each AsiSI-induced γH2AX domain , we found that some domains appeared to be cohesin-independent while others showed extended spreading upon SCC1 depletion . This difference was not a consequence of elevated γH2AX within domains , since among domains that incurred a similar increase in γH2AX upon SCC1 depletion , some domains showed extended spreading ( Figure 6B top panel ) while others did not ( Figure 6B bottom panel ) . The extended spreading observed on this domain was further confirmed by QPCR analysis using primers pairs at various locations ( Figure 6C ) . One possibility is that cohesins are directly involved in a subclass of domain boundaries where they act to constrain spreading . However , we could not find a correlation between cohesin distribution and boundary positions either globally ( Figure S24A ) or individually ( Figure S24B ) . Thus , it is unlikely that cohesins are physically involved in defining the limits of γH2AX domains . Alternatively , the global increase of γH2AX that occurs upon SCC1 depletion could account for the extended spreading on chromatin ( such as Figure 6B top panel ) unless some other specific features constrain this spreading ( such as on the domain Figure 6B bottom panel ) .
Taking advantage of our recently described inducible system to generate sequence specific DSBs at multiple positions , we have investigated the recruitment of cohesin at DSBs in human cells . Consistent with previous reports [21]–[24] , we observed an increase of several cohesin subunits at break sites . As in yeast this recruitment likely depends on H2AX phosphorylation , since significant decrease in SMC3 and SCC1 targeting was observed when using an ATM inhibitor ( [20] and our unpublished data ) . However , we found that cohesin recruitment was very moderate and restricted to the immediate vicinity of the DSB which is in stark contrast to the 50-kb wide cohesin loading that occurs in yeast around HO-induced DSBs [17]–[19] . Importantly , since in our system doing ChIP after 4H of 4OHT treatment allows studying all recruitment events that occur at a DSB between 0H and 4H of repair ( as once in the nucleus the enzyme cuts and re-cuts the site ) , this difference is unlikely to be due to a difference in the kinetics of cohesin recruitment at DSBs . We also performed cohesin ChIP at 14H post-break induction , in order to make sure that in human cells cohesin targeting does not occurs at very late time point ( Figure S4 ) . In addition , we also controlled that such a restricted cohesin recruitment was not due to the high amount of DSBs induced in our cell line . We showed that soluble cohesins were not limiting after DSB induction and that a similar cohesin recruitment pattern was also observed in an I-SceI cell line ( single cut ) ( Figure S7 ) . Thus , altogether , our data show that cohesins are only recruited to the vicinity of a DSB in human cells contrarily to the extended cohesin spreading observed in yeast . Accumulation of cohesin around DSBs has been proposed to enhance cohesion between sister chromatids in order to promote efficient repair by homologous recombination ( HR ) ( for review [33] ) . While HR accounts for the majority of repair events in yeast , DSBs are mainly repaired by Non Homologous End Joining events in mammalian cells , even during G2 phase [40] . This could thus account for the difference of cohesin spreading observed between yeast and mammalian cells . Several additional differences exist in the behaviour of cohesin complexes between yeast and metazoan . For example , yeast cohesins have been proposed to translocate along chromatin fibers , eventually accumulating at sites of convergent transcription [41] . In contrast , Drosophila and mammalian cohesins do not show any preference for convergent genes and accumulate at promoters and CTCF binding sites [34]–[36] , [42] . These differences in cohesin distribution may reflect basic differences in the organization of yeast and metazoan genomes , the former being smaller and more compact , with a higher density of transcribing genes . They might also be indicative of different cohesin targeting mechanisms , which could also partake in the different localizations observed at DSBs . Finally , the absence of cohesin spreading in human cells may be compensated for by post-translational modifications that increase cohesion . Acetylation and phosphorylation of cohesin subunits at various residues are suspected to play critical roles in regulating the ATPase and translocase activity , as well as the cohesion properties of the cohesin complex ( for review [15] ) . Thus , follow up investigations into the distribution of cohesin modifications upon DSB induction may reveal the molecular basis for the observed differences in localization between yeast and human cells . More specifically , residues 966 and 957 of SMC1 , which are phosphorylated by ATM in response to damage [43]–[46] , are not conserved in yeast and it is thus tempting to speculate that they could act to promote cohesion using preloaded cohesins in mammalian cells . We found that depletion of cohesin leads to a global increase of γH2AX after DSB induction ( both using ChIP and immunofluorescence ) , in agreement with reports of γH2AX increase in irradiated , SCC1- and SMC3-depleted cells [39] . While this increase was moderate , it was reproducible and observed at several γH2AX domains ( Figure S15 ) . Our data indicate that the removal of cohesin from chromatin triggers an accumulation of γH2AX in cis , since this increase is found preferentially on regions normally enriched in cohesin . One hypothesis is that cohesin inhibits the establishment of H2AX phosphorylation , for example by counteracting ATM activation or/and recruitment . Alternatively , the increase in γH2AX upon SCC1 depletion could reflect impairment in the recruitment of phosphatases at breaks , such as PP2A [47] . It is interesting to note that Sugoshin , a protein that interacts with the cohesin complex and regulates cohesion in mitosis and meiosis , also interacts with PP2A [48]–[50] . One could thus envisage that cohesin recruits PP2A to chromatin and thereby regulates γH2AX levels . Both Pol II and Pol I transcription are down regulated in the vicinity of a DSB in an ATM-dependent manner [10] , [12] , [13] . Whether this extinction is induced by γH2AX is not clear , since inhibition of Pol I is independant of H2AX [12] and inhibition of Pol II at least in yeast , appears to be dependent on resection rather than on γH2AX spreading [6] . We recently reported that the transcription of genes within γH2AX domains , but further away from a break , remains unchanged and that these active genes harbour reduced levels of γH2AX [10] . Here we found that this maintenance of transcription in γH2AX domains is impaired upon cohesin depletion . First we observed a moderate but general increase of γH2AX levels on the cohesin-target genes encompassed in γH2AX domains after cohesin depletion , indicating that cohesins contribute to maintain reduced γH2AX level on genes . Secondly , for eight genes located in various domains , this was associated with a significant DSB-dependant transcriptional decrease . Since the effect of cohesin depletion on γH2AX levels occurred on most genes of the domains , it is likely that the trend observed on these eight genes is a general feature illustrating the role of cohesin in transcriptional maintenance , although further genome wide studies would be required to generalize our findings . It is also important to underline that both γH2AX increase on genes and DSB-dependant transcriptional decrease in cohesin depleted cells were quite moderate , and thus , while this could be to due siRNA efficiency , or to an asynchronous cleavage of AsiSI sites in the cell population , we also cannot exclude that other unrelated factor participate in the protection of active genes in γH2AX domains . This cohesin-dependant gene protection is unlikely to be a damaged–induced process since cohesin recruitment at DSB only occurs on the surrounding 2kb . Instead we favour the hypothesis that the cohesins already present on a normal , undamaged genome could protect active genes from the chromatin changes induced by DSBs , such as γH2AX which has been proposed to enhance chromatin compaction [51] and could therefore be deleterious for transcription . Interestingly , many recent studies have established a clear link between the ability of cohesin to regulate transcription and its ability to mediate chromosome looping . It is thus tempting to speculate that cohesin could protect transcription in γH2AX domains , by maintaining transcribed loci outside of γH2AX foci . This would allow to both keep low levels of γH2AX on active genes and to ensure their correct transcription post DSB induction ( Figure 7 ) . Since cohesins are known to mediate chromatin looping , they could also be involved in anchoring the chromosomal domain , within which γH2AX would spread . While we found that cohesin depletion triggers boundary expansion at some domains , we could not find a corresponding enrichment in cohesin at those positions ( Figure S24 ) . Thus , it is unlikely that cohesin plays a direct role in anchoring γH2AX domains . We believe that the global increase in γH2AX levels that occurs in the absence of cohesin , leads to extended spreading farther away from the break unless some specific constraints counteract γH2AX propagation . In order to get insights into the nature of these potential constraints , we have compared our data with the recently published Hi-C mapping of long range chromosomal interactions [52] , which identified the positions of chromosomal domains , amongst other features . Remarkably , a significant proportion of γH2AX domain boundaries correlated with chromosomal domain transitions ( Figures S25 and S26 ) . In conclusion , we believe that γH2AX spreads around DSBs until it naturally fades away or it encounters a chromosomal domain transition . Fading is likely dependent on factors such as the distance from the break and the intensity of γH2AX induction , and thus cohesin depletion would trigger extended γH2AX spreading due to higher levels of γH2AX . In contrast , chromosomal domain transition stops propagation regardless of γH2AX levels , and it is unlikely that cohesins are involved in these domain transitions , since these boundaries were intact upon SCC1 depletion ( not shown ) . In summary , our results suggest that phosphorylation of H2AX after DSB is established on a pre-existing chromatin/chromosomal organization ( Figure 7 ) . While further investigations are required to validate such a hypothesis , it is interesting to point out that if true , γH2AX spreading might thus be used as read-out of 3-dimensional chromosome structure .
Rabbit polyclonal antibodies against SMC3 were raised using recombinant SMC3 ( αSMC3-A ) or an SMC3 peptide ( αSMC3-B ) and have been described in [53] . They were further tested and validated in human cells in [38] , and in the present manuscript ( Figure S1 ) . The rabbit polyclonal antibody against SA1 was raised using a C-terminal peptide as immunogen ( CEDDSGFGMPMF ) and has been validated by ChIP in mouse cells ( Remeseiro et al . , submitted ) , and in human cells ( this manuscript , Figure S2 ) . The rabbit polyclonal antibody against SA2 was made against a peptide within the C-terminal region of hSA2 “EPKRLRPEDSFMSV” , and affinity purified against the antigen . This antibody was validated against human proteins in Figure S2 . AsiSI-ER-U20S and I-SceI-ER-U20S cells were cultured in Dulbecco's modified Eagle's medium ( DMEM ) supplemented with antibiotics , 10% FCS ( Invitrogen ) and 1 µg/mL puromycin at 37°C under a humidified atmosphere with 5% CO2 . AsiSI-ER-T98G cells were cultured in Minimum Essential Media ( MEM ) GlutaMAX , supplemented with MEM Non Essential Amino Acid ( NEAA ) , antibiotics , and 10% FCS ( Invitrogen ) . Synchronization of AsiSI-ER-T98G cells was achieved by 72 hours of serum starvation ( 0% FBS ) . Cells were collected in G1 and G2 phase after 10H and 28H , respectively , of 20% FBS re-induction . Synchronisation of AsiSI-ER-U20S in G2 was achieved by an 18H R0-3306 ( Calbiochem ) 9 µM treatment . For siRNA transfection , 5 . 0×106 cells were electroporated with 10 µL of 100 µM siRNA using the Cell Line Nucleofector kit V ( Amaxa ) , according to the manufacturer instructions , and collected 48H after transfection . Sequences for siRNA are displayed Table S3 . When indicated , cells were treated with 300 nM 4OHT for 4H or 14H . RNA was extracted using the RNAeasy kit ( Qiagen ) following manufacturer instructions . 1 µg of RNA was reverse transcribed using Im-PromII RT ( Promega ) with random hexamers . cDNAs were analyzed by Q-PCR using primers described in Table S3 and normalized against P0 cDNA levels . Cell pellets ( 5 . 106 cells ) were fractionated as reported [54] . Briefly , cells were first resuspended for 15 min on ice in 200 µl of 50 mM HEPES pH 7 . 5 , 150 mM NaCl , 1 mM EDTA supplemented with the mini protease ( Roche ) and phosphatase inhibitor cocktail ( Sigma ) . Following centrifugation at 14000 rpm for 5 min , the supernatant was collected ( fraction I ) , and pellets were incubated in 200 µl of the same buffer supplemented with 0 . 1% triton for 15 min at 4°C . The supernatant was collected as before ( fraction II ) . The pellets were further extracted in 200 µL of the same buffer supplemented with 0 . 1 mg/mL RNAse ( Abcam ) for 30 min on ice . The extracts were clarified by centrifugation at 14 000 rpm for 5 min ( fraction III ) . Pellets were next resuspended in 200 µl extraction buffer supplemented with 10 nM MnCl2 and 0 . 07 mg/mL DNAse1 for 30 min at room temperature Western blot were performed using Invitrogen precast gels and buffer following manufacturer instructions , and using an anti-SMC3 ( αSMC3 A ) , an anti-SCC1 ( Ab992 rabbit ) , or an anti-H3 ( Ab1791-100 rabbit ) . ChIP assays were carried out according to the protocol described in [55] with the following modifications . 200 µg of chromatin was immunoprecipitated using 2 µg of anti-γH2AX ( Epitomics ) , anti-rad21 ( SCC1 ) ( Abcam ab992 ) , anti-SMC3 ( a mixture of the two rabbit homemade antibodies ) , anti SA1 ( rabbit homemade antibody ) , anti SA2 ( rabbit homemade antibody ) or without antibody ( mock ) . For ChIP-Q-PCR , immunoprecipitated and input DNA were analysed in triplicate by real time Q-PCR ( primer sequences are provided Table S3 ) . IP efficiency was calculated as percent of input DNA immunoprecipitated , on positive loci ( such as close to a DSB ) and on a negative locus ( devoid of DSB ) . Data were expressed relative to the signal obtained on the negative locus . For ChIP-chip , DNA was amplified , labelled , and hybridized to high density oligonucleotide tiling arrays covering human chromosome 1 and 6 ( Affymetrix Human Tiling 2 . 0R-A ) , using the standard Affymetrix procedure , by the GeneCore facility at EMBL Heidelberg . Scanned array data were normalized using Tiling Affymetrix Software ( TAS ) ( quantile normalization , scale set to 500 ) and analyzed as described in [10] . Peaks and boundaries of γH2AX domains were determined using our home made algorithm ( described in [10] ) . Briefly , this algorithm was inspired from [56] and allows determining enriched domains of any size . Domains are determined through a two-step process . The first step defines zones of interest as contiguous sections of N probes in which x% of the probes are above a certain threshold . Second step allows bidirectional zones extension from theses seeds , to refine their limits ( also based on % of probes above a certain threshold ) . These zones can next be merged and filtered based on their size and values . For cohesin peaks identification , we used the following settings: Contiguous sections of 20 probes with at least 17 probes above the threshold were identified ( threshold was based on the percentage of graph values greater than 90% on individual chromosomes ) . In order to plot data with respect to transcription start sites ( TSS ) , gene transcript positions and orientations were obtained from the refFlat table from UCSC ( hg18 ) . All genomic coordinates were from the genome assembly NCBI Build 36 . 1 , and annotations were retrieved from the UCSC genome browser http://genome . ucsc . edu . Microarray probe coordinates and data have been submitted to Array Express under accession number E-TABM-1164 . The full procedure for the cleavage assay has been previously described [10] . Briefly biotynilated double stranded oligonucleotide were ligated overnight to genomic DNA extracted from 4OHT treated or untreated AsiSI-ER-U20S cells . T4 ligase was heat inactivated at 65°C for 10 min , and DNA was fragmented by EcoRI digestion at 37°C for 2 h followed by heat inactivation at 70°C for 20 min . After a preclearing step , DNA was pulled down with streptavidin beads ( Sigma ) at 4°C overnight , and then washed 5 times in RIPA buffer and twice in TE . Beads were resuspended in 100 µL of water and digested with HindIII at 37°C for 4 h . After phenol/chloroform purification and precipitation , DNA was resuspended in 100 µL of water , and submitted to Q-PCR , using primers described in Table S3 . After transfection with siRNA , and 4OHT treatment for 4H , cells were fixed in PBS containing 3 . 7% formaldehyde for 15 min at RT , permeabilized in PBS-0 . 5% Triton X100 for 10 min , and blocked with 3% bovine serum albumine ( BSA ) for 30 min . After 2 h incubation with γH2AX antibody ( Cell Signalling ) , cells were washed with PBS and probed for 1H with an Alexa Fluor 594 anti mouse antibody ( Molecular Probes ) . Slides were mounted with Vectashield ( Vector Laboratories ) , and images were acquired using a Leica microscope equipped with a charge-coupled device camera ( CoolSNAP ES; Roper Industries ) , and the MetaMorph software ( MDS Analytical Technologies ) . Quantification of fluorescence levels was done on a least 100 nuclei using home-developed macros in ImageJ software ( National Institutes of Health , Bethesda , MA ) to normalize background , thresholds and measures . | Genomic stability requires that deleterious events such as DNA double-strand breaks ( DSBs ) are precisely repaired . The natural compaction of DNA into chromatin hinders DNA accessibility and break detection . Therefore , cells respond to DSBs by triggering multiple chromatin modifications that promote accessibility and facilitate repair . We have recently developed a novel system whereby a restriction enzyme can be induced to inflict multiple DSBs across the human genome . This system permits high-resolution characterization of changes in the chromatin landscape that are induced around DSBs . While we previously reported the profile of H2AX phosphorylation ( a primary event in chromatin remodelling that takes place in response to DSBs ) , we now provide the high resolution mapping of cohesin , a complex implicated in the 3-D organisation of chromosomes within the nucleus . Unexpectedly , we have discovered that cohesins play a role in the maintenance of gene transcription in regions where chromatin has been remodelled during the DSB response . | [
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] | 2012 | Cohesin Protects Genes against γH2AX Induced by DNA Double-Strand Breaks |
The C-terminus domain of non-structural 3 ( NS3 ) protein of the Flaviviridae viruses ( e . g . HCV , dengue , West Nile , Zika ) is a nucleotide triphosphatase ( NTPase ) -dependent superfamily 2 ( SF2 ) helicase that unwinds double-stranded RNA while translocating along the nucleic polymer . Due to these functions , NS3 is an important target for antiviral development yet the biophysics of this enzyme are poorly understood . Microsecond-long molecular dynamic simulations of the dengue NS3 helicase domain are reported from which allosteric effects of RNA and NTPase substrates are observed . The presence of a bound single-stranded RNA catalytically enhances the phosphate hydrolysis reaction by affecting the dynamics and positioning of waters within the hydrolysis active site . Coupled with results from the simulations , electronic structure calculations of the reaction are used to quantify this enhancement to be a 150-fold increase , in qualitative agreement with the experimental enhancement factor of 10–100 . Additionally , protein-RNA interactions exhibit NTPase substrate-induced allostery , where the presence of a nucleotide ( e . g . ATP or ADP ) structurally perturbs residues in direct contact with the phosphodiester backbone of the RNA . Residue-residue network analyses highlight pathways of short ranged interactions that connect the two active sites . These analyses identify motif V as a highly connected region of protein structure through which energy released from either active site is hypothesized to move , thereby inducing the observed allosteric effects . These results lay the foundation for the design of novel allosteric inhibitors of NS3 .
Flaviviruses ( family Flaviviridae ) are small ( ∼11 kilobases ) positive-sense , single-stranded RNA ( ssRNA ) viruses that include members such as dengue ( serotypes 1-4 ) , Zika , West Nile , yellow fever , and Japanese Encephalitis viruses . The dengue virus ( DENV ) is a public health threat that causes serious morbidity and mortality globally [1 , 2] . Infection with DENV can result in “break-bone” fever , an extraordinarily painful disease with symptoms ranging from a mild fever to a fatal hemorrhagic syndrome [3] . There are approximately 50 million serious infections and 20 , 000 deaths each year , and dengue infections are a leading cause of mortality in children in a number of Latin and Asian countries [1] . Dengue viruses have re-emerged in the United States , and a growing number of locally acquired infections in Florida , Texas , and Hawaii have been reported over the last decade . Despite a reinvigorated effort due to the recent Zika epidemic [4] , there are currently no approved small molecule antivirals to treat Flavivirus-induced diseases . One of the primary antiviral targets in Flaviviridae is the nonstructural protein 3 ( NS3 ) , which plays a critical role in the viral replication cycle [5–15] . NS3 is a multifunctional protein found in all Flaviviridae , possessing an N-terminal serine protease domain responsible for proteolytically cleaving the viral polyprotein during translation [16] and a C-terminal helicase/nucleotide triphosphatase ( NTPase ) /RNA triphosphatase domain [17–22] . In a nucleotide triphosphate ( NTP ) hydrolysis-dependent mechanism , the NS3 helicase domain ( NS3h ) unwinds double-stranded RNA ( dsRNA ) while translocating along the nucleic polymer . These functions are required to resolve the dsRNA replication intermediate into fully-mature positive strand RNAs ( see Ref . [23] for a recent review ) . Mutations in the NS3 helicase and NTPase active sites are seen to abrogate NS3 function as well as decrease viral survival [24–26] , demonstrating the importance of these enzymatic functions to the flavivirus life cycle . Drugs identified to inhibit DENV NS3h suffer from specificity issues because they are either NTPase inhibitors [27] or RNA/DNA mimics such as ivermectin [13] , suramin [14] or aurintricarboxylic acid [15] . Therefore , it is of interest to further elucidate the mechanism of DENV NS3h with molecular resolution to help identify new and specific target regions for antiviral therapeutics . The Flaviviridae NS3h have been classified as a superfamily 2 ( SF2 ) helicase ( NS3/NPH-II subfamily; a DEx/H helicase ) where the NTPase cycle ( Fig 1 ) provides the free energy needed to unwind dsRNA and translocate along the nucleic substrate in a 3′ to 5′ direction [28] . Structurally , NS3h are monomeric helicases composed of three subdomains; subdomains 1 and 2 ( red and orange in the inset of Fig 1 ) are RecA-like folds that are structurally conserved across all SF1 and SF2 helicases , whereas subdomain 3 ( green ) is unique to the NS3/NPH-II subfamily and contains some of the least conserved portions of the protein . In Fig 1 , an adenosine triphosphate ( ATP; purple ) molecule is bound within the NTPase active site between subdomains 1 and 2 . Also , an RNA substrate ( blue ) is bound within the RNA-binding cleft , separating subdomains 1 and 2 from subdomain 3 . The 5′ terminus of the RNA is positioned at the top of the protein in Fig 1 and the ds/ss RNA junction is hypothesized to be just above this region of the protein . The NS3/NPH-II subfamily of SF2 helicases exhibit both RNA-stimulated NTPase activity and NTPase-dependent helicase activity [17–22] . These experimentally observed phenomena suggest that ( 1 ) the presence of RNA affects the NTPase active site , thereby activating the NTPase cycle and ( 2 ) this cycle is the source of free energy needed to perform work on the RNA ( translocation and unwinding ) . In Fig 1 , the enzymatic cycle for the NTPase function is depicted by four dynamic events: RNA is bound within the RNA-binding cleft and activates the NTPase cycle , NTP binds , NTP is hydrolyzed , and finally products ( nucleotide diphosphate—NDP—and inorganic phosphate—H2PO4- , Pi ) are released . To date , it is unclear which stage ( s ) of the cycle are responsible for the translocation and unwinding functions of NS3h . Furthermore , the biophysical couplings between NTPase and helicase active sites are still poorly understood [28] . One of the better studied Flaviviridae NS3h is that of the Hepatitis C virus ( HCV; family: Flaviviridae hepacivirus ) [29–40] . Utilizing both ensemble [29–35] and single molecule [36–38 , 41 , 42] techniques , studies have provided insights into the kinetic steps of the HCV NS3h translocation function . These studies , alongside crystallography studies of various Flaviviridae NS3h , suggest that the NS3 enzyme tracks along the phosphodiester backbone of the nucleic oligomer , unwinding one base-pair per hydrolysis event [35–37] . To explain these experimental results , various models describing the translocation mechanism have been reported , depicting NS3h as a Brownian [33–35] or backbone stepping motor [36 , 39–41] protein . These models envision the coupling between NTPase and helicase functions through different biophysical mechanisms , yet the models are not mutually exclusive and are limited in temporal and spatial resolution [43 , 44] . Luo et al . reported a set of crystal structures of the DENV NS3h in important protein-substrate complexes of the NTPase cycle ( bolded text in Fig 1 ) [45] . From these structures , major allosteric influences of RNA-binding were seen in the NTPase active site . For example , Luo and coworkers noted that the presence of an RNA substrate shifts the carboxylate group of Glu285 ( motif II ) into a more catalytically relevant structure for the hydrolysis reaction . Mutation of the Glu285 residue abrogates NTPase and helicase activities [25] . These static structures have provided novel insights into RNA-induced protein structural changes yet provide limited insight into the NTPase cycle or translocation and unwinding functions of NS3h . Previous theoretical studies of helicases have focused on a broad range of enzymes such as PcrA ( SF1 ) [46–49] , transcription terminator Rho ( SF5 ) [50] , SV40 ( SF3 ) [51] , and various NS3h enzymes [52–56] . Of the theoretical studies on NS3h , Perez-Villa et al . reported microsecond-long molecular dynamics ( MD ) simulations of the HCV NS3h-ssRNA systems in the presence and absence of ATP and ADP . The reported simulations were used to interrogate the thermodynamics of these substrate states with various conformations of the NTPase active site [52] . While the reported results are of interest for NS3h , the authors provide limited insight into the molecular mechanisms at play during the NTPase cycle . Other theoretical studies of the NS3h enzyme are limited in timescales ( tens to hundreds of ns of simulation ) , substrate states modeled , or spatial resolution ( e . g . coarse grained elastic network model ) [53–56] . Therefore , theoretical modeling of the NS3h enzyme has yet to elucidate further details about the structural and dynamic couplings within NS3h in light of the NTPase cycle . We report here a multiscale theoretical study of the DENV NS3h enzyme at each substrate state along the NTPase cycle . RNA-induced allostery on the NTPase active site is reported wherein the presence of an RNA substrate alters the positioning and dynamics of waters within the hydrolysis active site . Inspired by this observation , minimum energy electronic structure calculations are performed to investigate the energy landscape of the hydrolysis reaction . Additionally , investigations into NTPase substrate-induced allostery on the RNA-binding cleft suggest that NS3h interacts with RNA in a NTPase substrate-dependent manner . Umbrella sampling ( US ) simulations are performed to enhance the sampling of a proposed elementary step of the translocation mechanism observed during the unbiased simulations . Finally , analyses of the correlated motions between residues are used to identify allosteric pathways that connect the two active sites . It is through these pathways that we hypothesize that free energy released during the NTPase cycle is transduced to the RNA-binding cleft and utilized to perform work on the RNA . This study of the substrate states of DENV NS3h lays the foundation for further study of the NTPase cycle and marks the most complete picture of the molecular mechanism of the NS3 NTPase/helicase to date .
A subset of the crystal structures reported by Luo et al . [45] of the Dengue NS3h ( serotype 4 ) are used as the initial structures for all-atom , explicit solvent MD simulations . Specifically , the binary complex of NS3h with a seven-residue ssRNA substrate ( PDB ID: 2JLU ) is used to model the ssRNA substrate state , while the ternary structures of ssRNA+ATP ( 2JLV ) , ssRNA+ADP+Pi ( 2JLY ) , and ssRNA+ADP ( 2JLZ ) model the pre-hydrolysis , post-hydrolysis , and product release states of the NTPase cycle , respectively . The Apo ( 2JLQ ) and ATP ( 2JLR ) substrate states are also simulated and used as experimental controls for our investigation into allostery . The RNA-bound structures of DENV NS3h were crystalized as dimers of the protein [45] . For these systems , chain A of the structure is used as the starting conformation . Furthermore , the A conformers are chosen for residues with multiple side chain conformations . In all crystal structures with ATP substrates , the crystalized Mn2+ divalent cation is converted into a Mg2+ . For the ATP crystal structure ( 2JLR ) , residues of the protease linker region were poorly resolved and so are transferred from the Apo ( 2JLQ ) structure after aligning the neighboring amino acid backbones in both systems . All-atom , explicit solvent MD simulations are performed for the six substrate states of DENV NS3 and presented in Fig 1 ( denoted Apo , ATP , ssRNA , ssRNA+ATP , ssRNA+ADP+Pi , and ssRNA+ADP ) . The simulations are performed using the GPU-enabled AMBER14 software [57] , ff14SB [58] parameters for proteins , and ff99bsc0χ OL3 [59 , 60] parameters for RNA . Parameters for ATP [61] , ADP [61] , Pi ( provided in Supplementary Information ( SI ) ; S2 File ) , and Mg2+ [62] are also used . For each system , the crystal structures are solvated in TIP3P water boxes with at least a 12 Å buffer between the protein and periodic images . Crystallographic waters are maintained . Sodium and chloride ions are added to neutralize charge and maintain a 0 . 10 M ionic concentration . The Langevin dynamics thermostat and Monte Carlo barostat are used to maintain the systems at 300 K and 1 bar . Direct nonbonding interactions are calculated up to a 12 Å distance cutoff . The SHAKE algorithm is used to constrain covalent bonds that include hydrogen [63] . The particle-mesh Ewald method [64] is used to account for long-ranged electrostatic interactions . A 2 fs integration time step is used , with energies and positions written every 2 ps . The minimum amount of simulation performed for each system is one trajectory of 1 . 5 μs , with the first 200 ns of simulation sacrificed to equilibration of the starting structures . Simulation of the ssRNA system is performed to 2 μs . For both the ATP and ssRNA+ATP systems , two 1 . 5 μs simulations are performed . The total amount of unbiased simulation reported here on the described structures is 12 . 5 μs . US simulations are performed to enhance sampling of a hypothesized elementary translocation event wherein the biased collective variable is the distance between the central carbon of the guanidinium group of Arg387 to the phosphorous atom of phosphate 4 in the RNA . These simulations are run for the ssRNA , ssRNA+ATP , ssRNA+ADP+Pi , and ssRNA+ADP systems , using the same protocol as the unbiased simulations with the addition of a bias . For each substrate state , a minimum of 22 sampling windows are simulated for 50 ns each with harmonic wells positioned every 0 . 5 Å and ranging from 3 . 50 to 14 . 00 Å . Harmonic force constants are 20 kcal mol-1 Å-2 . Further simulation and additional windows are run in regions of collective variable space with poor sampling . The weighted histogram analysis method ( WHAM ) [65] is used to analyze the results of these simulations , with bin sizes of 0 . 1 Å . Bootstrapping is used to approximate error bars for the probability density and free energy plots shown . The total amount of biased simulations reported here is 5 . 12 μs . Electronic structure calculations are performed at the ωB97X-D/6-31+G* level of theory [66] using the Guassian 09 version B . 01 program [67] . The ωB97X-D functional is chosen due to its broad applicability [68 , 69] and a recent study demonstrating its energetic accuracy for a variety of phosphate hydrolysis reactions [70] . The QM system is composed of a truncated ATP molecule ( truncated to methyl triphosphate , MTP ) , functional groups of nine surrounding protein residues ( Pro195 , Gly196 , Lys199 , Glu285 , Ala316 , Gly414 , Gln456 , Arg460 , and Arg463 ) , a Mg2+ ion , and seven water molecules . The amino acids are truncated at various positions ( more detail in S1 File ) using hydrogen atoms . For each residue , the position of the terminal heavy atom is frozen to maintain the active site geometry . This yielded a total of 138 atoms in the QM calculations . These calculations are performed on active site conformations pulled from the unbiased MD simulations of the ssRNA+ATP and ATP substrate states , thereby investigating the influence of observed RNA structural allostery on the hydrolysis reaction mechanism and energy landscape . Frames used for the initial reactant state structures were selected by visualizing MD frames in which a lytic water is present . Through visual and RMSD analyses of such frames , a single frame was chosen to represent the population of catalytically relevant structures . The hydrolysis reaction is then monitored by optimizing the reactants ( MTP+lytic water ) , products ( MDP+HPO42− ) , and a single transition state ( TS ) in between . The initial TS and product state structures were created from the previous optimized structure . The minima are confirmed using a Hessian calculation . The TS is confirmed by examining the direction of the single imaginary frequency . Following geometry optimization , frequency calculations are performed to obtain gas-phase , zero-point energy corrected free energies for each active site conformation . Unless stated otherwise , analyses of MD trajectories are performed using Python 2 . 7 and the MDAnalysis module ( version 0 . 15 . 0 ) [71] . Matplotlib is used for plotting data [72] . VMD is used for visualization of trajectories and production of structural figures [73–75] . For each substrate state , a single frame from the trajectories is used when presenting structural details of the respective substrate state . Further information on choosing these “exemplar” structures is given in the S1 File . Additionally , details of all analyses performed can be found in the S1 File . All scripts for the analyses are available on Github ( https://github . com/mccullaghlab/DENV-NS3h ) .
To date , no biophysical explanation has been proposed for the 10 to 100-fold increase in NTPase turnover rate observed for DENV NS3h in the presence of RNA [22] . Crystallographic studies of the DENV NS3h structure have identified static structural allostery due to RNA binding [45] , yet a dynamic picture and interpretation of these influences are still missing . In this section , comparisons of the simulations of the Apo , ATP , ssRNA , and ssRNA+ATP substrate states are used to depict structural rearrangements induced by RNA . These RNA-induced allosteries are observed to affect the positioning and dynamics of waters within the NTPase active site . These novel insights gained from the comparisons of the MD simulations inspire the reported electronic structure calculations of the reactant , transition , and product states of the hydrolysis reaction . In combination , these results demonstrate that the observed enhancement of NTPase activity originates from the RNA-induced destabilization of the lytic water . Experimental studies have shown that the NS3h helicase functions ( translocation and unwinding ) are NTPase dependent , yet it is unclear which equilibrium states and/or dynamic events of the NTPase cycle are the source of the necessary free energy for these functions [20 , 21] . All previously developed models describing these functions have deduced that the NTPase cycle drives conformational changes in the RNA-binding cleft , thereby cycling the protein-RNA interactions leading to unidirectional translocation and melting of the duplex/single stranded nucleic junction [33–36 , 39–41] . Yet , limited structural allostery attributed to the NTPase substrates ( e . g . ATP , ADP , and Pi ) is observed in the crystal structures of DENV NS3h [45] . Therefore , a subset of the MD simulations reported here ( ssRNA , ssRNA+ATP , ssRNA+ADP+Pi , and ssRNA+ADP ) is used to interrogate protein-RNA interactions as well as identify protein structural changes that have NTPase substrate-dependent behaviors . The current view of allosteric regulation focuses on signal transduction through complex , 3-dimensional networks , brought about by intrinsic structural and/or dynamic changes along pathways connecting two distal , non-overlapping active sites [96–98] . These allosteric pathways are described by coupled short-range , residue-residue interactions that lead to long-range correlations . In the previous two sections , RNA-induced and NTPase substrate-induced structural rearrangements have been presented . In this section , these allosteric structural changes are absorbed into a unified description of the allosteric pathways connecting the RNA-binding cleft with the NTPase active site . Dynamic network analyses , such as residue-residue correlations , have been used to identify allosteric pathways within proteins from simulation [96–100] . A growing body of literature has highlighted the functional importance of such pathways as well as the fundamental residue-residue interactions leading to their emergence [96–102] . We report here residue-residue distance correlation analyses that are used to identify the allosteric pathways present within the DENV NS3h protein . Focus is given to the motifs discussed in the previous sections ( α2 , motifs II and IVa ) due to the observed structural rearrangements . Additionally , the correlation heat maps are used to identify segments of the protein that experience strong correlations with numerous other regions of the protein , such as motif V . While motif V does not experience substrate-induced structural rearrangements , the strong correlations between motif V and motifs in both the NTPase active site and RNA binding cleft are hypothesized to have functional importance in the signal transduction mechanism of allosteric regulation . Unlike the previous two sections , comparisons between substrate states ( RNA-bound and NTPase substrate-bound ) are not considered here . Instead , focus is given to the discussion of the residue-residue distance correlation analysis of the ssRNA+ATP substrate state . Through analyses of the reported simulations , molecular observables of RNA- and NTPase substrate-induced allostery were identified . Specifically , an RNA bound within the RNA-binding cleft affects the dynamics and positioning of water molecules within the NTPase active site . This allosteric influence is conferred from the RNA-binding cleft to the hydrolysis active site through structural rearrangements of Lβ3β4 , α2 , and motif II . These RNA-induced structural changes lead to an entropic destabilization of the NTPase active site as well as a direct destabilization of the lytic water . Inspired from these results , electronic structure calculations were used to investigate the energetics of NTP hydrolysis reaction . The energetic landscapes obtained from the DFT calculations demonstrate that RNA decreases the activation barrier as well as affects the mechanism of the hydrolysis reaction . Combining these results into a kinetic model allowed for the calculation of a theoretical RNA-stimulated NTPase activity enhancement factor of 150 , which qualitatively matches the experimentally observed enhancement factor . Therefore , results from MD and DFT calculations provide novel , multiscale insight into the RNA-induced allosteric effects that stimulate the catalysis of the NTP hydrolysis reaction in DENV NS3h . Unlike RNA , the NTPase substrates are smaller perturbations to the NS3h structure and dynamics . Protein-RNA interaction energies were used to investigate the NTPase substrate-dependence of protein-RNA contacts in the unbiased MD simulations . From these analyses , the protein-RNA phosphodiester backbone interactions were observed to be NTPase substrate-dependent . The presence of the γ-phosphate ( or Pi ) of the NTPase substrate was observed to strengthen the protein-RNA contacts . Furthermore , the localized nonbonding interaction energies demonstrate a large shift in protein-RNA contacts , originating in part from the side chain conformational states of Arg387 . Results from US simulations demonstrate that the Arg387 side chain conformational states exemplify NTPase substrate-dependent protein-RNA interactions . With the purview of the NTPase cycle , transitions between conformational states leads to 3′ to 5′ translocation . Therefore , we hypothesize that the transition between Arg387 side chain conformations is an elementary step in the unidirectional translocation mechanism of NS3h along the phosphodiester backbone of RNA . Finally , consideration of these allosteric effects independent of one another provides an incomplete picture of the biophysics of the NS3h protein . Residue-residue correlation analyses were used to identify structural regions of the protein that experienced correlated motions with other regions . These analyses were used to describe the allosteric pathways that connect α2 with motifs II and IVa . The short-range , residue-residue interactions were presented that connect the RNA-binding cleft to the NTPase active site . Furthermore , the correlation heat maps allow for identification of regions of the protein that experience strong correlated motions with numerous other regions . Motif V is one such example , where the segment of 13 residues has strong coupled motions with seven other motifs in subdomains 1 and 2 . This highly correlated nature suggests that motif V functions as a centralized communication hub that connects distal portions of the protein structure . Complete modeling of a revolution of the NTPase cycle in the DENV NS3h presents a significant challenge for current computational methodologies . Rather , we have divided the cycle into equilibrium substrate states and dynamic events where the protein transitions from one substrate state to another . The simulations reported have modeled the important NTPase cycle substrate states , leading to novel insights into the function and underlying biophysics of the DENV NS3h enzyme with focus given to the allosteric connections between the RNA-binding cleft and NTPase active site . We hypothesize that the observed allosteric effects and pathways have important roles in the transduction of energy from one active site to the other during the dynamic events of the NTPase cycle . Therefore , the results reported have laid an initial foundation for theoretical investigations into the dynamic events of the NTPase cycle . Beyond further theoretical modeling of NS3h , transverse relaxation-optimized spectroscopy ( TROSY ) NMR , mutational biochemical studies , and targeted small molecule binding experiments can be envisioned to test the hypothesis and results presented . Nuclear magnetic resonance has been previously used to study dynamics within isolated HCV NS3 helicase subdomains [103–105] , but the size of the full dengue NS3h domain is too large for traditional NMR approaches due to line width increasing with increased molecular mass . However , TROSY NMR has been developed that may allow for experimental monitoring of fast NS3h dynamics [106] . We anticipate that perturbation of the wild-type structure or dynamics of the allosteric pathways in NS3h will lead to abrogation of NTPase and/or helicase functions , and are currently developing assays to test this hypothesis with dengue NS3h . Residues active in the allosteric pathways are viable targets for mutational studies where varying a specific amino acid residue is expected to alter the short-range , residue-residue interactions and lead to a destabilization of the pathway connecting the two active sites . This is hypothesized to result in reductions in enzymatic activity and would be observable in our biochemical assays . Additionally , these pathways are viable targets for theoretical and experimental small molecule drug docking experiments with focus given to molecules that disrupt the residue-residue interactions along the pathway . Co-crystallization of specific conformation-binding molecules may lock NS3h into transition-state conformations that can help verify our computational studies . Molecular candidates also have the potential for inhibiting NS3h function during replication and being specific to the NS3/NPH-II subfamily of SF2 helicases . | Non-structural protein 3 ( NS3 ) is a Flaviviridae ( e . g . Hepatitis C , dengue , and Zika viruses ) helicase that unwinds double stranded RNA while translocating along the nucleic polymer during viral genome replication . As a member of superfamily 2 ( SF2 ) helicases , NS3 utilizes the free energy of nucleotide triphosphate ( NTP ) binding , hydrolysis , and product unbinding to perform its functions . While much is known about SF2 helicases , the pathways and mechanisms through which free energy is transduced between the NTP hydrolysis active site and RNA binding cleft remains elusive . Here we present a multiscale computational study to characterize the allosteric effects induced by the RNA and NTPase substrates ( ATP , ADP , and Pi ) as well as the pathways of short-range , residue-residue interactions that connect the two active sites . Results from this body of molecular dynamics simulations and electronic structure calculations are highlighted in context to the NTPase enzymatic cycle , allowing for development of testable hypotheses for validation of these simulations . Our insights , therefore , provide novel details about the biophysics of NS3 and guide the next generation of experimental studies . | [
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] | 2018 | Allostery in the dengue virus NS3 helicase: Insights into the NTPase cycle from molecular simulations |
Anticancer topoisomerase “poisons” exploit the break-and-rejoining mechanism of topoisomerase II ( TOP2 ) to generate TOP2-linked DNA double-strand breaks ( DSBs ) . This characteristic underlies the clinical efficacy of TOP2 poisons , but is also implicated in chromosomal translocations and genome instability associated with secondary , treatment-related , haematological malignancy . Despite this relevance for cancer therapy , the mechanistic aspects governing repair of TOP2-induced DSBs and the physiological consequences that absent or aberrant repair can have are still poorly understood . To address these deficits , we employed cells and mice lacking tyrosyl DNA phosphodiesterase 2 ( TDP2 ) , an enzyme that hydrolyses 5′-phosphotyrosyl bonds at TOP2-associated DSBs , and studied their response to TOP2 poisons . Our results demonstrate that TDP2 functions in non-homologous end-joining ( NHEJ ) and liberates DSB termini that are competent for ligation . Moreover , we show that the absence of TDP2 in cells impairs not only the capacity to repair TOP2-induced DSBs but also the accuracy of the process , thus compromising genome integrity . Most importantly , we find this TDP2-dependent NHEJ mechanism to be physiologically relevant , as Tdp2-deleted mice are sensitive to TOP2-induced damage , displaying marked lymphoid toxicity , severe intestinal damage , and increased genome instability in the bone marrow . Collectively , our data reveal TDP2-mediated error-free NHEJ as an efficient and accurate mechanism to repair TOP2-induced DSBs . Given the widespread use of TOP2 poisons in cancer chemotherapy , this raises the possibility of TDP2 being an important etiological factor in the response of tumours to this type of agent and in the development of treatment-related malignancy .
The double-stranded helical structure of DNA creates topological problems in all processes that involve opening of the double helix and accessing the genetic information [1] , [2] . In particular , the transcription and duplication of DNA and its condensation into chromosomes generates knots and tangles that need to be resolved to avoid interference with diverse cellular processes and to ensure faithful chromosome segregation during mitosis . DNA topoisomerases are enzymes that introduce transient breaks in DNA to solve these topological problems . Type II topoisomerases , such as topoisomerase II in eukaryotes ( TOP2 ) are essential homodimeric enzymes that relax , unknot and decatenate DNA molecules by catalyzing the passage of duplex DNA through a transient DNA double strand break ( DSB ) created by the enzyme [3] . Two isoforms of TOP2 , α and β , exist in higher eukaryotes , with primary roles in replication and chromosome segregation and in transcription , respectively . A key intermediate of TOP2 activity is the cleavage complex , in which each of two topoisomerase subunits is covalently linked to the 5′-terminus of an enzyme-generated DSB via a phosphodiester bond between the active-site tyrosine and the 5′-phosphate . The cleavage complex is normally a very short-lived intermediate , because the topoisomerase rapidly re-ligates the DSB once DNA strand passage through the DSB has occurred . However , under certain circumstances , such as the presence of nearby DNA lesions , cleavage complexes can be stabilized resulting in an increased likelihood of collision with RNA or DNA polymerases [4] . Such collisions can convert cleavage complexes into potentially clastogenic or lethal DSBs that require cellular DNA repair pathways for their removal . Cleavage complexes are the target of a widely used class of anti-tumor agents that ‘poison’ topoisomerase activity , thereby prolonging the half-life of the intermediate and increasing the possibility of DSB formation [4] , [5] . Thus , these drugs kill tumor cells by inducing high levels of TOP2-associated DSBs . Consequently , TOP2 poisons are commonly used antineoplastic drugs in the treatment of a broad range of tumor types including malignant lymphomas , sarcomas , leukemias , and lung , ovarian , breast and testicular cancers [5] . However , similar to other chemotherapeutic agents , TOP2-targeting drugs are only partially selective for tumour cells , resulting in unwanted toxicity in normal tissues and in therapy-associated chromosome translocations and secondary leukemias [6]–[14] . Moreover , some breakpoints in such translocations have actually been correlated with preferential sites of cleavage by TOP2 [13]–[17] . A characteristic feature of TOP2-induced DNA breaks is covalent attachment of the enzyme to 5′ ends of the DNA , which must be removed by cellular end-processing enzymes if DSB repair is to occur [18] . Until recently , the only known mechanism for removal TOP2 peptide from DNA 5′-termini in mammalian cells involved excision of the DNA fragment linked to the peptide using nucleases such as the MRN complex , CtIP or Artemis [19]–[21] . Recently , however , we identified a human 5′-tyrosyl DNA phosphodiesterase ( 5′-TDP ) that can cleave 5′-phosphotyrosyl bonds and thereby release TOP2 from DSB termini without the need to also remove DNA sequence [22] . Consequently , this enzyme , which was previously known as signalling protein and transcription cofactor TTRAP/EAPII [23] , [24] , is now denoted tyrosyl DNA phopshodiesterase-2 ( TDP2; Human Gene Nomeclature Organisation ) . Notably , consistent with its enzyme activity , TDP2 is required for cellular resistance to the anti-cancer TOP2 poison etoposide , but is not required for cellular resistance to ionizing radiation or methylmethane sulphonate [22] , [25]; agents that induce DNA damage independently of TOP2 activity . Following DNA end processing , DSBs can be repaired either by homologous recombination ( HR ) or by non-homologous end joining ( NHEJ ) [26] . However , these pathways utilize fundamentally different mechanisms for rejoining DSBs and consequently differ in their accuracy . In particular , HR utilizes undamaged sister chromatids to replace any nucleotides removed from DNA termini during DNA end processing and consequently is normally ‘error-free’ . However , this process is available only during S phase or G2 , when sister chromatids are available . In contrast , NHEJ is a ‘cut-and-splice’ process in which DSB termini are ligated together following DNA end processing without accurate replacement of missing nucleotides , and thus is potentially ‘error-prone’ . Here , we employ avian and murine experimental models to show that TDP2/Tdp2 deletion results in hypersensitivity to a structurally diverse range of anti-cancer TOP2 poisons . Moreover , we present genetic , biochemical and cellular evidence for TDP2 functioning in a mechanism of NHEJ that protects genome integrity in response to TOP2-induced damage . Finally , we show that this TDP2 dependent pathway also operates in vivo , as , upon exposure to TOP2 poisons , it is required for normal adult mouse lymphopoiesis , intestinal mucosa homeostasis and the maintenance of genome stability in the bone marrow . Collectively , our results suggest that TDP2 defines an error-free mechanism of NHEJ in mammals , which is specialized in the repair of TOP2-induced DSBs and reduces both tissue toxicity and genome instability in response to this particular type of DNA damage . These findings suggest the possibility of TDP2 being a significant etiological factor in the clinical tolerance and response to widely used TOP2 poisons .
The discovery of TDP2 as the first 5′-TDP activity raised the possibility of it being an important factor in the clinical response to TOP2 poisons [22] , [25] . Indeed , TDP2 deleted avian DT40 cells are hypersensitive to etoposide [22] , [25] . To address this question further , we examined the sensitivity of TDP2−/−/− cells to two additional , structurally diverse , TOP2 poisons . These drugs , denoted doxorubicin and amsacrine ( m-AMSA ) , are employed widely during cancer therapy but in contrast to etoposide , ‘poison’ TOP2 by intercalating into DNA [5] . Nevertheless , similarly to etoposide , TDP2−/−/− cells displayed significant hypersensitivity to both doxorubicin and m-AMSA ( Figure 1A ) . Moreover , a functional TDP2 phosphodiesterase domain was required for cellular resistance to this type of drug , because expression of wild-type human TDP2 ( hTDP2 ) rescued the sensitivity of TDP2−/−/− DT40 cells to m-AMSA , whereas hTDP2D262A harbouring an inactivating mutation in the catalytic active site [5] did not ( Figure 1A ) . These results show that TDP2 is required for cellular resistance to a range clinically relevant and structurally diverse TOP2 poisons , and support our contention that this requirement reflects the 5′-TDP activity of this enzyme . To determine the impact of TDP2 on TOP2-induced DNA damage in mammals , and thus its possible relevance to anti-cancer therapy , we adopted a mouse model in which the first three exons of Tdp2 , plus the 5′-UTR , were deleted by Cre-mediated excision ( Figure 1B; see Materials and Methods ) . Mice homozygous for the deleted allele ( Tdp2flΔ , from here-on denoted Tdp2Δ1–3 ) are viable , and so far we have not detected any abnormal pathology ( unpublished observations ) . However , transformed Tdp2Δ1–3 mouse embryonic fibloblasts ( MEFs ) were hypersensitive to etoposide ( Figure 1C , left , and Figure S1 ) , but were not hypersensitive to DNA damage induced independently of TOP2 by γ-irradiation ( Figure 1C , right ) . Protein extracts from spleen , thymus , and bone marrow from wild type mice possess robust 5′-TDP activity , but , importantly , this activity was absent in analogous protein extracts from Tdp2Δ1–3 mice , confirming successful inactivation of the enzyme ( Figure 2A ) . Cell extracts prepared from primary Tdp2Δ1–3 MEFs also lacked detectable 5′-TDP activity ( Figure 2B ) . This was true not only for blunt-ended DSB substrates , but also for DSB substrates harbouring a 4-bp 5′-overhang ( Figure 2C ) , characteristic of TOP2-induced DSBs . Additionally , EDTA-mediated chelation of Mg2+ , which is essential for TDP2 function , completely eliminates 5′-TDP activity in wild type MEF extracts . These observations are significant because the related enzyme TDP1 , whose activity is Mg2+ independent , was recently reported to possess weak activity on this type of substrate [27] . Our data therefore suggest that TDP2 is the primary , if not only , source of 5′-TDP activity in MEF extracts ( Figure 2C ) . Based on the mechanism of TOP2 cleavage , we anticipated that TDP2 activity would reconstitute ‘clean’ DSBs ( 5′ phosphate and 3′ hydroxyl termini ) with 4-bp overhangs , which would be an ideal substrate for ligation by NHEJ . Interestingly , these ligation events would accurately preserve the DNA sequence , suggesting the possibility of an error-free NHEJ mechanism that specifically acts on TOP2-induced DSBs . To test this hypothesis , we examined whether TDP2 action at DSBs typical of those induced by TOP2 creates termini that can be ligated by T4 DNA ligase . Indeed , inclusion of T4 DNA ligase in reactions containing wild type MEF extract resulted in the additional appearance of a product of 46-nt , indicative of the completion of DSB repair by DNA ligation . However , this product was not observed if reactions contained cell extract from Tdp2Δ1–3 MEFs , confirming that DNA ligation was dependent on TDP2 activity ( Figure 2D ) . Interestingly , the length of the product is consistent with a ligation event in which DNA sequence is preserved . To analyse ligation events directly catalysed by cell extracts , we generated linear plasmids harbouring 5′ phosphate or 5′ phosphotyrosine ends by PCR amplification with the corresponding modified primers . The incubation of these substrates with NHEJ-competent nuclear extracts [28] results in plasmid circularization events that can be scored as colonies following bacterial transformation . As can be seen in Figure 2E , nuclear extracts from Tdp2Δ1–3 MEFs efficiently circularized linear plasmids with 5′ phosphate ends but not linear plasmids harbouring 5′-phosphotyrosine . This difference was lost upon addition of recombinant TDP2 to the reaction , confirming the TDP2–dependent nature of the repair reaction . Collectively , our data suggest that TDP2 activity facilitates NHEJ of 5′ tyrosine-blocked ends by generating DSBs with ligatable termini , consistent with our hypothesis that this enzyme can support error-free NHEJ of TOP2-induced DNA damage . To genetically test whether TDP2 functions indeed during NHEJ , we generated TDP2−/−/− DT40 cells harboring a targeted deletion of Ku70 , a core component of the NHEJ pathway ( Figure S2 ) . Whilst both TDP2−/−/− and KU70−/− cells were hypersensitive to etoposide , cells in which both genes were deleted ( TDP2−/−/−/KU70−/− ) were no more hypersensitive than cells in which Ku70 alone was deleted ( Figure 3A ) . In contrast to this epistatic relationship with a core NHEJ factor , transient knockdown of TDP2 further enhances etoposide sensitivity of HR defective ( BRCA2 mutated ) human fibroblasts ( Figure 3B ) . Based on these genetic relationships , we conclude that TDP2 functions in a NHEJ-mediated and HR-independent pathway for the repair of TOP2-induced DSBs . To further assign a role for TDP2 in the NHEJ pathway for DSB repair , we measured DSB repair rates in primary Tdp2Δ1–3 MEFs by immunodetection of γH2AX , a phosphorylated derivative of histone H2AX that arises at sites of chromosomal DSBs [29] . We measured DSB repair in specific phases of the cell cycle , because whilst NHEJ is operative throughout , HR-mediated DSB repair is operative only in S/G2 [30] . Notably , DSB repair rates were markedly reduced in Tdp2Δ1–3 MEFs following etoposide treatment , both in G0/G1 ( Figure 3C ) and G2 ( Figure 3D ) , consistent with TDP2 functioning , as NHEJ , independently of cell cycle . These results were not specific to murine cells , since similar results were observed in TDP2-depleted human A549 cells ( Figure S4 ) . In contrast to treatment with etoposide , the rate of DSB repair was normal in Tdp2Δ1–3 MEFs following γ-irradiation , consistent with a role for TDP2 specifically at TOP2-induced DSBs ( Figure 3E ) . Collectively , these data demonstrate that TDP2 is required in mammalian cells for rapid repair of TOP2-induced DSBs by NHEJ , and for cellular resistance to these lesions . We hypothesized that this TDP2-mediated error-free NHEJ mechanism would be important to maintain genome integrity upon exposure to TOP2 poisons . To address this possibility , we quantified the frequency of micronuclei ( MN ) , nucleoplasmic bridges ( NB ) , and chromosomal aberrations following etoposide treatment . These events constitute well-established indicators of genome instability caused by misrepair of DSBs in which acentric , dicentric and aberrant chromosomes or chromosome fragments can be formed . As expected , etoposide increased the number of micronuclei and nucleoplasmic bridges in both transformed Tdp2+/+ and Tdp2Δ1–3 MEFs , but this increase was significantly higher ( up to three-fold ) in Tdp2Δ1–3 cells ( Figure 4A ) . Primary Tdp2Δ1–3 MEFs at low passage ( P3–4 ) similarly displayed elevated levels of micronuclei and nucleoplasmic bridges following etoposide treatment , compared to wild type primary MEFs ( Figure 4B ) , although in the case of nucleoplasmic bridges the low number of cells displaying these structures prevented the difference from reaching statistical significance . An additional indicator of genome instability is elevated frequencies of chromosome aberrations . Consequently , we quantified the frequency of chromosome breaks and exchanges in metaphase spreads of transformed Tdp2+/+ and Tdp2Δ1–3 MEFs . In agreement with the increased cell cycle arrest of TDP2−/−/− DT40 cells in G2 following etoposide treatment [25] , we noted an etoposide-dependent reduction in metaphase cells that was particularly severe in Tdp2Δ1–3 MEFs ( unpublished observations ) . However , of those metaphases observed and scored , both chromosome exchanges and breaks were significantly higher ( 2 to 5-fold ) in Tdp2Δ1–3 MEFs than in Tdp2+/+ MEFs ( Figure 4C ) . A similar increase in these events in Tdp2Δ1–3 MEFs , compared to wild type cells , was observed if low-passage primary MEFs were employed , ruling out the possibility that the elevated genome instability in Tdp2Δ1–3 MEFs was an artefact of cellular transformation ( Figure 4D ) . In the latter case , etoposide treatment almost ablated the appearance of mitotic cells in populations of both wild type and Tdp2Δ1–3 MEFs , necessitating the use of caffeine to prevent G2 arrest . Taken together these results demonstrate that loss of TDP2 results in increased genome instability following TOP2-induced DNA strand breakage . The above results demonstrate increased genome instability in Tdp2Δ1–3 MEFs , consistent with a role for TDP2 in error-free NHEJ-mediated repair of TOP2-induced DSBs . In this scenario , we considered the possibility that loss of TDP2 might also result in channelling of DSB repair towards HR . To address this question , we analyzed the formation of RAD51 foci , a well-established indicator of repair by HR . Following treatment with etoposide , the average number of Rad51 foci per cell was ∼3-fold higher in Tdp2Δ1–3 than in wild-type MEFs ( Figure 5A ) , in agreement with an increase in the use of HR to repair TOP2-induced DSBs when TDP2 is not present . Furthermore , we compared the frequency of etoposide-induced sister chromatid exchanges ( SCEs ) , a molecular hallmark of HR [31] , in wild type and Tdp2Δ1–3 MEFs ( Figure 5B ) . Notably , SCE levels increased substantially in transformed MEFs following acute etoposide exposure , being significantly higher in Tdp2Δ1–3 cells at two etoposide concentrations tested ( 1 and 2 . 5 µM ) . These data confirm that , upon etoposide treatment , the frequency of HR is elevated in Tdp2Δ1–3 MEFs , consistent with TDP2 functioning in NHEJ . To address the relevance of TDP2-mediated repair of TOP2-induced DSBs in vivo , we compared the impact of etoposide on adult ( 8 wk ) wild type and Tdp2Δ1–3 mice . A single intraperitonal injection of etoposide ( 75 mg/kg ) caused a decrease in body weight in the initial 4 days post-treatment both in wild type and Tdp2Δ1–3 animals ( Figure 6A ) . However , whereas Tdp2+/+ mice exhibited relatively mild and transient weight loss , Tdp2Δ1–3 littermates lost weight progressively and were sacrificed at day 6 to prevent suffering . No differences in body weight were observed between mock-treated ( with DMSO ) wild type and Tdp2Δ1–3 mice . Histopathological analysis of Tdp2Δ1–3 mice sacrificed 6 days after etoposide treatment revealed marked villous atrophy in the small intestinal mucosa as the likely cause of the drastic weight loss ( Figure 6B ) . This was not observed in either wild-type and/or DMSO treated animals ( data not shown ) , suggesting a protective role for TDP2 against adverse effects of etoposide in vivo . In addition to severe intestinal damage , etoposide administration resulted in elevated splenic and thymic atrophy in Tdp2Δ1–3 mice , compared to wild type mice ( Figure 6C ) , consistent with the known hypersensitivity of these organs to this drug [32] . Histological analysis of these tissues revealed a marked reduction in the cellular content in Tdp2Δ1–3 animals ( Figure 6C , right , note the low density of dark-stained nuclei ) . In light of these results , we analysed B-cell and T-cell maturation in wild type and Tdp2Δ1–3 mice ( Figure 6D and Figure S5 ) . In the case of B-cell precursors in bone marrow , treatment with etoposide resulted in a decrease of 30–50% in the fraction of cells that were CD43+ B220+ progenitors ( Pro-B cells ) and a decrease of >95% in the fraction of cells that were CD43− B220low ( Pre-B cells ) or CD43− B220high ( immature B cells ) precursors . In all cases the reduction in B-cell precursors was greater in Tdp2Δ1–3 mice , but the differences were not statistically significant at the administered dose . In contrast , in the case of T-cell maturation , whereas etoposide treatment reduced the fraction of CD4+ CD8+ immature T cells by 30–40% in wild type mice , these cells were almost completely eliminated in Tdp2Δ1–3 mice ( Figure 6A , bottom right ) . No effect was observed in CD11b/Mac-1+ myeloid cells in the bone marrow ( Figure S6 ) . Taken together , these results suggest that loss of TDP2 increases cellular attrition in the lymphoid system , particularly in the T-cell lineage , in response to TOP2-induced DNA damage . A major side-effect of cancer therapy employing TOP2 poisons is secondary hematological malignancy , and in particular acute leukemia , resulting most likely from error prone/erroneous repair of TOP2-induced DSBs and chromosome translocations [4] , [7] . Given our findings that TDP2 limits genome rearrangements induced by etoposide in cells , we examined whether TDP2 also promotes genome stability in bone marrow in vivo . We quantified the fraction of micronucleated polychromatic erythrocytes ( PCEs ) in bone marrow smears from Tdp2Δ1–3 and Tdp2+/+ mice 24 hour after intraperitoneal injection of etoposide ( 1 mg/kg ) . The rodent erythrocyte micronucleus test is a standard procedure to detect cytogenetic damage in toxicological studies and is based on the detection of micronuclei in erythrocyte precursors ( Hayashi et al 1994 ) . As expected , etoposide increased the fraction of PCEs that were micronucleated in both wild type and Tdp2Δ1–3 animals ( Figure 7 ) . However , this increase was ∼2-fold higher in Tdp2Δ1–3 mice than in wild type mice , suggesting that TDP2 protects heamatopoietic cells from genome instability induced by anti-cancer TOP2 poisons .
In the current study we observe that Tdp2 deletion ablates detectable 5′-TDP activity in different mouse tissues and MEFs , consistent with our previous observations in DT40 cells [25] . It is worth noting that other roles have been assigned to this protein , in other cellular processes such as signal transduction and transcriptional regulation [33] . So far , however , we have been unable to detect any spontaneous phenotype caused by TDP2 loss , either at cellular level or in vivo , while dramatic effects are observed upon etoposide treatment . This suggests that the most important function of TDP2 , following Top2 induced DNA damage at least , is related to the 5′ TDP activity of this enzyme . Additionally , our data suggest that alternative , TDP2–independent , mechanisms of DSB repair are sufficient to cope with the endogenous level of TOP2 damage arising during normal mouse development and life . A role for human TDP1 in repairing TOP2-induced DSBs was recently suggested by a weak 5′-TDP activity of human recombinant protein on DSBs possessing 4-bp 5′-overhangs , and on a mild sensitivity of TDP1−/− DT40 cells to etoposide [27] . This is also consistent with the increased resistance to etoposide reported in cells highly overexpressing TDP1 [34] , and with the reported 5′-TDP activity of Tdp1 in Saccharomyces cerevisiae [35] . However , while our standard activity assays employs DSBs with blunt-ended 5′-phosphotyrosyl termini , in the current study we similarly failed to detect residual 5′-TDP activity in Tdp2Δ1–3 MEF extracts on DSB substrates with 4-bp 5′-overhangs ( Figure 2C ) . In addition , in our hands , TDP1−/− DT40 cells are not hypersensitive to etoposide , and deletion of TDP1 in TDP2−/−/− DT40 cells does not increase sensitivity to etoposide above that observed by TDP2 deletion alone [36] . Consequently , we conclude that TDP2 is the major if not only 5′-TDP activity in mammals ( as in DT40 chicken cells ) , at physiologically relevant enzyme concentrations at least . We have shown that Tdp2-deleted mouse cells are hypersensitive to TOP2-induced DNA damage , but not to ionizing radiation , in agreement with previous results with TDP2−/−/− DT40 cells [25] . Moreover , we demonstrate that this hypersensitivity correlates with a defect in the repair of etoposide-induced DSBs , as measured by immunostaining for sites of γH2AX , which suggests that TDP2-mediated repair promotes tolerance to TOP2-induced DNA damage in mammalian cells . Remarkably , we observed that TDP2 is required for resistance to TOP2-induced DNA damage not only at the cellular level , but also at the whole-organism level . Indeed , etoposide administration in Tdp2Δ1–3 mice resulted in both increased mortality due to intestinal damage and in elevated toxicity in lymphoid tissue , established in vivo targets of etoposide [32] . TDP2 is therefore a critical factor in the cellular and physiological response to TOP2 poisons . One important result of our study was to uncover the relationship between TDP2 and the major DSB-repair pathways , NHEJ and HR . We have shown that TDP2 can convert DSBs with 5′-phosphotyrosyl termini into DSBs that are directly ligatable , and might thus be of particular utility in facilitating an error-free NHEJ pathway for repair of TOP2-induced DSBs . Several of our observations support the idea that TDP2 is a component of NHEJ . First , the contribution of TDP2 to cellular resistance to TOP2 induced DNA damage is dependent on the NHEJ machinery and independent on HR , as , with regards to etoposide sensitivity , KU70 is epistatic over TDP2 deletion in DT40 cells while an additive effect is observed when TDP2 is depleted in BRCA2-deficient human fibroblasts . Second , loss of TDP2 results in a DSB repair defect not only in G2 but also in G0/G1 , cell cycle stages in which NHEJ is the main if not only DSB repair mechanism available [26] , [30] , [37] , [38] . Third , Tdp2Δ1–3 MEFs exhibit increased levels of HR-mediated DSB repair , as measured by elevated frequencies of RAD51 foci and sister chromatid exchange in response to etoposide treatment , which is a phenotype observed in other cell lines in which NHEJ is defective [39]–[41] . Additionally , we have been unable to generate DT40 cells in which both TDP2 and XRCC3 are deleted , suggesting that loss of both TDP2 and HR-mediated DSB repair is cell lethal ( unpublished observations ) . Whilst the above observations argue strongly that TDP2 is a component of NHEJ , it is important to note that TDP-independent NHEJ mechanisms to process TOP2-linked termini most likely also exist and employ nucleases such as MRN complex , CtIP or Artemis [5] , [18]–[21] . This explains why KU70−/− DT40 cells exhibit much greater hypersensitivity to etoposide than TDP2−/−/− DT40 cells , and why Tdp2Δ1–3 MEFs still repair a significant fraction of etoposide-induced DSBs in G0/G1 ( when NHEJ is the only DSB repair pathway available ) . Whilst nuclease-mediated NHEJ can support cell survival in response to TOP2-induced DNA damage , they most likely do so at the expense of increased genetic instability . This is because the removal of sequence from 4-bp complementary 5′-overhang during NHEJ will , on the one hand , likely result in chromosome deletions , and on the other hand , increase the propensity for DSB misjoining and chromosome translocation . In contrast , HR provides an error-free pathway to repair TOP2-induced DSBs that have been processed by nucleases , by restoring any missing DNA sequence from and intact sister chromatid in S and G2 [30] , [37] , [42] . In this scenario , the increased etoposide-induced genome instability in Tdp2Δ1–3 mice , both in cultured cells from these animals and in bone marrow in vivo , likely reflects the use of TDP2–independent NHEJ in cellular contexts in which HR-mediated DSB repair is unavailable ( e . g . in cells in G0/G1 ) , or is saturated by the number of etoposide-induced DSBs . In summary , based on these and our previously published data , we suggest that TDP2 defines a novel error-free NHEJ sub-pathway that converts TOP2-linked 5′-termini into ligatable DNA termini . We suggest that this may be particularly important during G1 and in post-mitotic cells , which lack HR-mediated repair , and thus in which it may be the only mechanism for error-free DSB repair of TOP2-induced DSBs ( Figure 8 ) . The results presented here can have important implications in the treatment of cancer . Given the widespread use of TOP2 poisons in cancer therapy , and the observed hypersensitivity to TOP2 poisons of cells lacking TDP2 , our findings suggest that TDP2 could affect the response of tumour cells to chemotherapy . In this context , TDP2 expression is reportedly elevated in the majority of non-small cell lung cancer cells [43] , and mutant-p53-dependent over-expression of TDP2 has been implicated in cellular resistance to etoposide in lung cancer cells [44] . TDP2 might therefore be a valid target for overcoming tumour resistance to TOP2 poisons and/or a useful predictive biomarker for clinical response to these agents . In addition , our toxicity assays in mice and the increased genome instability in cells and in mouse bone marrow correlate well with known side effects of treatment with TOP2 poisons during cancer therapy . This raises the possibility that heterogeneity in expression levels or activity of TDP2 could be an important etiological factor both in the toxicity that accompanies chemotherapy involving TOP2 poisons [45] and on the incidence of treatment-related hematological malignancy , typically acute leukemia occurring in a relatively high proportion of patients [4] , [7] , [8] . Like other acute leukemias , therapy-related malignancies are linked to specific translocations that result in the expression of fusion proteins and contribute in some way to disease development . Intriguingly , in some cases , these translocations map to regions of preferential TOP2 cleavage , supporting a model in which the translocations arise via erroneous repair of TOP2-induced DSBs . These translocations are also surprisingly similar to those found in infant leukemia [46] , suggesting that erroneous repair of TOP2-induced DSBs may also be a source of primary malignancy . Consistent with this idea , TOP2-induced DSBs are implicated in translocations commonly associated with prostate cancer [47] . In the light of our findings , it is tempting to speculate that TDP2 activity reduces the likelihood of oncogenic translocations , by ensuring rapid and accurate repair of TOP2-induced DSBs . It is possible , however , that TDP2 might occasionally promote a translocation , by liberating a DSB that engages in erroneous DNA ligation , as might be the case in some extremely conservative rearrangements that have been reported [12] , [13] . We have shown that TDP2 protects mouse cells from the cytotoxic and clastogenic effects of TOP2 poisons , most likely by functioning in error-free pathway for NHEJ . These results have important implications in the treatment of cancer . For example , development of small molecule inhibitors for TDP2 may provide a way of sensitizing particular types of tumor to chemotherapy , though precaution is necessary to consider the possible consequences of TDP2 inhibition on normal cells and on the generation of secondary malignancies .
All animal procedures were performed in accordance with European Union legislation and with the approval of the Ethical Committee for Animal Experimentation of the University of Leuven and the University of Seville , respectively . Chicken DT40 B lymphoma cells were cultured at 39°C , 5% CO2 in RPMI 1640 medium supplemented with 10−5 M β-mercaptoethanol , penicillin , streptomycin , 10% fetal calf serum ( FCS ) , and 1% chicken serum ( Sigma ) . TDP2−/−/− cell line was previously described [25] . To generate KU70 deletion constructs , Hygromycin ( HygroR ) or Neomycin ( NeoR ) resistance cassettes were inserted between sequences of 1 . 6 kb and 3 . 3 kb in length from the KU70 locus [48] . KU70-HygroR and KU70-NeoR deletion constructs were sequentially transfected into wild-type and TDP2−/−/− cells . The gene targeting events were confirmed by Southern blot analysis of EcoRI -digested genomic DNA hybridized to an external probe ( Figure S1 ) . Transformed human fibroblast lines 1BR ( wild-type ) and HSC62 ( BRCA2-mutant ) were described previously [49] . Cells were cultured in DMEM supplemented with penicillin , streptomycin and 15% FCS . Primary MEFs were isolated from littermate embryos at day 13 p . c . and cultured at 37°C , 5% CO2 , 3% O2 in Dubelcco's Modified Eagle's Medium ( DMEM ) supplemented with penicillin , streptomycin , 10% FCS and non-essential aminoacids . All experiments were carried out between P2 and P4 . MEFs were transformed by retroviral delivery of T121 , a fragment of the SV40 large T antigen that antagonizes the three Rb family members but not p53 [50] . Transformed MEFs were maintained at 37°C , 5% CO2 in DMEM supplemented with penicillin , streptomycin and 10% FCS . A targeting construct was generated for Tdp2 in which the first three exons were flanked by loxP sites , followed by an FRT- and loxP-flanked neomycin-resistance ( neo ) cassette . These three exons encode for the N-terminal half of TDP2 and contain mapped interaction domains for e . g . TDP2 itself , CD40 and TRAF6 [23] . The Tdp2flEx1–3 , neo targeting construct was electroporated in E14 ( 129Ola ) ES cells and correctly recombined ES cell clones were confirmed by Southern blot analysis . The functionality of the loxP sites was shown in vitro by electroporation of a correctly targeted ES cell clone with a Cre-expressing vector . Several correctly targeted ES cell clones were used for aggregation with CD1 morulae and transferred into pseudo-pregnant recipient females to obtain chimaeric mice . Three chimaeric males produced heterozygous offspring after breeding with CD1 wild-type females . The obtained offspring was genotyped with both a loxP-specific and a neo-specific PCR . Intercrosses between Tdp2flEx1–3 , neo/+ mice led to the generation of homozygous floxed Tdp2 mice which were viable and fertile . To delete the critical exons we crossed the heterozygous Tdp2 mice with an EIIa-Cre mouse ( Adenovirus EIIa-promoter driven Cre ) and obtained Tdp2flΔ/+ mice . Intercrosses of the latter mice resulted in viable homozygous knockout mice ( from now on denoted Tdp2Δ1–3 ) at the normal 25% Mendelian distribution . Southern blot analysis confirmed the complete recombination of the loxP-flanked sequences in the homozygous mice and hence the generation of Tdp2 knockout mice . Labelled double-stranded 5′-phosphotyrosyl substrates were generated essentially as previously described [22] , [22 , 25] . For 5′ overhang substrates 5′-Y-P-AATTCTTCTCTTTCCAGGGCTATGT-3′ ( Midland Certified Reagents ) and 5′-AGACATAGCCCTGGAAAGAGAAG-3′ ( Sigma ) oligonucleotides were annealed . Cell and tissue extracts were prepared by mild sonication in Lysis Buffer ( 40 mM Tris–HCl , pH 7 . 5 , 100 mM NaCl , 0 . 1% Tween-20 , 1 mM DTT ) supplemented with 1 mM PMSF and protease inhibitor cocktail ( Sigma ) and clarification by centrifugation 10 min 16500 g 4°C . Protein concentration was measured with Bradford reagent ( Sigma ) . 5′-TDP reactions contained 50 nM substrate , 80 µM competitor single-stranded oligonucleotide and the indicated amount of protein extract in a total volume of 6 µl Reaction Buffer ( 50 mM Tris-Cl , pH 7 . 5 , 50 mM KCl , 1 mM MgCl2 , 1 mM DTT , 100 µg/ml BSA ) . Ligation reactions with oligos contained in addition 5 units of T4 DNA ligase ( Fermentas ) and 1 mM ATP ( Sigma ) Reactions were stopped by the addition of 3 µl 3× Formamide Loading Buffer and 5 min 95°C incubation . Samples were resolved by denaturing polyacrylamide gel electrophoresis and analysed by phosphorimaging in a Fujifilm FLA5100 device ( GE Healthcare ) . Substrates were generated by PCR-mediated amplification of plasmid pEGFP-Pem1-Ad2 [51] with primers 5′-AATTCTTCTCTTTCCAGGGCTATGT-3′ and 5′- AATTCATCCCCAGAAATGTAACTTG-3′ harbouring phosphate ( Sigma ) or phosphotyrosine ( Midland Certified Reagents ) moieties at 5′ ends . NHEJ-competent nuclear extracts were prepared as previously described [28] . Reactions were performed by incubating ( 6 h at 16°C ) 100 ng of each substrate with 7 µg of nuclear extracts in NHEJ Buffer ( 50 mM Tris-HCl pH 7 . 5 , 50 mM KCl , 1 mM DTT , 2 mM MgCl2 , 1 mM ATP , 100 µg/ml BSA ) in the presence or absence of 50 nM hTDP2 recombinant protein ( purified as previously described [22] ) . Reactions were stopped by addition of EDTA ( to a final concentration of 100 mM ) and treated 30′ with Proteinase K ( 0 . 2 mg/ml ) . DNA was purified using FavorPrep GEL/PCR Purification Mini Kit ( Favorgen ) and transformed into MegaX DH10B T1 Electrocompetent Cells ( Invitrogen ) . Positive transformants were selected by plating on LB agar plates containing kanamycin ( 25 µg/ml ) . To determine sensitivity in DT40 , cells were plated in 5 ml of medium containing 1 . 5% ( by weight ) methylcellulose ( Sigma ) in 6-well plates at 50 , 500 , and 5000 cells/well per treatment condition . Media also contained the indicated concentration of doxorubicin ( Sigma ) , mAMSA ( Sigma ) or etoposide ( Sigma ) . In all experiments , cells were incubated for 7–11 days and visible colonies were counted . Survival assays in MEFs were carried out seeding 2000 cells in 100 mm dishes , in duplicate for each experimental condition . After 6 hours , cells were irradiated or treated with the indicated concentrations of etoposide for 3 hours , washed with PBS and fresh medium was added . Cells were incubated for 10–14 days and fixed and stained for colony scoring in Crystal violet solution ( 0 . 5% Crystal violet in 20% ethanol ) . The surviving fraction at each dose was calculated by dividing the average number of visible colonies in treated versus untreated dishes . Human fibroblasts were transfected with non-targeting Negative Control and TDP2 smartpool siRNAs ( Thermo Scientific ) using HyperFect transfection reagent ( Invitrogen ) . Cells were transfected twice in two consecutive days and used for survival 48 hours after second transfection . Other details as described above . MEFs grown on coverslips for the required time , 7 days for confluency-arrested cells and 2 days for cycling cells , were treated as indicated and fixed ( 10 min in PBS-4% paraformaldehyde ) , permeabilized ( 2 min in PBS-0 . 2% Triton X-100 ) , blocked ( 30 min in PBS-5% BSA ) and incubated with the required primary antibodies ( 1–3 h in PBS-1% BSA ) . Cells were then washed ( 3×5 min in PBS-0 . 1% Tween 20 ) , incubated for 30 min with the corresponding AlexaFluor-conjugated secondary antibodies ( 1/1000 dilution in 1% BSA-PBS ) and washed again as described above . Finally , they were counterstained with DAPI ( Sigma ) and mounted in Vectashield ( Vector Labs ) . Rad51 foci scoring requires 30 sec . pre-extraction in PBS-0 . 1% Triton X-100 prior to fixation . γH2AX and Rad51 foci were manually counted ( double-blind ) in 40 cells from each experimental condition . When necessary to identify replicating cells , 5-ethynyl-2′-deoxyuridine ( EdU , Invitrogen ) was added throughout treatment and repair at a final concentration of 10 µM . Click chemistry reaction was performed before DAPI staining by incubating ( 30 min r . t . ) with 1 µM AlexaFluor-conjugated azide ( Invitrogen ) in reaction cocktail ( 100 mM TrisHCl pH 8 . 5 , 1 mM CuSO4 , 100 mM ascorbic acid ) . For the analysis of G0/G1 confluency-arrested cells only Cyclin A negative cells were scored . For G2 , as EdU was present ( 10 µM ) during and after treatment , only Cyclin A positive cells without EdU incorporation were scored ( see Figure S3 ) . Primary antibodies were used at the indicated dilution: γH2AX ( Millipore , 05-636 ) 1/1000 , Cyclin A ( Santa Cruz , sc-751 ) 1/500 , Rad51 ( Abcam , ab213 ) 1/200 and Tubulin ( Santa Cruz , sc-5286 ) 1/2500 . Micronuclei and nucleoplasmic bridges were analysed in transformed and low passage primary MEFs previously seeded onto coverslips . Following treatment , cytochalasin B ( Sigma ) was added at 4 µg/ml to transformed but not to primary MEFs . 22 h ( transformed ) or 30 h ( primary ) post-treatment , cells were fixed and subject to DAPI staining as described above . In transformed cells only binucleated cells were scored , which was confirmed by visualization of the cytoplasm with anti Tubulin immunofluorescence ( performed as described above ) . Chromosomal aberrations were scored in Giemsa stained metaphase spreads . Following treatment , recovery in fresh medium was allowed for 2 h ( transformed MEFs ) or 4 h ( primary MEFs ) and demecolcine ( Sigma ) was added at a final concentration of 0 . 2 µg/ml . Caffeine ( Sigma ) was also added at a final concentration of 0 . 1 µg/ml but only to primary cells . 4 h later cells were collected by trypsinisation , subject to hypotonic shock for 1 hour at 37°C in 0 . 3 M sodium citrate and fixed in 3∶1 methanol∶acetic acid solution . Cells were dropped onto acetic acid humidified slides and stained 20 minutes in Giemsa-modified ( Sigma ) solution ( 5% v/v in H2O ) . For SCEs 10 µM BrdU ( Sigma ) was added to the medium for two complete cycles ( approximately 48 hours ) before collection . Drug treatment was applied for 30 minutes 6–8 hours before cell collection . Metaphase spreads were obtained as described above . Before Giemsa staining , slides were incubated in Hoescht 33258 solution ( 10 µg/ml ) for 20 minutes , exposed to UV light ( 355 nm ) for 1 hour and washed for 1 hour at 60°C in 20× SCC . The mouse colony was maintained in an outbred 129Ola , CD1 and C57BL/6 background under standard housing conditions , at 21±1°C with a photoperiod of 12∶12 h ( lights on at 8:00 ) . They were housed in isolated cages with controlled ventilation trough HEPA-filters and were in flow cabins . Sterile food pellets and water were available ad libitum . Breeding pairs between heterozygotes ( Tdp2+/flΔ×Tdp2+/flΔ ) were set to obtain wild-type ( Tdp2+/+ ) and knock-out ( Tdp2Δ1–3 ) littermates for analysis . Mice were genotyped with Phire Animal Tissue Direct PCR Kit ( Thermo ) following manufacturer instructions and using primers 5′-CCTTCATTACTTCTCGTAGGTTCTGGGTC-3′ , 5′-ACCCGCTCTTCACGCTGCTTCC-3′ and 5′-TACACCGTGCCATAATGACCAAC-3′ . This results in amplification of a 429 bp fragment from the wild-type allele or 561 bp fragment from the mutant allele . At 8 weeks of age , Tdp2+/+ and Tdp2Δ1–3 mice underwent intraperitoneal injection with 3 µl/g of body weight of either DMSO ( vehicle control ) or etoposide at 25 mg/ml in DMSO for a final dose of 75 mg/kg . Weight and general health status was monitored daily from the day of injection ( inclusive ) . 6 days post-treatment mice were sacrificed by cervical dislocation and dissected for histopathological analysis . Weight of spleen and thymus was recorded prior to their histological or cell content analysis . Bone marrow ( BM ) from femurs and tibias of each mouse was also obtained . For histological analysis organs were fixed in 4% paraformaldehyde for 2 days , embedded in paraffin , cut in 6 µm slices by microtome , stained with Hematoxylin-Eosin and visualized under the microscope . For cell content analysis by FACS , BM and thymus were homogenized in EDTA Buffer ( 140 mM NaCL , 1 . 5 mM KH2PO4 , 2 . 7 mM KCl , 8 . 1 mM Na2HPO4 , 0 . 6 mM EDTA ) . Cells from both tissues were immunolabelled with the appropriate fluorescently-labelled antibodies according to manufacture's recommendations and analyzed using a FACScalibur flow cytometer ( Becton Dickinson ) : B220-APC ( 17-0452 ) , CD43 FITC ( 11-0431 ) , CD8 APC ( 17-0081 ) and CD4 FITC ( 11-0043 ) ( eBiosciences ) ; CD11b/Mac-1 PE ( 550019 ) ( Becton Dickinson ) . Data was compiled and analysed using CellQuest software ( Becton Dickinson ) . At 8 weeks of age , Tdp2+/+ and Tdp2Δ1–3 mice underwent intraperitoneal injection with 2 . 5 µl/g of body weight of either 10% DMSO ( vehicle control ) or etoposide at 400 µg/ml in 10% DMSO for a final dose of 1 mg/kg . Mice were sacrificed by cervical dislocation 24 h after injection and BM from one femur and tibia was extracted and homogenized in 3 ml FBS . Cellular content was concentrated in 150 µl FBS by centrifugation and smears were prepared on glass slides . Following 5 min fixation in methanol , slides were stained 30 min in Giemsa-modified ( Sigma ) solution ( 5% v/v in 100 mM Tris-HCl pH 6 . 8 ) and visualized under the microscope . 2000 polychromatic erythrocytes ( PCE ) were scored for the presence of micronuclei ( MN-PCE ) in each slide . | DNA double-strand breaks ( DSBs ) are dangerous because they can lead to cellular death and tissue degeneration if not repaired , or to genome rearrangements , which are a common hallmark of cancer , if repaired incorrectly . Although required for all chromosomal transitions in cells , transient DNA cleavage by topoisomerase II ( TOP2 ) is a potential endogenous source of DSBs , which are characteristic in that TOP2 remains covalently bound to the DNA termini . In addition , numerous chemotherapeutic regimes rely on compounds that “poison” TOP2 activity , stimulating the formation of DSBs that target tumour cells . However , these compounds also affect healthy tissue and confer undesirable side effects , including the stimulation of genome rearrangements that can trigger secondary malignancies ( mainly acute leukemia ) . Identifying the factors that participate in the repair of TOP2-induced DSBs and fully understanding their mechanism of action are therefore important for the design of chemotherapeutic regimes that are more effective and safer . Here we demonstrate that TDP2 , a recently identified protein that can liberate DSB termini from blocked TOP2 , functions as part of established cellular DSB repair processes and is required to safeguard genome integrity upon treatment with TOP2 poisons , both in cells and in mice . These results can therefore have important implications in cancer treatment . | [
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] | 2013 | TDP2–Dependent Non-Homologous End-Joining Protects against Topoisomerase II–Induced DNA Breaks and Genome Instability in Cells and In Vivo |
New contained semi-field cages are being developed and used to test novel vector control strategies of dengue and malaria vectors . We herein describe a new Quarantine Insectary Level-2 ( QIC-2 ) laboratory and field cages ( James Cook University Mosquito Research Facility Semi-Field System; MRF SFS ) that are being used to measure the impact of the endosymbiont Wolbachia pipientis on populations of Aedes aegypti in Cairns Australia . The MRF consists of a single QIC-2 laboratory/insectary that connects through a central corridor to two identical QIC-2 semi-field cages . The semi-field cages are constructed of two layers of 0 . 25 mm stainless steel wire mesh to prevent escape of mosquitoes and ingress of other insects . The cages are covered by an aluminum security mesh to prevent penetration of the cages by branches and other missiles in the advent of a tropical cyclone . Parts of the cage are protected from UV light and rainfall by 90% shade cloth and a vinyl cover . A wooden structure simulating the understory of a Queenslander-style house is also situated at one end of each cage . The remainder of the internal aspect of the cage is covered with mulch and potted plants to emulate a typical yard . An air conditioning system comprised of two external ACs that feed cooled , moistened air into the cage units . The air is released from the central ceiling beam from a long cloth tube that disperses the airflow and also prevents mosquitoes from escaping the cage via the AC system . Sensors located inside and outside the cage monitor ambient temperature and relative humidity , with AC controlled to match ambient conditions . Data loggers set in the cages and outside found a <2°C temperature difference . Additional security features include air curtains over exit doors , sticky traps to monitor for escaping mosquitoes between layers of the mesh , a lockable vestibule leading from the connecting corridor to the cage and from inside to outside of the insectary , and screened ( 0 . 25 mm mesh ) drains within the insectary and the cage . A set of standard operating procedures ( SOP ) has been developed to ensure that security is maintained and for enhanced surveillance for escaping mosquitoes on the JCU campus where the MRF is located . A cohort of male and female Aedes aegypti mosquitoes were released in the cage and sampled every 3–4 days to determine daily survival within the cage; log linear regression from BG-sentinel trapping collections produced an estimated daily survival of 0 . 93 and 0 . 78 for females and males , respectively . The MRF SFS allows us to test novel control strategies within a secure , contained environment . The air-conditioning system maintains conditions within the MRF cages comparable to outside ambient conditions . This cage provides a realistic transitional platform between the laboratory and the field in which to test novel control measures on quarantine level insects .
Dengue is the most abundant arboviral infection in the tropics , with 50–100 million cases and 5 million people at risk annually [1] . Currently , there is no available human vaccine , thus dengue prevention is limited to control strategies attacking the mosquito vectors Aedes aegypti and Aedes albopictus . Outside of community education programs and source reduction campaigns that seek to remove artificial containers that produce the vectors , most government programs rely upon insecticides to reduce vector populations . These methods are often inefficient , costly and ineffective . Furthermore , many populations of A . aegypti have developed physiological resistance to many pesticides , rendering them ineffective [2] . Thus , novel population control strategies are being developed to control vectors of dengue and other mosquito-borne diseases . Releases of genetically modified ( GM ) A . aegypti that are refractory to dengue infection and transmission , and the inundative release of sterile males are currently being developed to reduce populations of A . aegypti [3] , [4] . Our research group is investigating the use of strains of the endosymbiotic bacteria Wolbachia pipientis to induce life-shortening and dengue virus interference in populations of A . aegypti . The Wolbachia infection is driven to fixation in populations of A . aegypti via a cytoplasmic incompatibility mechanism . The wMelPop strain is known to shorten the life span of A . aegypti reducing the number of individuals that survive long enough to transmit dengue [5] , and can also interfere with the replication and transmission of several viruses in A . aegypti , including DENV-2 and chikungunya virus [6] . Entomopathogenic fungi are also being studied for their life-shortening impact on A . aegypti [7] . Novel control strategies require confirmation under field conditions before they can be deployed operationally . Furthermore , experiments involving population replacement methods involving GM and novel agents such as Wolbachia and fungi that are conducted out of the laboratory must be under tight containment to avoid accidental release . A cross-disciplinary scientific working group developed guidelines for testing of gene drive systems within secure flight cages [8] . These facilities , termed “semi-field system” ( SFS ) [9] , typically consist of secure biocontainment laboratory for insect rearing , secure field cage for experimental release , and associated security features such as fencing , moats and pass-coded gates . Within the cages , experimental houses or huts simulating domestic premises to be tested are featured . This is especially important for A . aegypti , a mosquito that typically feeds on humans and harbours within houses and other human premises . Natural substrates of soil , grass and native vegetation are included . Natural larval habitat such as puddles for Anopheles malaria vectors and artificial containers for Aedes are included . Biocontainment structures typically include double-door atriums , air curtains , mosquito surveillance traps , double layers of insect-proof screening , screened water drains , etc . In the tropics , facilities must often be built to withstand heavy rain and strong winds , often to tropical storm , cyclone or hurricane strength . We describe a SFS that features a biocontainment level 2 laboratory/insectary that connects directly to 2 identical Quarantine Insectary Containment level 2 ( QIC-2 ) semi-field cages . This new facility , the James Cook University Mosquito Research Facility ( MRF ) , is currently being used to investigate the impact of wMelPop and wMel strains of Wolbachia infection on survival of A . aegypti , and the dynamics of its spread within a population of wild type A . aegypti . Each of the SFS cages contains the ground floor of a simulated Queenslander house and associated yard . Queenslander houses are typically timber houses set on concrete or wooden pillars , and are common throughout much of Queensland , Australia ( http://en . wikipedia . org/wiki/Queenslander_%28architecture%29 ) . They are generally unscreened to maximise ventilation , and the ground floor is often not fully enclosed , allowing free access to mosquitoes . Dengue transmission is often most intense in suburbs dominated by these older types of housing [10] . We describe the security and containment features of the MRF , measure the environmental conditions inside and outside the cage and the impact of its climate control system , and also examine the survival of wild type A . aegypti within the cage . Finally , we provide standard operating procedures ( SOP; File S1 ) designed to prevent escape of released mosquitoes .
Human ethics approval for use of human volunteers to blood feed colony ( dengue free ) A . aegypti was obtained ( JCU Human Ethics H2250 ) . Volunteers were examined for fever before each blood feeding , excluded if feverish , and could withdraw at anytime . Written consent was obtained from all staff involved in blood feeding . The MRF was constructed to provide a simulated Cairns urban environment , under QIC-2 containment levels ( http://www . daff . gov . au/aqis/import/general-info/qap/class7/quarantine_approved_criteria_qap_class_7 . 2_quarantine_insectary_containment_level_2_qic2_facilities ) , for testing novel control strategies on A . aegypti . The MRF is built on 133 m2 of land on the Smithfield campus of James Cook University ( 16°48′58”S , 145°41′15”E ) located ca . 15 km northwest of the city of Cairns , Queensland Australia . Cairns is located in the wet tropics of northern Queensland , and has a pronounced wet and dry monsoonal climate; the mean daily temperature ranges from 21°C in winter to 27°C in summer , and an average of 1992 mm of rain falls annually ( Australian Bureau of Meteorology; http://www . bom . gov . au/index . shtml ) . Cairns has a history of dengue outbreaks [10] , [11] , and A . aegypti are present in most urban areas . The campus building site was chosen as it is practical for researchers but , more importantly , it is situated within tropical rainforest and is isolated from urban areas of Cairns where A . aegypti is common . Thus , we think that any escaping A . aegypti are highly unlikely to breed with existing populations in the Cairns region . Construction on the MRF began in March 2008 and finished in January 2009 . The cost of the facility in $AUS was $469 , 000 for the cage , $888 , 000 for the laboratory and $364 , 000 for the air conditioning system including controller . Total cost was $1 , 721 , 000; with Goods and Services Tax ( 10% ) this was $1 , 893 , 000 . Of this total , 55% was material costs , and 45% labor . The MRF design ( Figure 1 ) allowed us to provide direct and secure access between the rearing laboratory and the SFS cages . Two cages were built so that treatment and control experiments could be conducted simultaneously . A service road connects to a loading bay located near the entry to the MRF laboratory . The screening of the cages reduced incoming light and thus potential solar gain . We measured light passing through the cage layers into the SFS cages using a Extech EasyView EA30 light meter ( Extech Instruments Corporation , Waltham , MA 02451 U . S . A . ) during mid day on clear conditions . We measured temperature and RH inside and outside each cage to test the ability of the shade cloth awning and AC system to maintain ambient conditions . Data loggers ( Esis Hygrocon DS1923 , Esis Pty Ltd , PO Box 450 , Pennant Hills NSW 1715 AUSTRALIA ) were set 24 cm above ground on a 8 L plastic bucket located within the Queenslander house and in the yard in the center of each cage , and run while the AC was on and off to investigate the impact of solar gain and the AC unit on conditions within the cages . Outside , 2 data loggers were set , one under a shaded tree ca . 1 . 5 m off the ground ( equivalent to 1 . 5 m Stevenson screen height used by Bureau of Meteorology ) and the other set on a upturned bucket in a shaded area adjacent to cage A . None of the data loggers were exposed to direct sunlight that could heat the unit and provide inaccurate temperature readings . Several systems are deployed at the MRF SFS to provide security against vandalism and to minimise the accidental release of insects . The cages are surrounded by 2 m high fencing topped with barbed wire to prevent access by animals and humans . Each cage has an auto-locking door that could only be opened once the entry door into the vestibule was closed . Before being opened , the entry door activated an air curtain above the cage side door that blew air downward over the entryway . The interior also had overlapping screens composed of fine polyester Tentex 72007 cloth ( located on the vestibule side ) that had a metal chain weight sewn into the bottom to ensure the screens securely overlapped . All doors entering the laboratory are auto-locking , and keys are only available to JCU staff working on the project . The doors have all been fitted with rubber seals . In total , there are 6 doors ( 3 from cage to insectary , and 3 from insectary to external ) between each cage and the external exit of the MRF SFS . Within each vestibule entry into the cage , a BG-Sentinel trap ( BGS , Biogents GmbH , Regensburg , Germany ) [12] runs continuously and a sweepnet is provided for staff to capture any escaped mosquitoes . All drains within the cages have stainless steel basket screens ( 0 . 25 ml ) covered with fine mesh socks that are regularly inspected and cleaned . The external and internal walls of the cage are inspected for damage weekly . All supply air and return air grilles are fitted with 0 . 25 mm stainless steel mesh within the MRF facility . Fire extinguishers are located within the cages and laboratory , and fire detectors are located in the laboratory , air-conditioning system and plant rooms . The building is fitted with a “Notifier” system that automatically dials out to the fire brigade and campus security personnel in the event of a fire alarm . A set of SOPs ( File S1 ) are used to maintain surveillance and security within the MRF SFS . Extensive monitoring is conducted on the JCU campus to detect mosquitoes that may have escaped the SFS . Sticky ovitraps [13] and 4 BGS traps are also situated in buildings near the MRF , and are serviced weekly . Sticky traps consisting of 700 BGS ml red plastic cups containing a sticky panel insert are placed within the containment space between the mesh layers of each portal frame ( Figure S1 ) . If required , breaches of the cage sections can be rectified by replacement of the independently fitted double layers of 0 . 25 BGS mm stainless steel mesh . Any mosquitoes collected are identified in the laboratory , and A . aegypti are sent to University of Queensland for identification of Wolbachia infection . The presence of Wolbachia was detected by polymerase chain reaction analysis using primers specific to the wMelPop IS5 insertion sequence as described in [14] . Several times a year container surveys are conducted on the JCU campus , and potential A . aegypti larval habitat is removed or treated with S-methoprene pellets . We sampled cohorts of male and female A . aegypti released within the MRF cage to determine their preferred resting sites . Three cohorts of 120 female and 60 male pupae were allowed to emerge in the cages at two day intervals . Mosquitoes were provided with daily human blood meals and access to oviposition sites as per regular experiment procedures . Separate areas in the cages were surveyed with a Prokopack aspirator [15] 3–7 days post-emergence . The cages were divided into five sections; ( facing into the cage ) left garden , inside Queenslander , right garden , behind Queenslander and front entrance of cage , and were surveyed in that order . Three surveys were performed at around dusk , when mosquitoes were less active , and three surveys were performed in mid-morning prior to blood-feeding . The dusk collections were performed 7 , 8 , 9 days after the first release of pupae and the day collections were performed 9 , 10 and 11 days after the first release of pupae . One person ( PHJ ) performed all aspirator surveys and followed a specified route around objects ( eg , plants , light fittings , furniture , sweaty towels ) in each section . Mosquitoes were released back into the cages at the end of each survey . Data for all survey times were combined . For each cage , Fisher’s Exact Test was used to compare the total number of female and male mosquitoes captured within the Queenslander structure compared with those captured elsewhere in the cage . A cohort of known numbers of equally aged male and female Ae . aegypti were allowed to synchronously emerge in each cage to estimate daily survival within each MRF cage . Mosquitoes ( F1 obtained from populations collected from over 280 ovitraps set in suburbs across Cairns ) were reared in the MRF insectary as a single large cohort . Larvae were reared in 3 . 4 L white buckets with approximately 2 L of water ( ca . 100–150/bucket ) and fed a diet of fish food ( Tetramin ) . Temperature was maintained at 26°C with a 12∶12 photoperiod . Pupae were sexed using size as an indicator and 2500 female and 2500 male 0–24 hr old pupae were placed in buckets and allowed to emerge in each cage ( total 5000 mosquitoes per cage ) . Mosquitoes within the SFS cages were blood fed on 1–2 human volunteers for 10 min . at around 10 AM each day . Two BGS traps were set in the Queenslanders in each cage and run for 30 min . and the mean number of male and female captured Ae aegypti calculated . Samples were not returned to the cages . After 22 days , all remaining mosquitoes were captured using BGS traps and human-bait sweepnet collections . Mosquito oviposition took place in 8 ovibuckets placed in the yard area of each cage . The ovibucket consisted of a 4 litre plastic bucket filled with 2 BGS L of a 20% hay infusion; a 10×15 cm red flannel cloth strip was attached inside the bucket as an oviposition substrate . Half of the ovibuckets in each cage were changed every three days so each ovibucket was in the cage for 6 days . Daily survival rates ( DSR ) were estimated using BGS trap sample and final trap-out data . Both methods of analysis assume that mortality is independent of age and are potentially biased as BGS trap samples were not returned to the cages [16] , [17] . However , linear analyses were used for both estimates as the recapture rate was low ( overall 10–12% of the total initial population ) , survival was high and data from the first collection period was very low . Mean BGS trap collections ( +1 ) for females and males in each cage were loge transformed and fitted by linear regression against time ( day of sample day 0 to day 15 for females and day 11 for males ) . The DSR were calculated from the resulting slopes [18] . For the DSR estimate for females using log-linear regression , the first sample point on day 3 was excluded from the regression as fewer mosquitoes were collected in the BGS-trap on day 3 than on the next sample day , day 7 ( Figure 4A ) , likely due to a poor collection of teneral adult females by the BGS trap [19] on day 3 . Male DSR were estimated based on samples from day 3 to day 11 . The DSR based on the remaining number of female mosquitoes collected in each cage on day 22 was estimated by solving for p in the exponential decay equation where n is days and y is the number of mosquitoes on that day . As no males were collected in the final trap-out or in the BGS traps after day 7 ( Figure 4B ) , day 11 and 0 males were used to estimate DSR .
Ambient light entering the cage was reduced by 98–99% ( Table 1 ) , and was reduced by well over 99% within the Queenslander . Temperature and relative humidity within the cages accurately tracked ambient conditions outside the cage during the Sept 2009 period ( Figure 5 , Figure S3 ) . Indeed , the AC system appeared to reduce daily peak temperatures by about 2–3°C , suggesting that the shade cloth awning above the cage helped prevent significant solar gain within each cage . Temperature and RH were comparable between the two cages . The mean absolute difference in hourly temperature inside and outside the cage was 0 . 92 and 1 . 02°C , respectively , for cage A and B with the AC turned off; and 0 . 71 and 0 . 99°C , respectively , with the AC turned on . For RH , the mean absolute difference was 5 . 6% and 5 . 5% , respectively , for cage A and B with the AC turned off; and 2 . 9% and 4 . 8% , respectively , with the AC turned on . Temperature and RH within the Queenslander were comparable to both outside ambient and yard conditions within each SFS cage ( Table 2 ) . The level of solar gain was not high , and reflects the 99% reduction in light entering the cage . Thus , temperature did not become extreme when the AC system was off , although the AC did appear to reduce highest temperatures in the afternoon . Aberrant RH peaks within both cages during the day was caused by water from the automated sprinkler system . Long term temperatures in both cages remained comparable ( Figure S3 ) , with cage A ca . 0 . 5°C warmer than cage B . Especially hot afternoon temperatures in early February 2010 exceeded 35°C , but were nearly identical inside and outside both cages . The exterior drainage system prevented overrunning and flooding within the MRF cage due to heavy tropical rains . Indeed , no evidence of flooding within the MRF cage has been observed despite extreme rain events in excess of 300 ml within 24 hr . Overflow of interior drains from rain penetrating the cage screens has also not been observed . The soil base of the cage allows much of the storm water to percolate out of the cage rather than being flushed through the floor drains . There is no evidence of A . aegypti escaping from the MRF-SFS . Aedes aegypti were occasionally captured in the BGS traps and sticky ovitraps located on the JCU campus . From February to June , 2009 a total of 47 ( 30 female and 17 male ) A . aegypti were collected in 4 BGS traps from February to May 2009 . During this time 14 , 800 non-infected and 48 , 000 Wolbachia-infected A . aegypti had been released in the cages . But none of the A . aegypti collected from the external traps was positive for Wolbachia by PCR assay . Whilst the absence of Wolbachia does not preclude the possibility that these mosquitoes escaped from the SFS cages , an alternative source of the mosquitoes was usually located . For example , the majority ( 25/47 ) of the A . aegypti were captured in one fortnight in a BGS trap located near an A . aegypti field bioassay from which adult mosquitoes had inadvertently escaped . Also , A . aegypti had been detected on campus before the cages were operational . Mosquito trapping and inspections detected larvae in potted plant bases , drain sumps and tyres . Although these sites were treated , breeding may have persisted . Unwanted arthropods , such as millipedes , phorid flies , ants and some spiders , were observed in the SFS cages . These were probably introduced before screening of the cages was completed , and may also have been entered the cages from contaminated mulch or ornamental plants . Many remained in the cages despite the steam-cleaning of the mulch . Most arthropod populations were self-limiting while spiders and their webs were removed by hand . Ants may been present in the site soil or tunnelled beneath fencing and ratwalls into the cages . These were subsequently controlled by placing ant baits containing AmdroTM ( 0 . 73% hydramethylnon ) within protective plastic petri dishes inside and outside each cage . A few geckoes ( the exotic Hemidactylus frenatus ( Dumeril and Bibron ) ) that probably invaded the Queenslander before the cages were screened were also found in each cage . These were removed by hand or by spraying with DettolTM ( active ingredient Chloroxylenol ( 4-chloro-3 , 5-dimethylphenol ) ) . Whilst the use of Dettol is not an approved method for removing geckos , it was the only effective one available . Spraying Dettol at the gecko would cause it to jump off the wall onto the floor rather than running to a crevice in the Queenslander wall or ceiling . Once on the floor the gecko could be quickly caught and killed by freezing . For cage A , significantly more A . aegypti females and males were collected inside the Queenslander structure compared with all the other areas of the cage ( Fisher’s exact test , p = 0 . 02 ) . This was less apparent in Cage B where similar number of females were captured in the Queenslander , ( Fisher’s exact test , p = 0 . 32 ) , but fewer mosquitoes were collected overall ( Figure 6 ) . The mean numbers of female and male A . aegypti collected in the BGS traps was consistent between the two cages ( Figure 4; Figure S2 ) . The day 22 trap-out collected 1 , 073 and 880 females from Cage A and Cage B , respectively; no males were collected from either cage . Estimated daily survival rate of females was similar for both cages across both estimation methods , ranging from 0 . 92–0 . 96 ( Table 3 ) . The DSRs for males were much lower , but there was nearly a 30% difference between estimates from the two methods , perhaps owing to different termination days .
The MRF provides a secure insectary for the production of mosquitoes and replicate quarantine level 2 SFS cages for conducting of experimental releases . The temperature and relative humidity within both SFS cages closely tracks ambient conditions outside the cages . We had fears that solar gain within the cages would result in high daytime temperatures that could be lethal to mosquitoes . Temperatures over 50°C were reported within the SFS in Tanzania , Africa [9] . However , the Tanzanian SFS had no AC system , and no protective awning to reduce solar gain . Our AC system was able to help maintain mean daily maximum temperatures within 1–2°C of external ambient ( Figure 5 , Figure S3 ) . The multiple layers of shade cloth and screening reduced incident light within the cage by 98–99% ( Table 1 ) . This , coupled with ventilation facilitated by the void of 20–30 cm void beneath the shade cloth , ensured that the cage did not heat up appreciably when the AC was turned off ( Figure 5 ) . Temperature and relative humidity within the SFS Queenslander were similar to those recorded in the SFS yard , but light incidence was considerably reduced . Temperature and RH with in the SFS Queenslander are comparable to those occurring within a typical well-ventilated Queenslander house . Data loggers set from 1–8 Dec . 2007 in three rooms within a Queenslander house in Cairns demonstrated that average temperature was within 1°C of external Stevenson screen height temperature ( S . Ritchie , unpublished data ) . However , cooler , high-humidity microclimates did exist in sheltered , moist areas such bathroom and laundry . The moist towels placed in the Queenslander within our SFS would have also provided a cooler , high humidity microclimate . A simple awning system also minimised solar gain and excessive temperature within in two smaller cages ( 7 m×6 m×4 m high ) near the MRF-SFS . These cages were built of 0 . 25 ml Tentex polyester covered with a 0 . 2 m elevated 90% shade cloth awning . The mean maximum daily temperature ( from 10 Feb . – 1 Mar 2010 using data loggers set 0 . 24 above ground ) in these cages was only 0 . 44°C and 0 . 17°C higher than ambient ( J . Darbro , unpublished data ) . Thus , data from both the MRF-SFS and the adjacent small cages indicate that a simple elevated awning of shade cloth will provide shade and ventilation , preventing high solar gain and extreme temperatures within the cage . This would be a cheaper alternative to air conditioning units . Aspirator collections within the SFS cages indicated that most mosquitoes harboured within the Queenslander structure . Furthermore , we do not observe large numbers of mosquitoes resting on the cage walls . These observations indicate that the MRF SFS simulates a typical north Queensland urban environment for A . aegypti . Daily survival rate of female A . aegypti was quite high within the MRF SFS . Estimated daily survival rate of 0 . 92–0 . 96 was obtained from a released cohort of females A aegypti . Male DSR was much lower , ( ca . 0 . 5–0 . 78 ) , suggested that they died from starvation due to a lack of food or feeding . Either the flowering plants available in the cage were not suitable , or the males spent less time feeding compared with other behaviours such as mating . However , the high DSR estimates for females may be unrealistic high . Certainly mortality from predation , insecticide exposure and desiccation during prolonged flights are minimised within the cage . Female mosquitoes also had ready and easy access to a blood source ( volunteer blood feeders were available every day ) and oviposition sites , and thus were likely to expend less energy in searching for hosts or oviposition sites than wild mosquitoes . We acknowledge that the MRF SFS has limitations . Due to the high construction costs , we were limited to only two SFS cages . Thus , experimental replication will be minimal , requiring multiple sequential experiments in some instances . These experiments could be further complicated by seasonal differences between sequential runs . Environmental conditions within the SFS cage are also different from the natural urban environment . While temperature and RH were comparable to external ambient conditions ( Table 2 , Figure 5 ) , the screening and shade cloth greatly reduced light and wind within the cage , and the limited space within the cage would may have greatly restricted flight activity . These could impact mosquito survival and the potential infection by agents such as Wolbachia . Thus , results from SFS experiments must be interpreted with caution , especially regarding extrapolation to field conditions . The MRF SFS is a highly secure environment . No Wolbachia-infected A . aegypti have been detected outside the SFS . Adult mosquitoes would have to escape through a double layer of 0 . 25 mm stainless steel , limiting these events to a breech of the containment by damage to the structure by flying tree branches , sabotage or vehicular collision . Barring a breech of the cage screening , a mosquito would have to fly through 6 secure locked doors to escape . Both are highly unlikely events . Adult mosquitoes could oviposit in free water or even mulch within the cage . However , all drains have secure 0 . 25 mm mesh baskets that would contain larvae . The oviposition buckets are the only source of free-standing water in the SFS cage . Thus , larvae hatched from eggs laid on mulch and other wet areas would not develop into adults . Nonetheless , care must be taken to eliminate free standing water in areas like plant axils and drains . In some instances regulatory bodies may require that genetic material not leave the SFS . Water from drainage and direct contact with the soil could allow for transfer of genetic material in our cage without the escape of living mosquito eggs , larvae or adults . A sealed concrete foundation , together with collection of waste water , would have to be used to prevent this . Finally , insects and other animals entered the cage in some instances . Most invaded the cage before it was screened , entered in mulch and plants brought into the cage or may have burrowed from the soil . Care must be taken to ensure contamination is minimal , and harmful mosquito predators , such as ants and geckoes , are eliminated . Nonetheless , contained SFS cages offer excellent opportunity to conduct research on insects . The secure environment prevents release of quarantine insects; to date , no Wolbachia-infected A . aegypti have been detected in surveillance traps on the JCU campus . The cage allows for the release of cohorts of known numbers . Thus , the direct impact of a control measure can be estimated by comparing changes in population between control and treatment cages . This approach as been used to study the impact of pesticides , repellents and parasite-vector interactions ( for a review see Ferguson et al . [9] ) . Cohort cage studies can also be used to study the behaviour of mosquitoes [9] , and to estimate the relative efficacy of traps [20] . The MRF SFS could also be used to conduct insecticide and repellent evaluations under controlled semi-field conditions without the ethical dilemma of disease risk . We hope to investigate the impact of competing oviposition containers on efficacy of ovitraps such as sticky ovitraps and lethal ovitraps . Furthermore , detailed studies on A . aegypti behaviour , such as the microclimate of preferred harbourage sites , can be conducted on released cohorts within the Queenslander structure . While we have not established populations within the cage , we believe it would be relatively easy to do so as has been done with Anopheles [9] . For our studies with Wolbachia , we will be able to observe the rate of Wolbachia invasion within a population of wild A . aegypti . These studies will measure the penetration of Wolbachia within wild A . aegypti after simultaneous release of known ratios of Wolbachia-infected and uninfected A . aegypti . This will occur over several generations and be used to estimate the time to fixation We are currently conducting invasion experiments using the wMelPop and wMel strains . | Novel vector control strategies require validation in the field before they can be widely accepted . Semi-field system ( SFS ) containment facilities are an intermediate step between laboratory and field trials that offer a safe , controlled environment that replicates field conditions . We developed a SFS laboratory and cage complex that simulates an urban house and yard , which is the primary habitat for Aedes aegypti , the mosquito vector of dengue in Cairns Australia . The SFS consists of a Quarantine Insectary Level-2 ( QIC-2 ) laboratory , containing 3 constant temperature rooms , that is connected to two QIS-2 cages for housing released mosquitoes . Each cage contains the understory of a “Queenslander” timber house and associated yard . An automated air conditioning system keeps temperature and humidity to within 1°C and 5% RH of ambient conditions , respectively . Survival of released A . aegypti was high , especially for females . We are currently using the SFS to investigate the invasion of strains of Wolbachia within populations of A . aegypti . | [
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] | 2011 | A Secure Semi-Field System for the Study of Aedes aegypti |
Seed development in angiosperms is dependent on the interplay among different transcriptional programs operating in the embryo , the endosperm , and the maternally-derived seed coat . In angiosperms , the embryo and the endosperm are products of double fertilization during which the two pollen sperm cells fuse with the egg cell and the central cell of the female gametophyte . In Arabidopsis , analyses of mutants in the cell-cycle regulator CYCLIN DEPENDENT KINASE A;1 ( CKDA;1 ) have revealed the importance of a paternal genome for the effective development of the endosperm and ultimately the seed . Here we have exploited cdka;1 fertilization as a novel tool for the identification of seed regulators and factors involved in parent-of-origin–specific regulation during seed development . We have generated genome-wide transcription profiles of cdka;1 fertilized seeds and identified approximately 600 genes that are downregulated in the absence of a paternal genome . Among those , AGAMOUS-LIKE ( AGL ) genes encoding Type-I MADS-box transcription factors were significantly overrepresented . Here , AGL36 was chosen for an in-depth study and shown to be imprinted . We demonstrate that AGL36 parent-of-origin–dependent expression is controlled by the activity of METHYLTRANSFERASE1 ( MET1 ) maintenance DNA methyltransferase and DEMETER ( DME ) DNA glycosylase . Interestingly , our data also show that the active maternal allele of AGL36 is regulated throughout endosperm development by components of the FIS Polycomb Repressive Complex 2 ( PRC2 ) , revealing a new type of dual epigenetic regulation in seeds .
Seed development is a tightly regulated process that is controlled , both before and after fertilization and requires tight coordination of parental gene expression [1] . A paradigm for the importance of balanced parental contribution is the observation that certain genes in the developing offspring of flowering plants are exclusively or preferentially expressed from only one of the two parental genomes , a phenomenon called genomic imprinting that has also been observed in mammals [2] , [3] . The relevance of parent-of-origin effects was first found in interploidy crosses [4] . Typically , an increase in the paternal genome results in larger seeds , while the opposite is observed if the maternal gene dosage is higher than normal [5] . This is in agreement with the parental conflict theory , which implies that fathers direct maximal amount of maternal resources to their own offspring and thereby promote growth . Mothers on the other hand would seek to distribute the resources equally among all their offspring , and balance their resource between themselves and their offspring . Thus , maternal factors are thought to dampen growth [6] . In mammals , imprinted genes are often involved in growth control [7]–[10] . In Arabidopsis , the endosperm is the major tissue regulating the flow of nutrients to the embryo , and is therefore a likely site for parent-of-origin dependent gene expression . Imprinting results from differences in epigenetic marks , involving DNA methylation and post-translational modifications of histones on the parental alleles [11] , [12] . Trimethylation of lysine 27 on histone H3 ( H3K27me3 ) leading to repression of gene expression , has been found to be a particularly important imprinting mechanism in plants . In Arabidopsis seeds , H3K27me3 mark is set by the FIS Polycomb Repressive Complex 2 ( PRC2 ) , which consists of at least four components; the histone methyltransferase MEDEA ( MEA ) , FERTILIZATION INDEPENDENT SEED 2 ( FIS2 ) , FERTILIZATION INDEPENDENT ENDOSPERM ( FIE ) , and MULTICOPY SUPPRESSOR OF IRA 1 ( MSI1 ) . The corresponding genes were identified in screens for autonomous endosperm development , indicating that the FIS complex acts as a repressor of endosperm development prior to fertilization [13]–[17] . An equally important regulatory mechanism in imprinting is DNA methylation resulting from the activity of several different methyltransferase enzymes , where each has specificity for cytosine ( C ) in certain sequence contexts . So far , imprinting has been shown to be under the influence of MET1 , the major Arabidopsis maintenance DNA methyltransferase involved in CG-methylation [11] , [18]–[20] . DNA demethylation can be achieved either by a passive process i . e . the repression of MET1 expression [21] , [22] , or by an active mechanism involving DNA glycosylase enzymes such as DME [23] . Several lines of evidence show that DME , which is expressed in the central cell of the female gametophyte , is necessary for maternal-specific gene expression in the endosperm [11] , [18] , [19] , [24] . So far , only about a dozen genes in Arabidopsis have been identified to have parental-specific gene expression , and they illustrate different modes of imprinting [3] . MEA , ARABIDOPSIS FORMIN HOMOLOGUE 5 ( AtFH5 ) and PHERES 1 ( PHE1 ) are imprinted by the action of FIS PRC2 , where only the latter is paternally expressed [13] , [25]–[31] . FIS2 , FLOWERING WAGENINGEN ( FWA ) and MATERNALLY EXPRESSED PAB C-TERMINAL ( MPC ) are all maternally expressed and regulated by the dual action of MET1 and DME [11] , [19] , [24] , [32]–[34] . Recently , five novel imprinted genes , HOMEODOMAIN GLABROUS 3 ( HDG3 ) , HDG8 , HDG9 , At5g62110 and ATMYB3R2 were identified by differential DNA methylation in embryo and endosperm [35] . In comparison to Arabidopsis , more than 100 genes have been shown to have a uniparental or preferential parental expression pattern in mammals [36]–[39] . This suggests that additional genes in Arabidopsis are imprinted . Furthermore , the low number of known imprinted genes in plants precludes the identification of general principles in this kind of gene expression control and thus , the identification of further imprinted genes is pivotal . Moreover , the targets of imprinted genes , as well as genomic pathways and regulatory modules influenced by imprinted genes are largely unknown . Here , we have designed a microarray strategy for the identification of seed regulators by exploiting the cdka;1 mutation . Using this approach , we have identified a cluster of previously uncharacterized AGAMOUS-LIKE ( AGL ) Type-I MADS-box transcription factors that are downregulated in endosperm with no paternal contribution . Here , we report that AGL36 is imprinted by the dual action of MET1 and DME . In addition , AGL36 is regulated throughout endosperm development in its maternal expression cycle by the Polycomb FIS-complex , thereby identifying a novel mode of regulation for imprinted genes .
Here we have used cdka;1 as a tool to identify factors sensitive to the vital parental gene balance in the endosperm . In heterozygous cdka;1 mutants , the second pollen mitosis is either missing or is severely delayed . However , mutant pollen can successfully fertilize the egg cell while leaving the central cell unfertilized [40] , [41] . A detailed analysis by Aw and colleagues has revealed that a second sperm cell is delivered to the central cell , but that karyogamy does not take place [42] . Although not properly fertilized , the majority of the central cells in cdka;1 fertilized ovules ( 70–90% ) are triggered to initiate endosperm proliferation [40] , [42] , [43] . Thus , fertilization by cdka;1 sperm cells creates a unique situation where endosperm initially develops without any paternal contribution ( in the following also referred to as cdka;1P ) . The endosperm , however remains under-developed , and ultimately the seed aborts , further demonstrating the importance of the paternal contribution to the endosperm for proper seed development . Since activation of maternal alleles by loss of maternal FIS PRC2 could rescue seed lethality [43] , we hypothesized that the disturbance of parental gene balance in the endosperm is the main cause leading to developmental arrest of cdka;1P at 3–4 days after pollination ( DAP ) . To identify factors and mechanisms sensitive to such an imbalance in gene dosage in the endosperm and with that likely key regulators of seed development , we performed microarray transcript profiling of cdka;1 fertilized seeds at 3 DAP ( Figure S1A ) . Due to the heterozygous nature of the cdka;1 mutant line used , a transcript that is absent in cdka;1p seeds will lead to a reduction of maximal 50% in the genome profiling experiment . For example , genes that are only expressed from the paternal genome would show such reduced expression levels ( Figure S1B ) . Likewise , maternally expressed genes that require activation by a paternally expressed gene ( s ) would be downregulated ( Figure S1C ) , whereas genes that are acted upon by paternally expressed repressors were expected to be upregulated in the microarray screen ( Figure S1D ) . When we compared the transcriptional profiles of Ler x cdka;1 versus Ler x Col seeds 3 DAP , we detected 17223 nuclear genes that were expressed in all biological replicates of both mutant ( cdka;1 set ) and wild-type ( WT set ) seed profiles . Our result is in good agreement with a set of genes identified by Goldberg & Harada laboratories ( GH ) in globular stage seeds of Arabidopsis Ws-0 plants as 68% of our genes were also identified by GH , and our gene set included >90% of the GH globular seed gene set ( Figure 1A; http://seedgenenetwork . net , [44] ) . To further validate the quality of our dataset , we examined the expression pattern of genes known to be preferentially expressed from the paternal allele . To date , only three genes have been identified that show a predominant paternal expression pattern; PHE1 , HDG3 and At5g62110 , where all three genes were found to be downregulated in our arrays ( Figure S1E ) , supporting our working hypothesis that paternally expressed genes can be detected amongst downregulated genes . In addition , out of seven imprinted maternally expressed genes present in our microarray sets , four were also detected as downregulated ( Figure S1E ) . This could reflect required activation by paternal factors ( Figure S1C ) , or be a result of more complex deregulation in response to change in gene dosage . To exclude array artifacts we tested all down-regulated genes by means of real-time PCR and could confirm their deregulation ( Figure 1B ) . Due to the background noise in the microarray experiment , modest but reproducible downregulation of arithmetic ratios ( ar ) ranging from 0 . 5 to 1 . 0 will produce False Discovery Rates ( FDR , see materials and methods ) with insignificant q values . Since the absence of paternally expressed genes was the simplest hypothesis to account for downregulation , we defined a functional limit for screening purposes that allowed us to detect two out of three known paternally expressed genes in the array . Both PHE1 and HDG3 are detected at q values of 0 . 35 and a downregulation cutoff of 0 . 8 ( ar ) . Consequently these values were chosen and used to filter the microarray data . Using these criteria , a set of 602 genes was extracted ( q≤0 . 35 and ar ≤0 . 8 ) , subsequently called Down 0 . 8 . For upregulation , we worked with two gene sets . For the first set , Up 1 . 2 , we used parameters equivalent to the downregulated set ( q≤0 . 35 and ar ≥1 . 2 ) , which resulted in a set of 1030 genes . For the second set , Up 1 . 5 , resulting in 323 genes , we chose ar ≥1 . 5 , a threshold for deregulation commonly used in genome-wide expression studies ( Table S3 ) . To test whether the deregulated genes could preferentially be attributed to a certain seed structure , we compared our data to gene sets expressed in different seed regions and compartments of globular stage seeds using data generated by Goldberg & Harada ( GH ) laboratories available at http://seedgenenetwork . net [44] . The overlap between the upregulated gene sets and the GH embryo , seed coat and endosperm was significantly lower than expected for independent sets of genes , indicating that among the upregulated genes we preferentially find those that are below the detection limit of the GH analyses . However looking at the downregulated genes , the picture was different . While we found slightly less overlap than expected by chance for the GH embryo set , the overlap was clearly larger than expected by chance for GH seed-coat ( 1 . 2<2 . 7e−07 ) and even more significant for the GH endosperm ( rf = 1 . 3 , p<2 . 0e−13 , Figure S2A , S2B ) . In order to functionally classify the deregulated gene sets according to their molecular functions we used the GO Slim classification system ( Figure 1C ) . Only for the GO Slim term “Transcription factor activity” we find a higher percentage and significant over-representation of both up- and down-regulated groups when compared to all genes on the array/all genes expressed . Since key regulators of seed development are likely to be transcription factors ( TF ) , we analyzed this class in detail . When comparing the fraction of deregulated genes among the different TF families , the Mγ MADS-box transcription factors clearly stood out with more than 60% of the seed expressed members being downregulated in Ler x cdka;1 arrays ( Figure S3A , S3B ) . We therefore focused on this MADS Type-I class for further analysis . Searches in publically available expression databases ( www . genevestigator . com , Figure S4 ) revealed that all identified genes were exclusively expressed in the seed and predominantly in the endosperm . From the identified Type-I Mγ MADS-box genes , we selected AGL36 for further in depth analysis ( Figure S4 ) . AGL36 was the previously undescribed Mγ candidate that interacted with the highest number of described AGLs in a Y2H screen performed by de Folter et al [45] . Both AGL36 and PHE1 have been shown to interact with AGL62 , which plays a major role in endosperm development [45] , [46] . Within the Mγ class , AGL36 clusters together with AGL34 and AGL90 [47] , which are both also detected as downregulated in our microarray experiment ( Figure S4 ) . AGL36 shares 85 . 7% and 84% nucleotide identity with AGL34 and AGL90 , respectively ( Figure S8 ) . On the amino acid level this results in of 80 . 2% similarity of AGL36 with AGL34 and 83 . 9% similarity with AGL90 . Real-time PCR measurement of AGL36 relative expression level three days after pollination ( 3 DAP ) in Ler ovules fertilized with either Col or cdka;1 pollen confirmed that AGL36 expression was reduced in cdka;1 fertilized seeds , ( 27% when normalized towards ACT11 , and 36% when normalized towards GAPA ) compared to wild-type seeds ( Figure 2A ) . To determine whether AGL36 has parental-specific expression , we took advantage of an AGL36 Single Nucleotide Polymorphism ( SNP ) existing between the Col and Ler ecotypes . This SNP allows the PCR product of Col cDNA to be digested by AlwNI , leaving the Ler cDNA PCR product intact ( Figure 2B ) . We performed reciprocal crosses between Col and Ler ecotypes , and analyzed the digested RT-PCR fragments on an Agilent Bioanalyzer Lab-on-a-Chip , allowing accurate measurement of fragment sizes and their concentrations . When Colmaternal is crossed with Lerpaternal , we only detected the Col bands ( 165 bp+234 bp ) after AlwNI digestion , indicating only maternal expression ( Figure 2C ) . Similarly , in the reciprocal cross when Lermaternal is fertilized with Colpaternal pollen , the cDNA PCR digest resulted only in an undigested band ( 399 bp ) originating from Ler , indicative of maternal expression ( Figure 2C ) . This testified that AGL36 was only expressed from the maternal genome after fertilization and thus identified as a novel imprinted gene . AGL36 expression level in wild-type seeds ( Ler x Col ) at different stages of seed development was monitored over a period of 12 days after pollination . Initially , a low expression level was detected ( 1 DAP ) , followed by a rapid increase and subsequent peak in AGL36 expression at 4 DAP , when the embryo is at the late globular stage of development , before declining ( Figure 3A ) . At the embryo heart stage , corresponding to 6 DAP , AGL36 expression had decreased to similar levels as 1 DAP . To address whether AGL36 imprinting is maintained throughout its expression cycle , we performed a SNP analysis of the RT-PCR product obtained from Ler x Col crosses harvested during 1 to 12 DAP ( Figure 3B ) . We found that AGL36 expression is originating from the maternal genome ( Ler ) throughout the experiment . By plotting the molarities of the maternal band obtained by Agilent Bioanalyzer , an expression profile closely identical to the pattern obtained in the real-time PCR analysis was found ( Figure 3C ) . To rule out that the observed maternal expression is due to expression of AGL36 in the ovule integument , which is a maternal tissue , we generated a reporter construct consisting of 1752 bp of the AGL36 promoter region fused to a GUS reporter ( pAGL36::GUS ) ( Figure 4A ) . Single-copy lines carrying this construct were used in reciprocal crosses with wild-type Ler and Col plants to examine GUS expression at 3 and 6 DAP . When inherited maternally , pAGL36::GUS expression in the seed was indeed found to be restricted only to the fertilization product ( Figure 4B , Figure S7D ) . In the reciprocal cross , when pAGL36::GUS was inherited from the paternal genome , no GUS expression was detected , ( Figure 4C , Figure S7E ) . Consistent with the SNP analysis , this demonstrated that AGL36 was imprinted and only maternally active throughout its expression cycle . Furthermore , the 1 . 7 Kb promoter fragment used in this analysis appears to be sufficient to confer parent-of-origin specific expression of the reporter . To further investigate the biological function of AGL36 , we screened the Koncz T-DNA collection for insertions [48] . We identified a mutant line , agl36-1 , harboring a single T-DNA insertion 16 bp upstream of the AGL36 ATG start codon ( Figure S5A ) . The agl36-1 line showed Mendelian segregation of the T-DNA insertion , as 75% of the plants were resistant to Hygromycin ( N = 1025 , χ2 = 0 , 83 , Table S1 ) . To test the transmission through the male and female gametes directly , reciprocal crosses of both hemizygous and homozygous agl36-1 mutant plants with wild-type plants were performed ( Table S1 ) . In a reciprocal cross , a hemizygous mutant will segregate 50% of the T-DNA resistance marker if the disrupted gene is not vital for gametophyte transmission or function . Thus , gametophyte requirement can be scored directly as reduced frequency of resistant plants [49] . In reciprocal crosses with agl36-1 , no transmission distortion through female or male gametophytes could be observed ( N = 661 , χ2 = 0 , 13 and N = 1015 , χ2 = 0 , 00 respectively , Table S1 ) . The position of the T-DNA insertion in agl36-1 predicts AGL36 expression failure , and indeed real-time PCR analyses of 3 DAP seeds of homozygous agl36-1−/− plants compared to Col wild-type indicate a 1000-fold AGL36 downregulation in the mutant seeds ( Figure S5B ) . In line with an imprinted and maternal-only expression of AGL36 , close to 50% reduction of the transcript level was observed in 3 DAP hemizygous agl36-1+/− seeds ( Figure S5B ) . We thereby concluded that agl36-1 represents a loss-of-function allele of AGL36 . Although depletion of AGL36 did not interfere with the fitness of the mutant allele in our experimental system , we have shown that AGL36 is specifically expressed from the maternal allele in the fertilization product , in a time frame between 2 and 6 DAP . To investigate whether this was reflected morphologically or developmentally in the developing seed , we compared embryo and endosperm development in wild-type and homozygous agl36-1−/− seeds within the AGL36 expression time frame . After fertilization of the egg and the central cell , the endosperm in Arabidopsis undergoes three syncytial rounds of nuclear divisions before the first asymmetric division of the zygote that creates the apical embryo proper and the basal suspensor that connects the embryo proper and the maternal tissue ( Figure S5C ) . At the 2 DAP stage , no obvious difference could be observed between wild-type and agl36-1−/− seeds , both typically harboring a 1–2 cell embryo proper and a 16–32 nucleated endosperm ( Figure S5C , left section ) . The embryo continues to divide through radial , longitudinal and transverse divisions to produce the so-called globular stage at 4 DAP ( Figure S5C , middle section ) . The endosperm also undergoes 3–4 syncytial nuclear divisions and remains uncellularized as cell proliferation at the upper half of the embryo forms the cotyledon primordia at the so-called heart stage at 6 DAP ( Figure S5C , right section ) . Although the main AGL36 expression peak occurs during this time frame , no obvious deviation between wild-type and agl36-1−/− could be observed at these stages . Similarly , using an endosperm specific pFIS2::GUS reporter [33] , a wild-type endosperm division pattern was observed in agl36-1+/− seeds ( Figure S5D ) . The majority of imprinted , maternally expressed genes identified in Arabidopsis so far have been shown to be paternally silenced by mechanisms involving symmetric CG methylation , maintained by MET1 [11] , [18] , [19] . Although not directly linked to imprinting , methylation can also be directed by CHROMOMETHYLASE 3 ( CMT3 ) that has specificity for CNG , and members of the DOMAINS REARRANGED METHYLTRANSFERASE ( DRM ) family; DRM1 and DRM2 , that are mainly responsible for asymmetric CHH methylation [50] . In order to address the involvement of DNA methylation in the regulation of paternal AGL36 expression , we performed SNP analyses of 3 DAP ovules from reciprocal crosses with mutants that have been shown to be involved in DNA methylation . In the SNP RT-PCR analysis of mutant pollen crossed to wild-type , paternal AGL36 expression is expected if the tested mutants are involved in AGL36 imprinting . CMT3 DNA methylation has been reported to be guided to specific sites by KRYPTONITE ( KYP ) H3K9 methylation [51] . When mutant cmt3-7 and kyp-2 pollen were crossed to Col wild-type plants , no difference in AGL36 expression was observed ( Figure 5A ) . In the reciprocal cross with cmt3-7 also no difference could be detected compared to wild-type expression ( Figure S6 ) . DRM1 and DRM2 are mainly responsible for asymmetric DNA CHH methylation [50] and rely on small interfering RNAs , processed by ARGONAUTE4 ( AGO4 ) , for target template guidance [52] . In our assays , fertilization by pollen lacking DRM1;DRM2 and pollen lacking AGO4 had no effect on the AGL36 expression pattern ( Figure 5A ) . Likewise , AGL36 expression in the reciprocal cross was identical to wild-type ( Figure S6 ) . DECREASE IN DNA METHYLATION1 ( DDM1 ) is involved in maintenance of DNA methylation [53] . In our SNP RT-PCR analyses where mutant ddm1-2 pollen was used to fertilize wild-type ovules , paternal AGL36 expression was not activated ( Figure 5A ) . In summary , CMT3 , KYP , DRM1;DRM2 , AGO4 and DDM1 appear not to be involved in the establishment nor maintenance of AGL36 imprinting ( Figure 5A , Figure S6 ) . However , paternal AGL36 expression was detected when plants hemizygous for the met1-4 mutation were used as pollen donor in crosses with wild-type Ler ( Figure 5B ) . In the reciprocal cross , using met1+/− as the maternal partner , no AGL36 expression from the paternal genome could be observed ( Figure 5B ) . Furthermore , we performed crosses using pollen from homozygous met1-4 parents . When first generation homozygous met1 plants were used as pollen donor on wild-type plants , prominent AGL36 expression from the paternal Col genome could be observed ( Figure 5B ) . This strongly suggests that the repression of the paternal copy of AGL36 is lifted due to the met1-4 mutation , and that MET1 is required for maintaining paternal inactivation of AGL36 . In the reciprocal crosses , only expression from the maternal genome could be detected , both in the heterozygous and the homozygous met1-4 situation , further substantiating the requirement of MET1 in the male germline in order to maintain AGL36 imprinting ( Figure 5B ) . Maternal AGL36 expression levels using homozygous met1-4 as the maternal cross partner appeared to be equal to maternal levels in the reciprocal crosses ( Figure 5B ) . This opens for the interpretation that DNA methylation is not required for the regulation of maternal AGL36 expression . In public expression databases , AGL36 is reported to be expressed in the seed and more precisely in the endosperm [54] ( Figure S4 ) . In order to monitor AGL36 expression in vegetative tissues and its dependence on DNA methylation , we performed a real-time PCR experiment on vegetative tissues from reciprocal Ler x Col crosses and homozygous met1-4 tissues . In biological replicates of progenies from both reciprocal crosses , weak AGL36 expression ranging from 1–6% of the seed expression level could be detected in seedlings , leaves and flowers ( Figure 6A ) . This showed that AGL36 was expressed throughout the plant life cycle , although at very low levels . In the same experiment , we monitored expression in met1-4 tissues . AGL36 expression levels were 50–90-fold higher in met1-4 leaves compared to seed expression levels ( Figure 6A ) . In a direct comparison , expression levels were elevated 2000-fold in homozygous met1-4 leaves compared to wild-type Col x Ler leaves ( Figure 6B ) . In flowers , the upregulation was more than 20-fold in met1-4 compared to wild-type Col x Ler flowers ( Figure 6C ) . In conclusion , these data showed that silencing of AGL36 in vegetative tissues involves MET1 , suggesting that the absence of maintenance DNA methylation elevates vegetative AGL36 expression beyond the maternal expression levels found in seeds . In order to investigate the parental expression pattern of AGL36 in vegetative tissues , we performed SNP analyses of flowers from F1 hybrids of Ler and Col reciprocal crosses . In both reciprocal crosses , AGL36 appeared to be expressed equally from the parental Ler and Col genomes , indicating biparental expression in flowers ( Figure 6D ) . This indicates that parental-specific expression , i . e . imprinting of AGL36 , as expected , only takes place in the seed and that a low basal biparental expression is present throughout the plant life cycle . Interestingly , biallelic expression in flowers suggests that further silencing of AGL36 takes place in the male germline before uniparental expression in the seed ( Figure 6D ) . According to our data , the action of MET1 suppresses AGL36 expression throughout the vegetative phase and this suppression is maintained in the fertilization product through the male germline . AGL36 imprinting thus requires specific activation of the maternal allele . DNA demethylation by DME has previously been shown to mediate maternal-specific gene expression in the endosperm [11] , [18] , [19] , [24] , and we therefore investigated AGL36 expression in dme-6 mutant plants . Since dme cannot be maintained in a homozygous state , we harvested siliques of dme-6+/− heterozygous plants pollinated with Col pollen at 3 and 6 DAP . We monitored the relative expression by means of real-time PCR using FWA and FIS2 as controls . At 3 DAP , both controls were downregulated by 69±0 . 09% and 53±0 . 30% respectively ( Figure 6E ) , in line with a lack of functional DME in 50% of the seeds in heterozygous dme-6+/− plants . AGL36 was downregulated in a similar manner as FIS2 ( 41±0 . 20% ) , suggesting that DME is indeed involved in early activation of the maternal AGL36 allele . We also tested the expression of FWA and FIS2 in 6 DAP samples and found that their downregulation were sustained as predicted ( Figure 6E ) . However , to our surprise AGL36 expression in dme-6+/− seeds was elevated more than 50-fold ( Figure 6E ) . This result was unexpected , and implicated a more intricate regulation of AGL36 . DME is required for the activation of MEA , the core histone H3K27 methyltransferase ( HMTase ) of the PRC2 FIS-complex [46] , [55] , [56] . To determine whether PRC2 FIS is involved in the regulation of AGL36 , we analyzed the relative expression of AGL36 over time ( 1 to 12 DAP ) in mea mutant seeds compared to wild-type ( Figure 7A ) . While AGL36 expression in wild-type seeds was at its maximum at 4 DAP , we observed that AGL36 expression in mea seeds surpassed the maximum levels of wild-type at 4 DAP , and reached its highest levels at around 6 DAP . At this point , the AGL36 relative expression in mea mutant seeds was approximately 40-fold higher than wild-type expression at the same stage , and 7-fold higher than the maximum AGL36 level found in wild-type seeds at 4 DAP ( Figure 7A ) . Our data thus indicate that the FIS-complex is indeed a repressor of AGL36 expression , and could also explain the elevated AGL36 expression level in 3 DAP dme-6+/− seeds ( Figure 6E ) . In line with these findings , we found highly elevated AGL36 relative expression levels in mutant seeds from three different mutant alleles of mea ( Figure 7C ) . Similar results were also obtained with mutants of other components of the FIS PRC2 complex ( FIS2 , FIE and MSI1 , data not shown ) . To investigate whether FIS activity was exerted on the maternal and/or paternal allele of AGL36 , we performed SNP analyses on the RT-PCR product of AGL36 obtained from mea mutant plants ( in Ler background ) pollinated with Col wild-type pollen . We found that AGL36 is expressed only from its maternal allele in the mea background throughout the duration of our experiment ( Figure 7B ) . In comparison to the expression pattern in wild-type ( Figure 3B ) , strong ectopic maternal expression was also observed at 9 and 12 DAP stages . No paternal expression could be observed in these stages . By plotting the molarities of the maternal band detected by the Agilent Bioanalyzer , an expression profile for the maternal allele could be generated ( Figure 7B , lower panel ) . This demonstrated that in the absence of MEA , AGL36 expression continues to increase after 4 DAP , and although the intensity decreases from 6 DAP , high level of AGL36 is maintained at 12 DAP . Hence , the FIS-complex represses the maternal allele of AGL36 after the 3 DAP stage . To further substantiate that maternal AGL36 expression is regulated by the maternal action of MEA , we crossed mea mutant plants with pollen expressing the pAGL36::GUS reporter line . Here , no obvious activation of the paternal transgene could be observed at 3 DAP ( Figure S7A ) . Surprisingly , at 6 DAP , corresponding to embryo heart stage , weak expression of the paternal copy in the embryo could be found ( Figure S7A ) . In addition , we performed reciprocal crosses with the pAGL36::GUS reporter line in mutant mea background . When the transgene was contributed from the female side in mea background , a GUS signal was found in 3 DAP stages that increased drastically up to 6 DAP ( Figure S7B ) . In the reciprocal cross however , no expression could be observed ( Figure S7C ) . The E ( z ) class of H3K27 histone methyltransferases ( HMTases ) in Arabidopsis consists of MEA , SWINGER ( SWN ) and CURLY LEAF ( CLF ) that participate in different PRC2 complexes . To test whether AGL36 repression is a specific function of FISMEA PRC2 , we analyzed AGL36 expression in homozygous swn-4 and clf-2 seeds . For mutants of both HMTases values similar to the wild-type situation were found , and in conclusion AGL36 appear to be specifically regulated by FISMEA PRC2 ( Figure 7C ) . In summary , maternal AGL36 expression appears to be repressed specifically by the maternal action of FIS PRC2 . For all genes known to be imprinted by PRC2 , the FIS-complex is involved in the repression of the silenced allele [25]-[27] , [30] , [56] . Our data suggest that silencing of the paternal AGL36 allele requires MET1 whereas the maternal allele is activated by DME . Modulation of female AGL36 expression by PRC2 thus represents a novel mechanism in this type of gene expression system , and adds an additional level of parent-of-origin specific gene expression to the scheme . In order to investigate if this regulation applies to other genes imprinted by the dual action of MET1/DME [11] , [18] , [19] , we analyzed the relative expression levels of FWA , FIS2 , AGL36 and MPC in a mea mutant . At 3 DAP expression levels were unchanged or slightly downregulated ( 0 . 40–0 . 99 ) for all genes tested ( Figure 7D ) . However , while the expression of FWA and FIS2 remained stable at 6 DAP , AGL36 and MPC levels were elevated up to 80-fold ( Figure 7D ) . Thus , genes imprinted by means of MET1/DME can be divided in two classes based on their dependence of FIS PRC2 for additional regulation of the expressed allele . Whereas one class appears not to be regulated by FIS PRC2 , the other class depends on the action of the FIS-complex for developmental regulation of its expression .
Here , we report that AGL36 is a novel imprinted gene that is only expressed from its maternal allele in the endosperm . Silencing of the paternal allele requires the action of MET1 , as paternal expression is restored in met1 mutants . In public high-density DNA methylation maps prepared from wild-type seedlings ( http://signal . salk . edu ) , both the AGL36 transcribed region and the 5′and 3′regulatory regions are decorated by CG methylation . In line with this , AGL36 was expressed at very low levels in vegetative tissues . Transcript levels however , were highly elevated in the absence of MET1 , in accordance with the virtual absence of CG methylation in met1 mutants ( http://signal . salk . edu ) [67] . AGL36 is expressed from both parental alleles at low levels in vegetative tissues , which show that AGL36 imprinting occurs specifically in the endosperm . Other imprinted genes in Arabidopsis have been shown to have biallelic expression in the embryo and other vegetative tissues [11] , [34] , [68] . However , for most imprinted genes this issue is not clarified [3] . Since paternal AGL36 expression is absent in the seed , it suggests that further silencing of AGL36 takes place by entry into the male germline . Moreover , silencing in the female germline must be lifted to allow AGL36 expression in the seed . Alternatively , maintenance methylation and further silencing do not take place on the AGL36 gene in the female gametophyte . The majority of previously described imprinted genes are regulated by a dual switch of methylation and demethylation involving MET1 and DME [11] , [18]–[20] , [35] . Here we have shown that AGL36 expression is reduced in a dme mutant , indicating that DME has an activating function towards AGL36 . In accordance with this , mutants of CMT3 , KYP , AGO4 , DDM1 and DRM1/2 had no effect on paternal AGL36 expression suggesting that maintenance and repression by MET1 and activation by DME is sufficient for AGL36 imprinting . In our SNP analyses , a weak paternal signal was observed only at the 2DAP stage . This was interpreted as an artifact since the signal was absent both before and after this stage . If this is a real paternal signal , it suggests an alternative hypothesis where silencing is achieved in the endosperm post fertilization . Further analyses are however required to support this . In two recent studies , the genome-wide methylation profile of the seed was dissected by comparing cytosine methylation in wild-type embryos to wild-type and dme endosperm . This showed that endosperm development , and hence the activity of endosperm-specific genes , is marked by an extensive demethylation of the maternal genome , especially at specific transposon sequences [35] , [69] . According to the Zilberman Lab Genome Browser ( http://dzlab . pmb . berkeley . edu/browser/ ) , such demethylation indeed takes place in the 5′and 3′regulatory regions of AGL36 . Methylation patterns are regained in the dme mutant , supporting our data that AGL36 is maternally activated through the action of DME . In an elegant approach by Gehring and colleagues , novel imprinted genes have recently been identified by the prediction of Differentially Methylated Regions ( DMRs ) between embryo and endosperm . In support of our findings , significant DMRs were also mapped to 5′and 3′region regions of AGL36 [35] . Imprinting could be demonstrated in transgenic pAGL36::GUS seeds , thus indicating that the 1752 bp promoter fragment used is sufficient for parent-of-origin specific expression . The genomic environment of imprinted genes is highly correlated with transposable elements ( TE ) , and imprinting has been postulated to be an evolutionary byproduct of silencing of invading transposons [23] , [69] , [70] . For instance , methylation of a SINE-related tandem repeat structure in the 5′-region correlates with FWA expression [32] , [71] , and DMRs in MEA , PHE1 , HDG3 and HDG9 map to TE [35] . In line with this , a variety of remnants of TE reside in both the 5′and 3′ regulatory regions of AGL36 ( Figure 4A ) . The 1752 bp pAGL36::GUS promoter fragment harbors remnants of Helitrons and parts of an Arnoldy TE . An 800 bp DMR maps immediately ( 78 bp ) upstream of the AGL36 transcriptional start site overlapping the Helitron TEs ( [35] , Mary Gehring , personal communication ) . Clearly , the 1752 bp 5′region is sufficient for basal AGL36 imprinting , and similar to the abovementioned examples , AGL36 DMRs map to TE . Further investigations will be needed to elucidate the role and the mechanisms of additional 5′and 3′DMRs as well as the involvement of small RNAs in AGL36 imprinting [72] . Distinct from the expression pattern of AGL36 that subsides at the time of cellularization in wild-type seeds , AGL36 maternal expression in mea mutant seeds was highly elevated and sustained throughout seed development . Recently , Walia et al . also reported AGL36 upregulation obtained in five days old seeds from selfed fis2+/− plants [65] . Our results show that FIS-complex mediated repression acts exclusively on the expression of the maternal allele of AGL36 . The paternal allele was efficiently silenced throughout endosperm development . Surprisingly , weak paternal pAGL36::GUS expression could be observed in 6 DAP early heart stage embryos when the mother was homozygous for mea . MEA has been shown to have biallelic expression in the embryo [28] , and thus the observed paternal expression in hemizygous mea embryos is not caused by the lack of functional MEA . This could hint to dosage-dependent regulation of paternal AGL36 expression by MEA , directly or indirectly , but in lack of further experiments this remains speculation . Different PRC2 complexes can regulate common genes [30] . However , in mutants of CLF and SWN , the paralogues of MEA , no significant effect on AGL36 expression levels was found , indicating that AGL36 regulation is specific to PRC2FIS . H3K27 trimethylation mediates PRC2s repressive function , and in a whole-genome assay for H3K27 methylation more than 4400 target genes were detected [73] ( Daniel Bouyer , personal communication ) . AGL36 was however not part of this set of genes . Since this material was obtained from seedlings and may not reflect the situation in the seed , it is not known whether AGL36 is a direct target of H3K27 trimethylation . Repression of the maternal AGL36 allele identifies a novel means of dual epigenetic regulation of imprinted genes . In this scenario , the expressed maternal AGL36 allele is antagonistically activated by DME and repressed by PRC2FIS . To our knowledge , this is the first report of an imprinted gene where the expressed allele is concurrently regulated by a repressive epigenetic mark . We asked whether this type of regulation was specific for AGL36 by investigating the fis mutant for expression of three other imprinted genes that are activated by DME . We found that these genes fall into two distinct groups; FWA and FIS2 which were largely unaffected by the lack of FIS , and MPC along with AGL36 which showed strong upregulation . This suggests that additional PRC2 regulation of DME-activated alleles defines a common mechanism that applies to a subset of imprinted genes . In Arabidopsis , three imprinted genes , MEA , PHE1 and AtFH5 are known to have their silenced allele repressed by PRC2FIS , and two of these genes , MEA and PHE1 are additionally regulated by DNA methylation [55] . In these cases however , the repressed allele is silenced by PRC2 whereas the active allele is regulated by DNA methylation [74] . Here , we show that AGL36 defines a novel type of regulation where the same allele is activated by DME and repressed by PRC2FIS in a sequential fashion . This suggests that maternal AGL36 expression after DME activation needs to be dampened and developmentally regulated by PRC2FIS , in accordance with the strong AGL36 expression observed in hypomethylated met1−/− plants . Interestingly , DME is required to activate both PRC2MEA and AGL36 , and is thus a key player in developmental tuning of parent-of-origin specific AGL36 expression . AGL36 was identified in our transcript profiling as a downregulated gene when the paternal contribution to the endosperm was absent . A simple hypothesis to account for this regulation would be that AGL36 is under the control of one or more paternally expressed factor ( s ) that activate the maternal allele of AGL36 . The identity of such factor ( s ) remain unknown , and was not approached in this work , but a simple prediction from this hypothesis is that AGL36 would be upregulated in paternal excess interploidy crosses . In a recent report , AGL36 is indeed upregulated in such crosses , as well as in crosses with unreduced diploid jas pollen [66] . Such parental cross-talk is however likely to involve complex genetic and epigenetic regulatory mechanisms , and the mechanism that cause the observed transcriptional response of AGL36 and other previously described imprinted genes in cdka;1p seeds remains to be clarified . In our study , we have shown that AGL36 is only maternally expressed . Our current model suggests that the paternal allele is silenced by the action of MET1 and the maternal allele activated by DME ( Figure 8 ) . In addition , we have also shown that PRC2FIS regulates the expression of the maternal AGL36 allele at the transition between proliferation and cellularization ( Figure 8 ) . Although AGL36 is identified as a novel target of the imprinting machinery in Arabidopsis , we have limited knowledge about its function during plant and seed development . Since expression of AGL36 and its interacting partners coincide with the transition of endosperm from proliferation to differentiation , we speculate that it plays an important role in this process . This is in agreement with recent findings [65] , showing that suppression of an AGL cluster including AGL36 is critical for successful transition of endosperm from syncytial to cellularized stage . In this work we have identified a novel imprinted gene that is controlled by a novel type of dual epigenetic regulation in the seed . This underscores the importance of further investigations to identify imprinted genes in order to unravel the complex network of epigenetic regulation of parent-of-origin effects in seed development .
All plant lines used in these experiments were obtained from the Nottingham Arabidopsis Stock Centre ( NASC ) unless otherwise stated . The mutant lines cdka;1-1 ( SALK_106809; [40] , [41] ) , ddm1-2 ( a kind gift from E . Richards; [53] ) , dme-6 ( GK-252E03-014577; Figure S9 ) , mea-8 ( SAIL_55_C04; [75] ) , mea-9 ( SAIL_724_E07; Figure S9 ) , met1-4; ( SAIL_809_E03; [76] and swn-4 ( SALK_109121; [77] ) were in the Col accession . The mutant lines ago4-1 ( N6364; [78] ) , clf-2 ( N290; [79] ) , cmt3-7 ( N6365; [80] ) , fis1 ( a kind gift from A . Chaudhury; [14] ) and kyp-2 ( N6367; [51] were in the Ler accession . The drm1;drm2 ( N6366; [81] ) line was in the Ws-2 accession . Mutants used in this study were genotyped using gene-specific and T-DNA specific primers as described in Table S2 . The ddm1-2 mutant line was genotyped by an allele-specific PCR test using dCAPS primers DDM1f and ddm1-2Rsa , as described by [68] , allowing digestion of the PCR fragment of the ddme1-2 allele with RsaI restriction endonuclease , generating a ∼130 bp band . We obtained the agl36-1 allele from the Koncz collection [48] . Allele-specific PCR , using the primers HOOK1 ( left border T-DNA primer ) and AGL36-AS2-KONCZ ( genomic AGL36 primer ) , was carried out to verify the T-DNA insertion , followed by sequencing analysis of the PCR product using the HOOK1 primer . The left border of the insertion was verified to be 16 bp upstream of the ATG start codon of AGL36 . In addition , there is an 11 bp long DNA filler located between the genomic sequence and the T-DNA sequence . Arabidopsis seeds were surface-sterilized using EtOH , bleach and Tween20 prior to plating out on MS-2 plates [82] supplemented with 2% Sucrose , containing the correct selection when necessary . Seeds on the MS-2 plates were stratified at 4°C O . N before they were incubated for 14 days at 18°C to germinate . The seedlings were then put on soil and grown in long day conditions ( 16 hr light ) at 18°C . To increase tissue specificity , siliques were cut open and seeds were isolated directly in tubes containing pre-chilled ceramic beads ( Roche MagNA Lyser Green Beads ) . Isolated tissues were stored at −80°C . Homogenization was performed by the addition of Lysis buffer containing β-ME ( Sigma Spectrum Plant Total RNA Kit ) directly to the samples , followed by 3×15 second intervals of homogenization using a MagNA Lyser Instrument ( Roche ) . To prevent RNA degradation , samples were chilled on ice two minutes between each homogenization interval . After the last homogenization step , the samples were centrifuged at 4°C for one minute prior to the transfer of the lysate to a new 1 . 5 ml tube . RNA extraction was performed according to the Sigma Plant Total RNA Kit protocol , except that all centrifugation steps were done at 4°C and not at room temperature as indicated in the protocol . RNA was eluted in 50 µl volume . cDNA was synthesized by first preparing the RNA for real-time PCR by treatment with DNase I ( Sigma ) followed by Reverse Transcription using Oligo ( dT ) and SuperScript III Reverse Transcriptase ( Invitrogen ) according to the manufacturer's protocols . The synthesized cDNA was purified utilizing QIAquick PCR Purification kit ( Qiagen ) and eluted in 30 µl volume prior to measurement of cDNA concentration using a NanoDrop 1000 Spectrophotometer . Plants were grown and seeds isolated as described above . Total RNA was isolated as described above . For microarray analysis , three biological replicas were generated , each consisting of approximately 35 hand-pollinated siliques from ten different plants . The microarray experiment was conducted by the NARC Microarray Service in Trondheim . Microarray slides were printed by the Norwegian Microarray Consortium ( Trondheim , Norway ) . A custom made Arabidopsis chip with 32567 unique 70-mer oligo probes was used in the experiments . Total RNA ( 15 mg ) and Super-Script III reverse transcriptase ( Invitrogen ) were used in a reverse transcription reaction . A 3DNA Array 350 kit with Cy3- and Cy5-labelled dendrimers ( Genisphere Inc . ) was used for labeling . Hybridizations were performed in a Slide Booster Hybridization Station ( Advalytix ) , and the slides were washed according to the manufacturers' descriptions ( Genisphere and Advalytix ) . The slides were scanned at 10 mm resolution on a G2505B Agilent DNA microarray scanner ( Agilent Technologies ) . The resulting image files were processed using GenePix 5 . 1 software ( Axon Instruments ) . Spots identified as not found or manually flagged out as bad were filtered out . Spots with more than 50% saturated pixels were also excluded . The data sets were log-transformed and normalized using the print-tip Loess approach [83] . Within-array replicated measurements for the same gene were merged by taking the average between the replicates . The data were then scaled so that all array data sets had the same median absolute deviation . The differentially expressed genes were identified using the Limma software package [84] . The resulting set of p-values were used to compute the q-values as described [85] . The microarray data generated in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE24809 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE24809 ) . We defined the following sub-sets for our microarray data ( see Table S3 ) : All expressed = all genes having a present call ( 17223 genes ) ; Down 0 . 8 = in Ler x cdka;1 downregulated genes with q≤0 . 35 and arithmetic ratio ( ar ) ≤0 . 8 ( 602 genes ) ; Up 1 . 5 = in Ler x cdka;1 upregulated genes with q≤0 . 35 and ar ≥1 . 5 ( 323 genes ) ; Up 1 . 2 = in Ler x cdka;1 upregulated genes with q≤0 . 35 and ar ≥1 . 2 ( 1030 genes ) . The q-value is the false discovery rate ( FDR ) of the p-value , and was adjusted with Storey's q-procedure [85] . The threshold for analysis was set to q≤0 . 35 since this value detected paternally expressed genes at an arithmetic ratio ( ar ) ≤0 . 8 . A functional classification was done at http://www . arabidopsis . org/tools/bulk/go/ using the GO-Slim Molecular Function classification system . For the detailed transcription factor analysis we used the Transcription Factor ( TF ) classification from the Arabidopsis transcription factor database ( AtTFDB ) hosted on the Arabidopsis Gene Regulatory Information Server ( AGRIS , http://arabidopsis . med . ohio-state . edu/AtTFDB ) . The MADS TFs were sub-grouped as in de Folter et al [45] . We compared our microarray data with seed expression data generated by the Goldberg & Harada laboratories , available at http://seedgenenetwork . net/analyze ? project=Arabidopsis . For data comparison a reference set of genes was used that contained all genes covered by the Operon chip used in our study ( Arabidopsis thaliana 34K NARC serie 8; GEO Platform GPL11051GPL ) and the Affimetrix chip used by Goldberg & Harada ( Ath1 , GEO Platform GPL198 ) . For the Ath1 chip we used the annotation provided by Goldberg & Harada available at http://seedgenenetwork . net/media/Arab_Final_Annotations_09-07-07_completed . txt . For the operon chip we used the current TAIR 9 . 0 annotation . From these annotations all AGIs for nuclear genes were extracted and the overlap was calculated . This reference set contained 22130 genes . We used the reference set overlap of the following Goldberg/Harada datasets for comparison: GH seed = call all present and experiment in Arabidopsis ATH1 Array/Arabidopsis/Globular Stage/Seed; GH seed coat = call all present and experiment in Arabidopsis ATH1 Array/Arabidopsis/Globular Stage/Chalazal Seed Coat or Arabidopsis ATH1 Array/Arabidopsis/Globular Stage/General Seed Coat; GH endosperm = call all present and experiment in Arabidopsis ATH1 Array/Arabidopsis/Globular Stage/Chalazal Endosperm or Arabidopsis ATH1 Array/Arabidopsis/Globular Stage/Micropylar Endosperm or Arabidopsis ATH1 Array/Arabidopsis/Globular Stage/Peripheral Endosperm; GH embryo = call all present and experiment in Arabidopsis ATH1 Array/Arabidopsis/Globular Stage/Embryo Proper . Venn diagrams were generated using the VENN diagram generator designed by Tim Hulsen at http://www . cmbi . ru . nl/cdd/biovenn/ [86] . The test for statistical significance of the overlap between two groups of genes was calculated by using software provided by Jim Lund accessible at http://elegans . uky . edu/MA/progs/overlap_stats . html . To generate the pAGL36::GUS construct we utilized the Gateway cloning technology ( Gateway; Invitrogen ) . The promoter region ( ÷1740–12 ) spanning the ATG start codon was amplified using the attB sequence containing primers attB1-pAGL36-AS7 and attB2-pAGL36-S4 ( Table S2 ) , and cloned into the pMDC162 GUS-vector [87] . The resulting construct , after checking the DNA sequence , was introduced to Col ecotype by Agrobacterium tumefaciens mediated transformation using the floral-dip method [88] . Histochemical assays were performed after a modified protocol from Grini et al . ( 2002 ) by incubating the tissues in staining buffer ( 2 mM X-Gluc; 50 mM NaPO4 , pH 7 . 2; 2 mM K4Fe ( CN ) 6 x 3H2O; 2 mM K3Fe ( CN ) 6; 0 . 1% Triton ) overnight at 37°C before the reaction was terminated using 50% EtOH . The tissues were cleared and mounted on slides according to Grini et al . ( 2002 ) , and inspected using an Axioplan 2 Carl Zeiss Microscope . Images were acquired with an AxioCam HRc Carl Zeiss camera and processed with AxioVs40 V 4 . 5 . 0 . 0 software . Real-time PCR was performed using a Light-cycler LC480 instrument ( Roche ) according to the manufacturer's protocol . To ensure high PCR efficiency and to avoid undesired primer dimers , all oligonucleotide pairs were initially tested by melting point analysis using SYBR Premix Ex Taq ( TaKaRa ) . To obtain higher level of gene specificity , probe-based real-time PCR with confirmed primers were performed using Universal Probe Library ( UPL ) hydrolysis probes ( Roche ) in combination with Premix Ex Taq ( TaKaRa ) . For AGL36 real-time PCR , we used primers AGL36-160-LP and AGL36-160-RP , which gave a 60 bp amplicon ( Table S2 ) . Comparison of the sequences of the coding region and the 3′UTR of AGL36 with AGL34 and AGL90 , revealed more than 85% and 84% sequence similarity respectively between these genes ( Figure S8 ) . To ensure that the abovementioned primers are only amplifying AGL36 , we cloned the obtained amplicon from four independent reactions into the pCR2 . 1 vector ( Invitrogen ) , and subsequently sequenced two clones of each construct with M13-Forward and M13-Reverse primers . Sequence results revealed exclusive and specific AGL36 amplification . ACTIN11 ( ACT11 ) , a housekeeping gene that is strongly expressed in the developing ovules [89] , was shown in a preliminary analysis not to be affected by our experimental conditions ( data not shown ) , and was therefore selected as a reference gene . GLYCERALDEHYDE-3-P DEHYDROGENASE A-SUBUNIT ( GAPA ) was used as an additional reference gene . The oligo sequences , their amplicons and appropriate UPL probes are shown in Table S2 . Real-time PCR of all samples and reference controls were performed in two independent biological replicates and repeated at least two times ( technical replicas ) unless otherwise stated . The PCR efficiency was determined independently for all replicates ( biological and technical ) by series of dilutions ( 100 ng , 50 ng , 20 ng , 5 ng template/rxn ) for each experiment . This allowed us to obtain the efficiency for each single reaction . Calculations of relative expression ratios were performed according to a model described by Pfaffl [90] with minor exceptions . Since we had efficiency for all reactions ( four values for each calculation corresponding to Etarget-sample , Etarget-standard , Ereference-sample and Ereference-standard ) , we calculated the average Etarget and Ereference values from the standards and the samples , ending up with two E-values that we could use in the formula described by Pfaffl . RNA was isolated and cDNA synthesized and purified as described above . Polymorphisms between various ecotypes were identified using TAIR Genome Browser ( www . arabidopsis . org ) and/or the Arabidopsis SNP Sequence Viewer tool provided by the Salk Institute Genomic Analysis Laboratory ( http://natural . salk . edu/cgi-bin/snp . cgi ) . A selected region spanning the SNP of interest was amplified by PCR using TaKaRa Ex Taq DNA polymerase applying 100 ng template per reaction , and the following PCR parameters in a 50 µl reaction: 94°C-3 min , 35× ( 94°C-1 min , 58°C-30 sec , 72°C-1 min/kb ) , 72°C-5 min , 4°C-∞ . Parental-specific expression based on SNP was determined by setting up an appropriate restriction digest . For AGL36 SNP analysis , 20 µl of the SNP PCR reaction was digested with 15 U of AlwNI at 37°C for a duration of 2 . 5 hrs , followed by a 20 min inactivation at 65°C . For the FWA control SNP , due to the absence of a restriction site in the SNP region in both Col and Ler ecotypes , dCAPS primers were used , generating a NheI restriction site in the Col ecotype . The obtained amplicons for both ecotypes were digested with NheI [11] . In cases where the detected SNP did not result in digestion in neither ecotype , a primer sequence was designed to introduce a base exchange adjacent to the SNP , leading to restriction digestion of one of the ecotypes . The obtained amplicon for both ecotypes were then treated with the appropriate restriction enzyme . In all experiments either genomic DNA or cDNA from wild-type plants from both ecotypes used in the study was used as controls for the presence or absence of digestion . The digested samples were analyzed using DNA-1000-LabOnChip and 2100 Bioanalyzer ( Agilent Technologies ) . To rule out that the primers used for AGL36 SNP PCR ( AGL36-SP7-SNP and AGL36-ASP6-SNP ) ( Table S2 ) would amplify the highly similar AGL90 , we oriented the AGL36-SP7-SNP primer such that it was located in a region that was annotated as intron in AGL90 but not in AGL36 ( Figure S8 ) . First , the presence of the intron in AGL90 was confirmed by amplifying the intron-flanking region ( AGL90-SP1-subcloning and AGL90-ASP2-subcloning primers ( Table S2 ) ) , and comparing the size differences obtained between the genomic PCR and cDNA PCR . Due to high sequence similarity , we suspected to amplify both AGL36 and AGL90 in these PCR reactions . To distinguish between these two amplicons , we took advantage of the presence of two unique restriction sites ( MslI and BspBI ) in the amplified region of AGL36 that are absent in AGL90 . Sequence comparison between the abovementioned AGL36-SNP primers and AGL34 showed that there was approximately 70% and 91% sequence similarity between the primers and the AGL34 gene . However , if these primers were functional in amplifying AGL34 , they would result in a smaller amplicon than AGL36 amplicon ( 373 bp versus 399 bp respectively ) . This difference could easily be detected using a DNA-1000-LabOnChip . Our SNP data only showed the expected 399 bp band , verifying that AGL34 was not amplified using the above primers . The paternally imprinted FWA gene was used as a positive control by utilizing primers FWA-RTf and FWA-dNheI ( Table S2 ) for PCR amplification followed by NheI restriction digest . | Seeds of flowering plants consist of three different organisms that develop in parallel . In contrast to animals , a double fertilization event takes place in plants , producing two fertilization products , the embryo and the endosperm . Imprinting , the parent-of-origin–specific expression of genes , typically takes place in the mammalian placenta and in the plant endosperm . A prevailing hypothesis predicts that a parental tug-of-war on the allocation of available recourses to the developing progeny has led to the evolution of imprinting systems where genes expressed from the mother dampen growth whereas genes expressed from the father are growth enhancers . The number of imprinted genes identified in plants is low compared to mammals , and this precludes the elucidation of the epigenetic mechanisms responsible for this specialized expression system . Here , we have used genome-wide transcript profiling of endosperm without paternal contribution to identify seed regulators and , among these , imprinted genes . We identified a cluster of downregulated MADS-box transcription factors , including AGL36 , that was subsequently shown to be imprinted by an epigenetic mechanism involving the DNA methylase MET1 and the glycosylase DME . In addition , the expression of the active AGL36 allele was dampened by the FIS Polycomb Repressive Complex , identifying a novel mode of regulation of imprinted genes . | [
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"Results",
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] | 2011 | Genome-Wide Transcript Profiling of Endosperm without Paternal Contribution Identifies Parent-of-Origin–Dependent Regulation of AGAMOUS-LIKE36 |
The catalytic activity of GDP/GTP exchange factors ( GEFs ) is considered critical to maintain the typically high activity of Rho GTPases found in cancer cells . However , the large number of them has made it difficult to pinpoint those playing proactive , nonredundant roles in tumors . In this work , we have investigated whether GEFs of the Vav subfamily exert such specific roles in skin cancer . Using genetically engineered mice , we show here that Vav2 and Vav3 favor cooperatively the initiation and promotion phases of skin tumors . Transcriptomal profiling and signaling experiments indicate such function is linked to the engagement of , and subsequent participation in , keratinocyte-based autocrine/paracrine programs that promote epidermal proliferation and recruitment of pro-inflammatory cells . This is a pathology-restricted mechanism because the loss of Vav proteins does not cause alterations in epidermal homeostasis . These results reveal a previously unknown Rho GEF-dependent pro-tumorigenic mechanism that influences the biology of cancer cells and their microenvironment . They also suggest that anti-Vav therapies may be of potential interest in skin tumor prevention and/or treatment .
Rho guanosine nucleotide exchange factors ( Rho GEFs ) promote the transition of Rho family GTP hydrolases ( GTPases ) from the inactive ( GDP bound ) to the active ( GTP bound ) state during signal transduction [1] , [2] . These enzymes can be subdivided into the Dbl-homology ( DH ) and the Dedicator of Cytokinesis ( Dock ) families based on the catalytic domain utilized for the GDP/GTP exchange reaction . A common feature of these two families is their extreme diversity because , in mammals , they are composed of 54 and 11 members , respectively . These family members vary widely in terms of catalytic specificity , presence of regulatory and effector domains , mechanism of activation , and expression patterns . As a consequence , they are key elements to adapt the activation of Rho GTPases to specific cell types , membrane receptors , or subcellular localizations [1] , [2] . Rho GEFs have been traditionally regarded as important for tumorigenesis and , thereby , as potential drug targets [3] . However , the large number of Rho GEFs and their regulatory complexity have made it difficult to identify which ones were the most important for the development and/or progression of specific tumors . Inferences from sequencing data have not been useful in this case , because their genes seldom undergo mutations in cancer cells [3] . The use of animal models in this functional context has been also rather limited . However , the few studies available do support the idea that these enzymes have pro-tumorigenic functions . Thus , the T-cell lymphoma invasion and metastasis-inducing protein 1 ( Tiam1 , ID number: 21844 ) has been shown to be important for both cutaneous squamous and colorectal [4] , [5] tumors . The adenomatous polyposis coli-stimulated exchange factor 1 ( Asef1 , ID number: 226970 ) and Asef2 ( ID number: 219140 ) proteins have been linked to colorectal cancer [6] . Finally , Vav3 ( ID number: 57257 ) and phosphatidylinositol 3 , 4 , 5-triphosphate-dependent Rac exchanger 1 ( P-Rex1 , ID number: 277360 ) are involved in the development of p190Brc/Abl-driven acute lymphoblastic leukemia and melanoma , respectively [7] , [8] . Vav proteins exemplify well the complexity existing in the large Rho GEF family . Thus , this subfamily has three members in vertebrates ( Vav1 [ID number: 22324] , Vav2 [ID number: 22325] , Vav3 ) that display overlapping but not identical expression patterns . They all share similar structures that encompass a complex array of regulatory , catalytic , and protein–protein interaction domains [9] . These domains enable them to interact with and become activated by receptors with either intrinsic or associated tyrosine kinase activity , activate GDP/GTP exchange on Rho GTPases , and in addition , engage parallel routes in GTPase-independent manners [9]–[17] . The physiological role of Vav proteins in the immune , nervous , and cardiovascular systems are now well established thanks to the use of genetically engineered mice [9] , [18]–[25] . By contrast , the genetic analysis of the role of these proteins in cancer has been restricted so far to acute lymphoblastic leukemia and polyomavirus middle T-antigen-induced breast cancer . These studies have revealed that Vav3 and Vav2 plus Vav3 were required for the development of each of those tumors , respectively [7] , [17] . In the present work , we aimed at expanding the spectrum of Vav family-dependent tumors by focusing our attention on cutaneous squamous tumors ( CSTs ) , the second most frequent type of skin cancer worldwide [26] . To this end , we decided to use Vav family knockout mice to evaluate the role of these proteins in the development of 7 , 12-dimethylben[a]antracene ( DMBA ) /12-O-tetradecanoylphorbol-13-acetate ( TPA ) - and DMBA/DMBA-triggered skin tumors . In the former model , a single topic administration of DMBA induces oncogenic mutations ( Q61L ) in the HRas locus ( ID number: 15461 ) in a small pool of keratinocytes ( initiation phase ) . Subsequent serial topic applications of TPA are then applied to expand this pool of transformed keratinocytes to generate papillomas ( promotion phase ) and , depending on the genetic background of mice , cutaneous squamous cell carcinomas ( cSCCs ) ( progression phase ) . Such pro-tumorigenic effect is mediated by the stimulation of intracellular signaling cascades in the initiated keratinocytes and , in addition , through autocrine/paracrine-based crosstalk between cancer and tumor-associated stromal cells that ultimately favor the expansion of the initial pool of transformed keratinocytes [27]–[30] . The latter model uses serial topic applications of DMBA that increase the frequency of cSCC development at the end of the carcinogenic protocol . We selected these models for our experiments because: ( i ) they are known to be Rac1- ( ID number: 19353 ) and Tiam1-dependent [4] , [31]; ( ii ) high levels of two Vav family proteins and a large number of additional Rho GEFs are present in normal and tumoral skin ( see Figure S1 ) , thus making a perfect working model to address intra-GEF family redundancies in a tumorigenic context; and ( iii ) they are compatible with the analysis of the role of the proteins under study in the tumor initiation , promotion , and progression phases [28] . Using this strategy , we have unveiled a Vav2/Vav3-dependent and cancer-specific autocrine/paracrine program that contributes to the initiation and promotion phases of skin tumors .
Expression analyses indicated that mouse papillomas and cSCCs expressed large numbers of Rho GEFs , including Vav2 and Vav3 ( Figure S1 ) . To assess if these two Vav family proteins played nonredundant roles with other Rho GEFs in the skin , we used compound Vav2−/−;Vav3−/− mice to evaluate the impact of the systemic inactivation of these two proteins in both epidermal maintenance and tumorigenesis . Unlike the case of Rac1−/− mice [32]–[34] , we could not find any defect in skin development , histological structure , or self-renewal in those mice regardless of the genetic background used ( Figure S2 and unpublished data ) . Wild-type-like parameters were also found in triple Vav1−/−;Vav2−/−;Vav3−/− C57BL/10 mice , indicating that the lack of an epidermal phenotype was not due to functional compensation events by Vav1 ( unpublished data ) . Hence , other Rho/Rac GEFs must be in charge of stimulating Rac1 in skin stem cells and keratinocytes under physiological conditions . By contrast , we observed that Vav2−/−;Vav3−/− FVB mice displayed lower kinetics of tumor development ( Figure 1A ) , a ≈5-fold reduction in the total number of tumors developed per mouse ( Figure 1B , C ) , and 10-fold lower levels of carcinoma in situ ( Table S1 ) when subjected to the two-step DMBA/TPA carcinogenic method . This effect was independent of the mouse genetic background , because C57Bl/10 Vav2−/−;Vav3−/− mice also showed statistically significant reductions in tumor burden when compared to control animals ( Figure 1D–F ) . Vav2−/−;Vav3−/− FVB mice also displayed lower kinetics of tumor development ( Figure 1G ) , a 2-fold reduction in tumor burden per mouse ( Figure 1H , I ) , and a decrease in the percentage of cSCC development ( Table S2 ) when subjected to the complete DMBA/DMBA tumorigenic method . Taken together , these results indicate that Vav2 and Vav3 play important roles in CST development but not in normal epithelial development and homeostasis . The lower tumor burden observed in DMBA/DMBA-treated Vav2−/−;Vav3−/− mice indicated that Vav proteins may have direct roles during the initiation phase of CSTs . To investigate this possibility , we analyzed the short-term response of the epidermis of wild type and Vav2−/−;Vav3−/− mice to DMBA ( Figure 2A ) . Using immunostaining with antibodies to the cleaved fragment of caspase 3 , we observed that DMBA induced ≈2-fold higher apoptotic cell numbers in the epidermis of Vav2−/−;Vav3−/− mice than in controls ( Figure 2B , C ) . This was not due to enhanced absorption and/or metabolization of the carcinogen , because immunohistochemistry experiments with antibodies to phospho-histone H2AX ( ID number: 15270 ) indicated that the DMBA treatment triggered similar levels of DNA double-strand breaks in mice of both genotypes ( Figure 2D , E ) . We also found that primary Vav2−/−;Vav3−/− keratinocytes were more susceptible to programmed cell death upon DMBA treatment or serum starvation in tissue culture , suggesting that the defects detected in vivo were keratinocyte autonomous ( Figure 2F ) . This was a stimulus-dependent defect , because wild-type and mutant keratinocytes displayed similar cell death rates when challenged with other pro-apoptotic agents such as radiomimetic ( bleomycin ) and endoplasmic reticulum stress-inducing ( dithiothreitol ) drugs ( Figure 2F ) . The ectopic expression of either HA-tagged Vav2 or Myc-tagged Vav3 , but not of the control green fluorescent protein ( GFP , ID number: P42212 ) , restored wild-type-like apoptotic rates in both DMBA-treated and serum-starved Vav2−/−;Vav3−/− keratinocytes ( Figure 2G , H ) , further indicating that this survival defect was a direct effect of the Vav2;Vav3 gene deficiency in keratinocytes . We hypothesized that Vav proteins could be also involved in the TPA-dependent promotion phase of skin tumors , an idea consistent with our prior observations indicating that the effect of the double Vav2;Vav3 gene deficiency in the reduction of tumor burden was significantly more conspicuous in DMBA/TPA- than in DMBA/DMBA-treated mice ( Figure 1; compare panels B and H ) . To investigate this possibility , we evaluated the short-term proliferative and inflammatory reaction induced by TPA in the skin of control and Vav2−/−;Vav3−/− mice ( Figure 3A ) . The TPA-induced proliferative response of the epidermis was severely attenuated in the absence of these two proteins , as demonstrated by the limited hyperplasia ( Figure 3B , C ) and the low levels of BrdU incorporation into keratinocytes ( Figure 3D ) detected in the epidermal layers of TPA-treated Vav2−/−;Vav3−/− mice . In vivo BrdU pulse-chase experiments indicated that those proliferative defects were associated with delayed kinetics and a reduced efficiency in the G1/S phase transition induced by TPA in the mutant keratinocytes ( Figure 3E ) . Consistent with this , immunoblot analyses using total cellular extracts obtained from the epidermis of TPA-treated mice showed that the activation ( extracellular regulated kinase , [Erk , ID numbers: 26417 , 26413] , signal transduction and activator of transcription 3 [Stat3 , ID number: 20848] ) or abundance ( cyclin E , ID number: 12447 ) of proteins involved in such cell cycle transition did not take place efficiently in Vav2−/−;Vav3−/− mice ( Figure 3F ) . Furthermore , we observed that the dermis of these animals did not show any sign of neutrophil infiltration ( Figure 3G , H ) or edema-associated thickening ( Figure 3G , I ) , indicating that the inflammatory response that takes places during the tumor promotion phase is totally abated in the absence of Vav proteins . The lack of an inflammatory response led us to explore the potential contribution of Vav2−/−;Vav3−/− inflammatory cells to the proliferative defects found in the epidermis of Vav2−/−;Vav3−/− mice . We observed that such defects still persisted in Vav2−/−;Vav3−/− C57BL/10 mice carrying a wild-type C57BL/6-Ly5 . 1 hematopoietic system ( Figure 4A–C ) , ruling out the possibility that the defective proliferation of the epidermis of the knockout animals could be an indirect consequence of dysfunctional hematopoietic cells . In agreement with those results , we also found that the grafting of Vav2−/−;Vav3−/− C57BL/10 bone marrow cells into lethally irradiated wild-type C57BL/6-Ly5 . 1 mice did not have any detectable effect on the responsiveness of the epidermis of host animals to TPA ( Figure 4D–F ) . To further assess the keratinocyte autonomous nature of the proliferative defects found in the epidermis of Vav2−/−;Vav3−/− mice , we evaluated the proliferative response of wild-type and Vav2;Vav3-deficient primary keratinocytes to TPA in cell culture . In agreement with the in vivo data , we observed that quiescent Vav2−/−;Vav3−/− keratinocytes incorporated less efficiently the S phase marker 5-ethynyl-2′-deoxyuridine ( EdU ) than their wild-type counterparts upon TPA stimulation ( Figure 5A ) . This was a TPA-specific defect , because Vav2−/−;Vav3−/− keratinocytes showed normal cell cycle progression when stimulated with complete growth media ( Figure 5A ) . Western blot and GTPase-linked immunosorbent ( G-LISA ) assays revealed that the Vav2;Vav3 gene deficiency was associated to reduced amounts of activation of Erk ( Figure 5B , upper panel ) , Stat3 ( Figure 5B , third panel from top ) , and Rac1 ( Figure 5C , upper panel ) in TPA-stimulated cells . It also reduced the basal levels of RhoA ( ID number: 11848 ) activation in nonstimutated cells and , in addition , eliminated the inactivation of RhoA that was typically observed in TPA-stimulated wild-type keratinocytes ( Figure 5C , lower panel ) . Normal levels of Erk and Rac1 activation were observed upon overexpression of HA-tagged Vav2 ( Figure S3A , C ) or Myc-tagged Vav3 in Vav2;Vav3-deficient keratinocytes ( Figure S3B , C ) , thus confirming that those defects were directly due to the Vav2;Vav3 gene deficiency . Furthermore , and consistent with the TPA-specific deficiency of the cell cycle transitions , we observed that those signaling responses were normal when Vav2−/−;Vav3−/− keratinocytes were stimulated with either serum or synthetic CnT07 media ( Figure 5D , E ) . We surmised that a tyrosine kinase had to be involved in this process , because Vav proteins cannot be activated by direct TPA/diacylglycerol binding or protein kinase C ( PKC ) –mediated serine/threonine phosphorylation 9 , 35 . In agreement with this idea , we observed that TPA triggered the tyrosine phosphorylation of both endogenous and ectopically expressed Vav2 in keratinocytes ( Figure 5F ) . This phosphorylation was blocked when cells were pre-incubated with either general PKC ( GF109203X ) or Src family ( PP2 ) inhibitors ( Figure 5F ) prior to the TPA stimulation step . An inactive PP2 analog ( PP3 ) did not have such inhibitory effect on Vav2 tyrosine phosphorylation ( Figure 5F ) . Similar results were obtained with the TPA-mediated stimulation of Erk route ( Figure 5G ) . Although many PKC family members are present in keratinocytes ( Figure S4A ) , we believe that a classical PKC ( cPKC ) must be involved , because we could reproduce the lack of Rac1 activation typically seen in Vav2−/−;Vav3−/− keratinocytes when we incubated the wild-type counterparts with Gö6976 ( Figure 5H ) , a cPKC-specific inhibitor that does not inactivate PKCs belonging to the novel or atypical subclasses [36] . A similar effect was observed when Fyn ( ID number: 14360 ) was knocked down in wild-type cells using short hairpin RNA ( shRNA ) techniques ( Figures 5H and S4B ) , indicating that this kinase was the Src family member preferentially involved in this signaling response . This is consistent with previous results reporting the TPA-mediated stimulation of this kinase in mouse keratinocytes [37] . These results indicate that Vav proteins act downstream of a cPKC/Fyn signaling route that mediates the pro-mitogenic effects of TPA in keratinocytes . We next considered the possibility that Vav proteins could control , in addition to the intrinsic signaling programs of keratinocytes described above , the stimulation of long-range autocrine/paracrine programs in the skin . This idea was consistent with the long-term defects seen in the activation/expression of G1/S phase-related signaling proteins in the epidermis of TPA-stimulated Vav2;Vav3-deficient mice ( Figure 3F ) and , in addition , by the total lack of inflammatory response found in the skin of those mice ( Figure 3G–I ) . To explore this idea , we carried out microarray experiments to identify the fraction of the TPA-induced transcriptome of the skin ( epidermis plus dermis ) that was Vav-dependent . We found a significant subset of TPA-regulated transcripts whose upregulation ( Figure S5A; right panel , A1 cluster; for functional annotation , see Table S3A , B ) or repression ( Figure S5A; right panel , A2 cluster; for functional annotation , see Table S3C , D ) was Vav2/Vav3-dependent . Interestingly , bioinformatics analyses using a skin tumor microarray dataset [38] revealed that most A1 cluster transcripts displayed a similar up-regulation in DMBA/TPA-induced papillomas and/or cSCCs when compared to normal skin ( Figure S5B , see clusters 2 and 3 ) . Conversely , most A2 cluster mRNAs were expressed in normal skin and down-regulated in papillomas and/or cSCCs ( Figure S5B , see clusters 5 and 6 ) . These results indicated that the short-term Vav2/Vav3-dependent gene signature identified in the above microarray experiments is mostly conserved in fully developed tumors . The functional annotation of the A1 gene cluster revealed a statistically significant enrichment of genes encoding extracellular ligands , including EGF family members ( i . e . , amphiregulin [Areg , ID number: 11839] , tumor growth factor α [TGFα , ID number: 21802] , heparin-binding EGF-like growth factor [HbEGF , ID number: 15200] ) , hepatocyte growth factor ( HGF , ID number: 15234 ) , fibroblast growth factor 7 ( FGF7 , ID number: 14178 ) , vascular endothelial growth factor β ( VEGFβ , ID number: 22340 ) , and a large cohort of cytokines and chemokines ( i . e . , IL1β [ID number: 16176] , interleukin 6 [IL6 , ID number: 16193] ) ( Table S4 ) . The detection of cytokine-encoding transcripts was not due to the infiltration of hematopoietic cells in the samples analyzed , because the A1 cluster did not include myeloid- or lymphocyte-specific genes ( Table S3A ) . In silico analyses indicated that many of the genes for those ligands could be regulated by transcriptional factors belonging to the Stat ( p = 6 . 4×10−6 ) , nuclear factor of activated T-cells ( NFAT; p = 0 . 002 ) , nuclear factor kappa-light-chain-enhancer of activated B cells ( NFκB; p = 0 . 004 ) , AP1 ( p≤0 . 05 ) , and E2F ( p = 0 . 03 ) families . We confirmed by quantitative RT-PCR ( qRT-PCR ) that Vav proteins were important for the TPA-mediated induction of mRNAs for EGF family ligands , HGF , FGF7 , IL6 , and IL1β both in vivo ( Figure S5C ) and in vitro ( Figure S5D ) . The only exception found for the correlation between whole skin and cultured keratinocytes was the Tgfa mRNA , which showed a Vav-dependent expression pattern in TPA-stimulated skin ( Figure S5C ) but not in isolated primary keratinocytes ( Figure S5D ) . These results suggest that some of the transcripts detected in the skin microarray experiments probably represent secondary waves of transcriptional activation set in place upon the engagement of other Vav2/Vav3-dependent extracellular ligands . Defects in the production of HGF and IL6 by Vav2;Vav3-deficient mice were confirmed at the protein level using ELISA determinations in skin and serum samples obtained from TPA-treated animals ( Figure S6A ) and , in the case of IL6 , by carrying out immunohistochemical analyses in skin sections ( Figure S6B ) . The TPA-mediated up-regulation of these Vav-dependent transcripts was abolished in wild-type keratinocytes upon the Fyn mRNA knockdown ( Figure S7 ) , indicating that this autocrine/paracrine program is one of the downstream responses triggered by the TPA/cPKC/Fyn/Vav pro-mitogenic route previously characterized in keratinocytes ( see above , Figure 5 ) . This Vav-dependent autocrine/paracrine program was keratinocyte-specific , since it was mostly absent in the recently described Vav-dependent transcriptome of mouse breast cancer cells [17] . Indeed , these two transcriptomes only shared the Areg , Hbegf , Tnfa , Il24 ( ID number: 93672 ) , Il23a ( ID number: 83430 ) , Osm ( ID number: 18413 ) , and Cxcl14 ( ID number: 57266 ) transcripts ( Table S4 ) . By contrast , we observed that the majority of the Vav2/Vav3-dependent transcripts previously found to be involved in the lung-specific metastasis of breast cancer cells was also present in the Vav-dependent transcriptome of TPA-stimulated skin ( Inhba [ID number: 16323] , Ptgs2 [ID number: 19225] , Tacstd2 [ID number: 56753]; Figure S8 ) [17] . The only exception was Ilk ( integrin-linked kinase , ID number: 16202 ) , which was repressed rather than induced by TPA independently of the expression status of Vav proteins ( Figure S8A ) . This indicates that the Vav-dependent transcriptome contains both cell-type-dependent ( i . e . , the autocrine/paracrine program ) and -independent ( i . e . , lung metastasis-related genes ) subsets . Given the critical roles that extracellular factors play in both the initiation and promotion phase of skin tumors [29] , [30] , we investigated whether they could be involved in the tumorigenic defects observed in Vav2−/−;Vav3−/− keratinocytes . If so , we speculated that the experimental manipulation of the extracellular environment had to rescue wild-type-like responses in them . To this end , we first checked whether the survival defects exhibited by DMBA-treated keratinocytes could be overcome when co-culturing them in the presence of equal numbers of wild-type cells . To distinguish each cell subpopulation in the mixed cultures , one of the subpopulation was labeled with a cell permeable chromophore prior to the co-culturing step . Using annexin V flow cytometry , we found that wild-type cells restored normal survival rates to both DMBA and serum deprivation in the co-cultured Vav2−/−;Vav3−/− keratinocytes ( Figure 6A ) . A similar protective effect was found when we included , without wild-type cells , ligands for transmembrane tyrosine kinase receptors ( epidermal growth factor [EGF , ID number: 13645] , TGFα , HGF , FGF7 ) in the culture media of Vav2−/−;Vav3−/− keratinocytes ( Figure 6B ) . By contrast , the addition of IL6 protected wild type but not Vav2−/−;Vav3−/− keratinocytes in the same type of experiments ( Figure 6B ) . The cell cycle defects shown by those cells under TPA-stimulation conditions were also eliminated when the serum-free media was supplemented with either transmembrane tyrosine kinase receptor ligands or IL1β ( Figure 6C ) . However , as in the apoptotic assays , we observed that IL6 could induce cell cycle entry in wild-type but not in Vav-deficient keratinocytes ( Figure 6C ) . This lack of responsiveness was not due to abnormal expression of any of the two IL6 receptor ( IL6-R ) subunits ( Figure S9 ) , indicating that Vav2;Vav3-deficient keratinocytes have , in addition to the general defect in the generation of the autocrine/paracrine program , a specific signaling defect downstream of the IL6-R . Consistent with this idea , immunoblot analyses indicated that EGF ( Figure 6D ) , but not IL6 ( Figure 6E ) , could trigger proper phosphorylation levels of Erk and Stat3 in Vav2−/−;Vav3−/− keratinocytes . This was a direct consequence of the Vav2;Vav3 gene deficiency , because we could restore normal phosphorylation levels of Erk and Stat3 downstream of the IL6-R upon the re-expression of either HA-tagged Vav2 ( Figure 6F ) or Myc-tagged Vav3 ( Figure 6G ) in Vav2−/−;Vav3−/− cells . The implication of Vav proteins in the signaling of the IL6-R was further demonstrated by the observation that IL6 triggered tyrosine phosphorylation of endogenous Vav2 in wild-type keratinocytes ( Figure 6H ) . To further evaluate the relevance of the Vav-dependent autocrine/paracrine program , we investigated whether the intradermal injection of mitogens could eliminate the proliferative and inflammatory defects seen in the skin of TPA-treated Vav2−/−;Vav3−/− mice . We observed that the injection of EGF , TGFα , or FGF7 induced similar levels of hyperplasia ( Figure 7A ) and BrdU immunoreactivity ( Figure 7B ) in the epidermis of wild-type and Vav2;Vav3-deficient mice . The simultaneous application of TPA resulted in a synergistic proliferative response in the epidermis of animals of both genotypes ( Figure 7C , D ) . The intradermal injection of IL6 could trigger a robust , TPA-like mitogenic response in the epidermis of control but not Vav2−/−;Vav3−/− animals ( Figure 7A–D ) , thus recapitulating the signaling defects observed in primary keratinocyte cultures ( see above; Figure 6 ) . IL6 , but not the other ligands tested , did induce a potent infiltration of neutrophils in the skin of both wild-type and knockout mice ( Figure 7E , F ) . This indicates that , unlike the case of keratinocytes , the IL6-R does not require the presence of Vav2 and Vav3 for proper signaling in myeloid cells . This is not due to functional compensation events from Vav1 , because IL6-treated Vav1−/−;Vav2−/−;Vav3−/− mice showed neutrophil infiltration rates similar to both wild-type and Vav2−/−;Vav3−/− animals ( Figure 7F , right panel ) . Finally , we investigated whether the tumors that developed in Vav2;Vav3-deficient mice could be the result of the outgrowth of cancer cells that , due to selection events , could have compensated the lack of Vav proteins by the exacerbation of other signaling routes . We suspected that such compensation could occur in this case because the few tumors that developed in mutant mice displayed a high similarity to those found in control animals in terms of size distribution , proliferation rates , and differentiation stage ( unpublished data ) . Based on the above , we decided to compare the abundance of transcripts encoding a variety of mitogenic ligands in papilloma and cSCC samples obtained from FVB mice of both genotypes . In addition , we monitored the expression pattern in those samples of Vav2/Vav3-dependent genes that were common between breast cancer cells and TPA-stimulated keratinocytes [17] . We observed that the Vav2/Vav3-dependent Tgfa , Hgf , Fgf7 , and Il6 transcripts showed reduced abundance in papillomas derived from Vav2/Vav3-deficient mice when compared to the levels present in control mouse tumors ( Figure 8A ) . This reduction , however , was milder than the defect originally seen in the TPA-stimulated skin of Vav2−/−;Vav3−/− mice ( see above , Figure S5C ) . Other Vav2/Vav3-dependent ( Areg , Hbegf ) and -independent ( Egf , Btc [ID number: 12223] ) transcripts displayed similar levels in papillomas regardless of the Vav2/Vav3 expression status , further suggesting that the autocrine/paracrine defect was ameliorated in these tumors ( Figure 8A ) . Such compensation event was exacerbated in carcinomas , since we observed a striking up-regulation of the abundance of EGF-R family ligand transcripts in all cSCC samples derived from mutant mice ( Figure 8B ) . Such up-regulation took place irrespectively of whether the ligands were of the Vav-dependent ( Tgfa , Areg , Hbegf ) or Vav-independent ( Egf , Btc ) subclasses ( Figure 8B ) . Carcinomas from Vav2;Vav3-deficient mice also displayed up-regulation of the Hgf mRNA ( Figure 8B ) . This was not a sign of a total elimination of the Vav2/Vav3 signaling deficiency , because lower levels of Fgf7 and Il6 transcripts were still detected in cSCCs collected from Vav2−/−;Vav3−/− mice ( Figure 8B ) . A similar behavior was observed in the case of the Inhba and Ptgs2 when their abundance was compared between papilloma and cSCCs ( Figure 8C , D ) . However , the Tacstd2 and Ilk transcripts showed no variations in these experiments ( Figure 8C , D ) . These results suggest that the HRasQ61L-transformed keratinocytes have to progressively bypass part of the Vav2/Vav3 signaling deficiency to generate fully developed tumors . This compensation event is not due to Vav1 overexpression , because this transcript shows a similar 3-fold increase in abundance in cSCCs of both wild-type and Vav2−/−;Vav3−/− mice ( n = 4 ) .
Our work indicates that Vav2 and Vav3 play first- and second-level signaling roles in keratinocytes that , although mechanistically different , act in a concerted manner to favor the initiation and promotion phases of CSTs ( Figure 8E ) . From a signaling hierarchical point of view , the earliest role of Vav proteins is to work in a cPKC- and Fyn-dependent signaling cascade that leads to the downstream stimulation of Rac1 and optimal phosphorylation kinetics of both Erk and Stat3 ( Figure 8E , step 1 ) . This route has a direct impact on the G1/S transition of primary keratinocytes . A more distal endpoint of this Vav-dependent route is the engagement of a wide autocrine/paracrine program that favors keratinocyte survival to DNA damage , epithelial hyperplasia , and the formation of an inflammatory microenvironment ( Figure 8E , step 2 ) . This program is composed of extracellular factors ( i . e . , HGF , FGF7 , IL1β ) involved in the monovalent regulation of keratinocyte survival and proliferation during the initiation and promotion phases , respectively . In addition , it contains bivalent extracellular factors ( i . e . , IL6 ) that regulate keratinocyte mitogenesis and the engagement of inflammatory responses during the tumor promotion phase . Finally , Vav proteins play a second-level , keratinocyte-specific role in the signaling route of one of the Vav2/Vav3-dependent extracellular factors , the cytokine IL6 ( Figure 8E , step 3 ) . This biological program may play roles in both inchoate and fully established tumors , as indicated by the similar regulation of the TPA-induced Vav2/Vav3-dependent gene signature in papillomas and cSCCs obtained from DMBA/TPA-treated mice . By contrast , we have observed that the compound Vav2;Vav3 gene deficiency does not induce any overt dysfunction in normal skin development and homeostasis , indicating that the Vav2/Vav3-dependent route only becomes functionally relevant in the epidermis under conditions that require increased signaling thresholds for the assembly of new , pathophysiological-specific programs . To our knowledge , this is the first demonstration of the implication of Vav proteins , Rho GEFs , or any Rho GTPase in the assembly of such large autocrine/paracrine program in either a physiological or pro-tumorigenic scenario . Several indications support the idea that the upstream and downstream roles of Vav proteins in this autocrine/paracrine program are critical for the initiation and promotion phase of inchoate skin tumors and , probably , for long-term tumor sustenance . Thus , previous reports have shown that many of the Vav2/Vav3-dependent extracellular factors regulated by Vav2 and Vav3 contribute to keratinocyte survival , proliferation , and tumorigenesis [29] , [30] . Furthermore , we have shown that all the defects detected in cultured Vav2−/−;Vav3−/− keratinocytes can be effectively bypassed upon their co-culture with wild-type keratinocytes or , alternatively , upon the addition of specific Vav2/Vav3-dependent ligands ( EGF family ligands , HGF , FGF7 , IL1β ) to their cultures . Similar results were observed when these rescue experiments were carried out in vivo using intradermal injections of those extracellular factors in Vav2−/−;Vav3−/− mice . However , and in good agreement with the critical role of Vav proteins downstream of the IL6-R , IL6 could not restore the survival and mitogenic defects of Vav2/Vav3-deficient keratinocytes when used in vitro or in vivo . It is likely that these second-level signaling defects also contribute to the lower tumor burden observed in Vav2−/−;Vav3−/− animals , since previous reports have shown that IL6-mediated signaling is essential for skin tumorigenesis [39] . Interestingly , we observed that IL6 did restore the defective inflammatory response observed in the skin of TPA-stimulated Vav2−/−;Vav3−/− animals . This result indicates that IL6 is indeed part of the Vav2/Vav3-dependent pro-mitogenic and pro-inflammatory program of keratinocytes and , in addition , that the critical role of Vav proteins downstream of the IL6-R is keratinocyte-specific . The use of Vav1−/−;Vav2−/−;Vav3−/− mice has ruled out the possibility that the normal chemoattraction of Vav2−/−;Vav3−/− neutrophils induced by IL6 could be due to functional compensation events mediated by the hematopoietic-specific Vav1 protein [9] . Thus , these cell-type-specific differences could be due to the presence of other IL6-regulated Rho GEFs in neutrophils ( e . g . , P-Rex1 ) [40] or , alternatively , reflect the implication of Vav proteins in a proliferative/survival signaling branch of the IL6-R that is not important for migration and the induction of the pro-inflammatory program . Additional experiments using wild-type and Vav family-deficient neutrophils will be needed to discriminate those possibilities . Finally , the pathophysiological importance of this program during skin tumorigenesis is further highlighted by our in silico analyses indicating that the Vav2/Vav3-dependent transcriptomal signature is conserved in fully developed tumors and , in addition , by the exacerbated up-regulation of most transcripts for those autocrine/paracrine factors consistently seen in cSCCs from Vav2−/−;Vav3−/− mice ( Figure 8E ) . We surmise that these experiments have only revealed the tip of the iceberg of this biological program , because the Vav2/Vav3-dependent transcriptomal signature encodes other pro-mitogenic and pro-inflammatory factors that have not been yet analyzed . It also contains factors with assigned roles in angiogenesis ( i . e . , Cyr61 [ID number: 16007] , IL6 , VEGFβ ) , the polarization of the TH cell response towards the TH1 ( i . e . , Ccl3 [ID number: 20302] , Ccl4 [ID number: 20303] , Cxcl5 [ID number: 20311] , IL1β , Spp1 [ID number: 20750] , TNFα ) , and TH17 ( i . e . , Csf3 [ID number: 12985] , Cxcl2 [ID number: 20310] , Cxcl5 , IL6 , IL10 [ID number: 16153] ) subtypes or the tumor-induced “education” of dermal fibroblasts ( i . e . , IL1β ) [29] , [30] , [41]–[43] , thus suggesting that the engagement of the Vav2/Vav3 route in keratinocytes could result in a widespread reprogramming of the tissue microenvironment ( Figure 8E ) . This biological program is also endowed with multiple positive feedback mechanisms that can lead to self-amplification and long-term signal sustenance upon its initial engagement in keratinocytes . An obvious signaling relay is the Vav→IL6→IL6-R→Vav→Stat3 route , since it is known that the stimulation of phospho-Stat3 re-feeds this autocrine loop by activating transcriptionally the Il6 gene [44] , [45] . The targeted inflammatory and stromal cells provide additional options for amplification and diversification of signals , since those cells can turn on additional paracrine mechanisms that target keratinocytes and stromal cells [41]–[43] . Thus , it is likely that the initial activation of the Vav route in keratinocytes will kindle a “butterfly effect” that will induce widespread and reciprocal signaling interactions among keratinocytes , resident stromal cells , and newcomer inflammatory and immune cells . Our work also sheds light on issues related to functional redundancies with the Rho GEF family and Vav subfamily during the initiation and promotion phases of skin tumors . On the one hand , the cell reconstitution experiments indicate that Vav2 and Vav3 seem to act redundantly in all the biological processes analyzed in this work . Consistent with this idea , we have observed that single Vav2−/− and Vav3−/− knockout mice do not show the initiation and promotion defects found in the compound Vav2;Vav3-deficient mice ( M . M . -M . and X . R . B . , unpublished data ) . On the other hand , this program seems to be quite idiosyncratic for Vav proteins as inferred by the detection of a phenotype despite the large number of Rho GEFs that , according to our bioinformatic array analyses , are present in normal skin , papilloma , and cSCCs . The cancer-linked phenotype of Vav2/Vav3-deficient mice is also quite different from that previously reported for Tiam1-deficient mice during both tumor initiation [4] , [5] and papilloma/cSCC malignant progression [4] . In this context , the observation that subsets of Rho GEFs are differentially regulated in normal skin , papilloma , and cSCCs suggests the specific engagement of physiological- and tumor-stage-specific Rac1- , RhoA , and Cdc42 ( ID number: 12540 ) -dependent programs that may contribute to both skin homeostasis and pathophysiology . These results emphasize the importance of extending animal-model-based genetic analysis to all Rho GEF family members . Our results suggest that the pharmacological targeting of Vav proteins could be a potentially useful strategy in skin cancer . Since our data have been generated using mice lacking Vav proteins from the initiation stage , they could only be formally used to establish the value of such therapies at the prevention rather than the remediation level . However , preventive therapies are interesting in this case because CSTs are known to develop at high frequency and multiplicity in individuals with actinic keratosis or in patients treated with some immunosuppressants , antifungal antibiotics , or antitumoral therapies ( i . e . , B-Raf ( ID number: 109880 ) inhibitors ) [46] . Assessing the value of such potential therapies in the case of fully developed or metastasized CSTs will require the generation of chemically inducible knock-in systems . In any case , we can anticipate from the present data that anti-Vav therapies will elicit much fewer intrinsic side effects in the skin than those based on the inactivation of either Tiam1 or Rac1 .
All animal work has been done in accordance with protocols approved by the Bioethics committees of both the University of Salamanca and CSIC . To analyze the expression of Rho GEFs mRNAs in skin and tumors , raw data containing samples from normal tail skin ( n = 83 ) , papillomas ( n = 60 ) , and carcinomas ( n = 68 ) was downloaded from the Gene Expression Omnibus website ( Accession number: GSE21264 ) . These data were obtained in Balmain's laboratory using Affymetrix Mouse Genome 430 2 . 0 arrays and animals belonging to a mixed Mus musculus FVB/Mus spretus genetic background [38] . Signal intensity values were obtained from CEL files after RMA . Probesets corresponding to Rho/Rac GEFs were extracted from the dataset and ANOVA analysis used to identify Rho/Rac GEF-encoding transcripts that were differentially expressed between normal tail , papillomas , and carcinomas ( p value <0 . 01 ) . For those genes with more than one probeset significantly deregulated , we selected the one with the lowest p value for graphic representation . Total RNA was extracted from the indicated cells using Trizol ( Sigma ) and quantitative RT-PCR performed using the QuantiTect SYBR Green RT-PCR kit ( Qiagen ) and the iCycler machine ( Bio-Rad ) or , alternatively , the Script One-Step RT-PCR kit ( BioRad ) and the StepOnePlus Real-Time PCR System ( Applied BioSystems ) . Raw data were then analyzed using either the iCycler iQ Optical System software ( Bio-Rad ) or the StepOne software v2 . 1 ( Applied Biosystems ) . We used the abundance of the endogenous Gapdh mRNA as internal normalization control . Primers used for transcript quantitation included 5′-TTG CCC AGA ACA AAG GAA TC-3′ ( forward for mouse Vav1 ) , 5′-AAG CGC ATT AGG TCC TCG TA-3′ ( reverse for mouse Vav1 ) , 5′-AAG CCT GTG TTG ACC TTC CAG-3′ ( forward for mouse Vav2 ) , 5′-GTG TAA TCG ATC TCC CGG GAT-3′ ( reverse for mouse Vav2 ) , 5′-GGG TAA TAG AAC AGG CAC AGC-3′ ( forward for mouse Vav3 ) , 5′-GCC ATT TAC TTC ACC TCT CCA C-3′ ( reverse for mouse Vav3 ) , 5′-GGC AAA AAG TCA GTC CGA CC-3′ ( forward for mouse Fyn , transcript variants 1 and 2 ) , 5′-AAA GCG CCA CAA ACA GTG TC-3′ ( reverse for mouse Fyn , transcript variants 1 and 2 ) , 5′-TCG TGG CAA AAG AGC TTG GA-3′ ( forward for mouse Fyn , transcript variant 3 ) , 5′-TAG GGT CCC AGT GTG AGA GG-3′ ( reverse for mouse Fyn , transcript variant 3 ) , 5′-CGT CCG CCA TCT TGG TAG AGA GAG CAT-3′ ( forward for mouse Cd3e ) , 5′-CTA CTG CTG TCA GGT CCA CCT CCA C-3′ ( reverse for mouse Cd3e ) , 5′-ATG CTA GCG ATG CAT GAG TG-3′ ( forward for mouse Tgfa ) , 5′-CAG GGA CTT TCT TGC CTG AG-3′ ( reverse for mouse Tgfa ) , 5′-CGG TGG AAC CAA TGA GAA CT-3′ ( forward for mouse Areg ) , 5′-TTT CGC TTA TGG TGG AAA CC-3′ ( reverse for mouse Areg ) , 5′-GCT GCC GTC GGT GAT GCT GAA GC-3′ ( forward for mouse Hbegf ) , 5′-GAT GAC AAG AAG ACA GAC G-3′ ( reverse for mouse Hbegf ) , 5′-GCA GAC ACC ACA CCG GCA CAA-3′ ( forward for mouse Hgf ) , 5′-GCA CCA TGG CCT CGG CTT GC-3′ ( reverse for mouse Hgf ) , 5′-TTT GGA AAG AGC GAC GAC TT-3′ ( forward for mouse Fgf7 ) , 5′-GGC AGG ATC CGT GTC AGT AT-3′ ( reverse for mouse Fgf7 ) , 5′-CTT CCT ACC CCA ATT TCC AAT G-3′ ( forward for mouse Il6 ) , 5′-ATT GGA TGG TCT TGG TCC TTA GC-3′ ( reverse for mouse Il6 ) , 5′-ACG GAC CCC AAA AGA TGA AGG GCT-3′ ( forward for mouse Il1b ) , 5′-GGG AAC GTC ACA CAC CAG CAG G-3′ ( reverse for mouse Il1b ) , 5′-CGC TGC TTT GTC TAG GTT CC-3′ ( forward for mouse Ereg ) , 5′-GGG ATC GTC TTC CAT CTG AA-3′ ( reverse for mouse Ereg ) , 5′-CCC AGG CAA CGT ATC AAA GT-3′ ( forward for mouse Egf ) , 5′-CCC AGG AAA GCA ATC ACA TT-3′ ( reverse for mouse Egf ) , 5′-GGA ACC TGA GGA CTC ATC CA-3′ ( forward for mouse Btc ) , 5′-TCT AGG GGT GGT ACC TGT G-3′ ( reverse for mouse Btc ) , 5′-TGC ACC ACC AAC TGC TTA GC-3′ ( forward for mouse Gapdh ) , and 5′-TCT TCT GGG TGG CAG TGA TG-3′ ( reverse for mouse Gapdh ) . Primers used for quantitating Inhba , Ptgs2 , Tacstd2 , and Ilk transcripts were described before [17] . To calculate the number of copies of mouse Vav family mRNAs in cell/tissue samples , the Ct values obtained for the amplified Vav1 , Vav2 , and Vav3 cDNA fragments by qRT-PCR in each sample were compared with those obtained using serial dilutions of plasmids of known concentration containing the Vav1 ( pJLZ52 ) [13] , Vav2 ( pCCM33 ) [17] , and Vav3 ( pCCM31 ) [17] cDNAs . The number of copies obtained for each transcript was finally calculated using this titration curve and the size of each plasmid . Single and compound Vav family knockout mice were described elsewhere [19] , [23] , [47]–[49] . For long-term carcinogenesis experiments , the backs of animals of the indicated genotypes were shaved and , 2 d later , the two-step carcinogenic DMBA/TPA protocol initiated using a single topic application of DMBA ( 25 µg diluted in 200 µl of acetone , both from Sigma ) . The promotion phase consisted of biweekly applications of TPA ( 200 µl of a 1×10−4 M solution in acetone , Sigma ) during a 20-wk-long period . For complete carcinogenesis , mice were treated biweekly with 5 µg of DMBA alone in 200 µl of acetone during 20 wk . The number , size ( measured with a digital caliper ) , and incidence of papilloma was determined weekly . At the end of the experiment , animals were injected intraperitoneally with BrdU ( 100 µg/g body weight , Sigma ) and euthanized 1 h later . For short-term studies of in vivo epidermal apoptosis , the dorsal skin of mice was treated with a single application of either DMBA ( 25 nmol in 200 µl of acetone ) or acetone ( 200 µl ) 2 d after shaving . For short-term in vivo proliferation assays , the dorsal skin of mice was treated with either one or four applications of either TPA ( 6 . 8 nmol in 200 µl acetone ) or carrier solution 2 d after shaving . Animals were injected with BrdU and euthanized , as indicated above . For the determination of in vivo cell cycle transitions , animals were treated with a single topic application of TPA and , 1 h before euthanasia , injected intraperitoneally with BrdU . Alternatively , TPA-treated animals were euthanized and the epithelial layer obtained to generate tissue cell extracts . For intradermal injection of mitogens and cytokines , mice were anesthetized and injected under the epidermal layer of their skin with 10 µl of either the appropriate protein-containing saline solution or the control buffer injected using a 30-gauge needle and a Hamilton microsyringe . The concentration of ligands ( Peprotech ) in the injection solution was 100 ng/ml . Mice were then BrdU labeled and killed as above . For bone marrow reconstitution experiments , 3–5×106 cells of donor bone marrow cells were injected intravenously into recipient mice that were previously subjected to sublethal doses of irradiation ( 600 rad; 1 Gy = 100 rads ) . In these experiments , we used “wild-type” C57BL/6 Ly5 . 1 mice in order to distinguish the hematopoietic populations derived from them ( which express the CD45 . 1 surface marker ) and from the knockout C57BL/10 mice ( expressing the CD45 . 2 surface marker ) . Reconstitution experiments were done in both orientations . Eight weeks after injection , peripheral blood samples were collected from the cheek vein and the proper reconstitution of the immune system by the injected donor cells evaluated using flow cytometry . Surface markers used in the cytometry experiments included an allophycocyanin-labeled mouse monoclonal antibody to CD45 . 2 , a biotin-labeled mouse monoclonal antibody to mouse CD45 . 1 , a biotin-labeled rat monoclonal antibody to mouse CD45R/B220 , a biotin-labeled rat monoclonal antibody to mouse CD19 , a phycoerytrin-labeled rat monoclonal antibody to mouse CD11b , a phycoerytrin-labeled hamster monoclonal antibody to mouse CD3ε ( all from BD Pharmingen ) , a biotin-labeled hamster monoclonal antibody to mouse TCRβ , and a phycoerytrin-labeled rat monoclonal antibody to mouse CD19 ( both from eBioscience ) . Bone-marrow-reconstituted animals were then subjected to topic TPA treatments in the skin as indicated above . Whereas the long-term carcinogenesis assays were done in animals of both the FVB and C57BL/10 genetic backgrounds , we selected animals of the C57BL/10 background for the short-term studies . This facilitated comparative studies with other Vav family knockout animals ( i . e . , single Vav1−/− and triple Vav1−/−;Vav2−/−;Vav3−/− mice , which were all homogenized in that background ) as well as the bone marrow reconstitution experiments ( which required transplantation between genetically compatible donor and recipient mice ) . At the beginning of each experimental procedure , cohorts of 6–8-wk-old animals with an even gender distribution of each genotype subset were used . Samples from tumors and short-term skin experiments were processed following three independent protocols: ( i ) Fixed in 4% paraformaldehyde ( Sigma ) , ( ii ) cryoprotected and stored at −70°C , and ( iii ) snap-frozen and stored at −70°C for molecular analyses ( i . e . , RNA and protein extraction ) . Tissues were extracted , fixed in 4% paraformaldehyde ( Sigma ) , cut in 2–3 µm thick sections , and stained with hematoxylin/eosin . Tumor sections were analyzed by pathologists to classify them according to malignancy grade ( benign , benign plus carcinoma in situ , malignant ) and level of differentiation ( high , mild poor ) . In short-term experiments , the thickness of the epidermal layer and total skin thickness was measured in vertical cross-sections in at least 10 different locations in each mouse to determine epidermal hyperplasia and inflammatory response ( edema ) , respectively . For standard immunohistochemical staining , paraffin-embedded sections were dewaxed , microwaved in citrate buffer , blocked with 10% nonimmune horse serum ( Gibco ) , and incubated overnight with the appropriate primary antibody at 4°C . After exhaustive washing in 137 mM NaCl , 2 . 7 mM KCl , 10 mM Na2HPO4 , 2 mM KH2PO4 , and 0 . 1% Tween-20 ( PBST ) , sections were incubated with appropriate biotin-coupled secondary antibodies ( all used at a 1∶1 , 000 dilution in PBST ) followed by avidin-peroxidase ( ABC Elite Kit Vector , Vector labs ) . Positive staining was determined using diaminobenzidine as a substrate ( DAB Kit Vector , Vector labs ) following the manufacturer's recommendations . Sections were then counterstained with hematoxylin and mounted . Images were captured using an Olympus BX51 microscope coupled to an Olympus DP70 digital camera . Staining quantification was blindly assessed by two independent investigators and classified according to intensity ( 0–10 ) and estimation of ratio of cells stained ( 0–1 ) . The relative value for each section was scored as the product of intensity×ratio given a general score ranging from 0 to 10 . For immunofluorescent staining , sections treated as before were incubated with either a Alexa Fluor 488–labeled goat anti-rabbit IgG antibody ( Molecular Probes , 1∶200 dilution ) or a Cy3-conjugated goat anti-mouse IgG antibody ( Jackson Immunoresearch laboratories , Inc , 1∶200 dilution ) , washed , and countersatined with 4′ , 6-diamidino-2-phenylindole ( DAPI; 2 ng/ml , Sigma ) with or without rhodamine-labeled phalloidin ( Molecular Probes , 1∶200 dilution ) . Primary antibodies used in these experiments included rabbit polyclonal antibodies to keratin 1 , keratin 5 , keratin 14 , and filaggrin ( Covance , 1∶500 dilution in each case ) , rabbit polyclonal antibodies to IL6 ( Genzyme , 1∶100 dilution ) , rat monoclonal antibodies to keratin 8 ( Troma1 , not commercial; 1/5 dilution of the hybridoma supernatant ) and CD45 ( BD Biosciences , 1∶50 dilution ) , mouse monoclonal antibodies to keratin 10 ( Santa Cruz Biotechnology , 1∶50 dilution ) , keratin 13 ( Sigma , 1∶50 dilution ) and histone H2A . X ( Cell Signaling , 1∶100 dilution ) , and a rabbit polyclonal antibody to myeloperoxidase ( Abcam , 1∶250 dilution ) . To detect proliferating cells , dewaxed sections were denaturalized in 2N HCl at 37°C for 1 h , washed extensively in 0 . 1 M borate buffer , blocked with 2% nonfat dry milk ( Nestlé ) in 0 . 1% Triton-PBS , and incubated overnight with a mouse monoclonal antibody to BrdU ( BD Biosciences , 1∶400 dilution ) . To detect apoptotic cells , we used two different approaches . In some cases , tissue sections were deparafinized , hydrated , digested with proteinase K ( Dako ) for 30 min at 37°C , and subjected to the Tunel reaction using the Tunel-based In Situ Cell Detection kit ( Roche ) as indicated by the manufacturer's instructions . Alternatively , tissues sections were cryoprotected using stepwise immersions in 15%–30% sucrose-PBS solutions , embedded in Tissue-Tek OCT ( Sakura Finetek Europe ) , and stored frozen at −70°C . Upon thawing , slides were blocked as described before and incubated with a rabbit polyclonal antibody to cleaved caspase 3 ( Cell Signaling , 1∶50 dilution ) . Images from immunofluoresce experiments were captured using a Leica CTR600 microscope . Quantifications of both standard and immunoflurescence signals were done with the Metamorph-Metaview software ( Universal Imaging ) . Epidermis from neonates of the indicated genotypes were treated with 250 U/ml of dispase ( Roche ) overnight at 4°C and keratinocytes prepared using CnT07 media ( CELLnTEC ) according to the manufacturer's instructions . Keratinocytes were maintained in CnT07 media on type I collagen-precoated plates ( BD Biosciences ) . When genotypically mixed cultures were utilized , the keratinocytes of one the genotypes used were labeled in culture with a cell permeable chromophore ( CellTracker green CMFDA , Invitrogen ) for 30 min according to the manufacturer's instructions . Labeled and nonlabeled cultures were trypsinized and plated either as genotypically pure or mixed populations in tissue culture plates . Apoptotic rates in vitro were determined by flow cytometry using the Annexin V kit ( Immunostep ) . In vitro cell cycle transitions were determined using the Click-iT EdU Alexa Fluor 647 Flow Cytometry Assay Kit ( Invitrogen ) . For in vitro apoptotic assays , exponentially growing keratinocytes of the indicated genotypes were either serum starved in Eagle's minimal essential medium ( EMEM , Lonza ) supplemented with 0 . 05 mM CaCl2 ( Sigma ) or , alternatively , treated with 100 µM DMBA , 0 . 15 µM bleomycin ( Sigma ) , or 2 mM dithiothreitol ( Sigma ) . Cells were then harvested 8–24 h later and the number of apoptotic cells determined by flow cytometry using the Annexin V kit ( Immunostep ) . In apoptotic experiments using keratinocytes ectopically expressing proteins , cells that had integrated the lentiviral particle ( GFP+ ) were gated away from noninfected ( GFP− ) cells . A similar gating strategy ( presence/absence of the CellTracker green CMFDA chromophore ) was used to characterize apoptotic rates in genotypically mixed cell cultures . In rescue experiments with extracellular ligands , these factors were added at the beginning of the starvation period at a concentration of either 10 ng/ml ( EGF , TGFα , HGF , FGF7; all obtained from Peprotech ) or 100 ng/ml ( IL6 , Peprotech ) and apoptosis quantified as above after an overnight incubation . For keratinocyte G1/S phase transition assays , cells were starved for 3 h as above and then stimulated by the addition EMEM supplemented with 0 . 2 µM TPA , 10 ng/ml of receptors for transmembrane tyrosine kinases ( EGF , TGFα HGF , FGF7 ) , 10 ng/ml IL1β ( Peprotech ) , 100 ng/ml IL6 , or 8% calcium-chelated fetal calf serum . After 4 h , cells were incubated with the EdU reactive ( Invitrogen ) for 30 min , and the percentage of cells in S phase ( EdU+ ) determined using the Click-iT EdU Alexa Fluor 647 Flow Cytometry Assay Kit ( Invitrogen ) . For intracellular signaling experiments , exponentially growing primary cells of the indicated genotypes were washed and starved for 3 h in EMEM supplemented with 0 . 05 mM CaCl2 . For stimulation , we used EMEM supplemented with 0 . 2 µM TPA , 10 ng/ml EGF , or 100 ng/ml IL6 for the indicated periods of time . When indicated , the pan-PKC GF109203X ( 5 µM , Sigma ) , the classical PKD-specific Gö6976 ( 3 µM , Calbiochem ) , and the Src family PP2 ( 2 µM , Calbiochem ) inhibitors were added to the starved keratinocytes 30 min before the stimulation with TPA . The PP3 molecule was used as negative control for the PP2 experiments ( 2 µM , Calbiochem ) . When indicated , HA-Vav2 , Myc-Vav3 with or without GFP were expressed in wild-type ( GFP ) or Vav2−/−;Vav3−/− ( each of the above proteins ) keratinocytes using lentiviral delivery methods . To this end , the pCCM33 [17] , pCCM31 [17] , or pCQS1 [21] vectors were transfected into Lenti-X 293T cells ( Clontech ) using the Lenti-X HT packaging mix ( Clontech ) . Viral particles were collected 48 h after transfection , concentrated using the Lenti-X concentrator kit ( Clontech ) , and then used to infect keratinocyctes of the indicated genotypes during 3 consecutive days using centrifugation at 1 , 500× g in the presence of 8 µg/ml polybrene ( Sigma ) . As controls , we carried out infections of wild-type and mutant keratinocytes using either the empty pLVX-IRES-Hyg or the GFP-encoding pcDH1-MCS1-EF1-coGFP ( System Biosciences ) lentivirus . The shRNA-mediated knockdown of Fyn transcripts was carried out using lentiviral particles ( TRC lentiviral Mouse Fyn shRNA; Thermo Scientific ) as previously described [17] . The TRC number and the shRNA sequence yielding the greatest knockdown was clone number TRCN0000023382 ( 5′-AAA CCC AGG GCT GCC TTG GAA AAG-3′ ) . This clone was used in the experiments presented in this work . In the case of tissue extracts , skin samples were excised from the euthanized animals , placed on ice-cold glass plates , the epidermis removed with a razor blade , transferred into a lysis buffer ( 10 mM Tris-HCl [pH 8 . 0] , 150 mM NaCl , 1% Triton X-100 , 1 mM Na3VO4 , 10 mM β-glycerophosphate , and a mixture of protease inhibitors [Cømplete , Roche] ) , and mechanically homogenized using the GentleMACS dissociator ( Miltenyi Biotec ) . In the case of primary keratinocytes maintained in culture , cells were washed with chilled PBS , scrapped in lysis buffer , and disrupted by extensive vortexing ( Ika ) . Extracts were precleared by centrifugation at 14 , 000 rpm for 10 min at 4°C , denatured by boiling in SDS-PAGE sample buffer , separated electrophoretically , and transferred onto nitrocellulose filters using the iBlot Dry Blotting System ( Invitrogen ) . Membranes were blocked in 2% BSA ( Sigma ) in TBS-T ( 25 mM Tris-HCl ( pH 8 . 0 ) , 150 mM NaCl , 0 . 1% Tween-20 ) for at least 1 h and then incubated overnight with the appropriate antibodies . Those included rabbit polyclonal antibodies to phospho-Erk1/2 ( residues Thr202/Tyr204; Cell Signaling , 1∶1 , 000 dilution ) , total Erk1/2 ( Cell Signaling , 1∶1 , 000 dilution ) , phospho-Stat3 ( residue Tyr705; Cell Signaling , 1∶1 , 000 dilution ) , total Stat3 ( Cell Signaling , 1∶1 , 000 dilution ) , cyclin E ( Abcam , 1∶1 , 000 dilution ) , the Myc epitope ( Upstate/Millipore , 1∶1 , 000 dilution ) , Gp130 ( Cell Signaling , 1∶1 , 000 dilution ) , PKC and PKD ( all from Cell Signaling , 1∶1 , 000 dilution ) , as well as mouse monoclonal antibodies to α-tubulin ( Calbiochem , 1∶1 , 000 dilution ) , phosphotyrosine ( Santa Cruz Biotechnology , dilution 1∶1 , 000 ) , the HA epitope ( Covance , 1∶1 , 000 dilution ) , and IL6-Ra subunit ( Santa Cruz Biotechnology , 1∶500 dilution ) . Homemade rabbit polyclonal antibodies to Vav2 and Vav3 have been previously described [16] , [20] , [24] . After three washes with TBS-T to eliminate the primary antibody , the membrane was incubated with the appropriate secondary antibody ( GE Healthcare , 1∶5 , 000 ) for 30 min at room temperature . Immunoreacting bands were developed using a standard chemoluminescent method ( ECL , GE Healthcare ) . In the case of immunoprecipitations , keratinocyte lysates obtained as above were incubated overnight at 4°C using either a rabbit polyclonal antibody to Vav2 or a monoclonal antibody to HA . Immunocomplexes were collected with Gammabind G-Sepharose beads ( GE Healthcare Life Biosciences ) , washed , and analyzed by immunobloting as above . ELISAs were used to measure the amount of IL6 ( IL6 Mouse ELISA Kit , Invitrogen ) and HGF ( HGF Mouse ELISA kit , Abnova ) according to the manufacturer's instructions . Absorbance at 450 nm was measured immediately at the end of the protocol using a plate reader ( Ultraevolution , Tecan ) . Total cellular lysates were obtained as above , snap frozen , thawed , quantified , and analyzed using Rac1 and RhoA G-LISA assay kits according to the manufacturer's instructions ( Cytoskeleton ) . Absorbance at 490 nm was measured using a Ultraevolution plate reader . All microarray experiments were performed by the personnel of the Genomics and Proteomics Unit of our Institution . Total cellular RNA was extracted from the back skin of wild-type or knockout mice treated as described before ( n = 3 for each treatment ) using the RNAeasy kit ( Qiagen ) , quantified using 6000 Nano Chips ( Agilent ) , and used ( 2 . 3 µg/sample ) to generate labeled cRNA probes according to the manufacturer's instructions ( Affymetrix ) . Purified RNA was processed and hybridized to Affymetrix GeneChip Mouse Gene 1 . 0 ST as indicated elsewhere [17] , [50] . Signal intensity values were obtained from CEL files after robust multichip average [51] , [52] . We performed Pavlidis template matching analysis , in which the Pearson's correlation coefficient is computed between the intensities measured for each gene and the values of an independent variable [53] . The p values to test for the null hypothesis that the correlation is zero are provided . Corrected p values were also calculated ( Q value ) . For functional annotation purposes , genes were considered differentially regulated if their Q values were lower than 0 . 05 . For metagenomic analysis , we used as threshold significance values a Q≤0 . 025 and fold change variations relative to control skin ≥1 . 5 . Graphical representation of microarray data was generated using hierarchical clustering analysis and the BioConductor HCLUST tool . Further bioinformatics were used to check the expression of Vav2/Vav3-dependent mRNAs in normal skin and skin tumors . To that end , probesets within GSE21264 dataset corresponding to A1 and A2 gene clusters were analyzed from differential expression between normal tail , papillomas , and carcinomas using the microarray database indicated above and ANOVA analysis ( p≤0 . 01 ) . For those genes with more than one probeset significantly deregulated , we selected the one with the lowest p value for representation . The raw microarray data generated in this work have been uploaded to the GEO database ( www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=pjcxliqugwowqfq&acc=GSE40849 ) . Differences in tumor multiplicity and incidence were analyzed by the Mann-Whitney U test and the χ2 test , respectively . Other wet lab data were processed using the Student's t or the one tail Mann-Whitney tests . In all cases , p values lower than 0 . 05 were considered statistically significant . p values were represented in all figures as * ( when ≤0 . 05 ) , ** ( when ≤0 . 01 ) , and *** ( when ≤0 . 001 ) . Data obtained are given as the mean ± the s . e . m . | GDP/GTP exchange factors ( GEFs ) involved in Rho GTPase activation have been traditionally considered as potential anticancer drug targets . However , little is known about the best GEFs to inhibit in different tumor types , the pro-tumorigenic programs that they regulate , and the collateral effects that inactivation may induce in healthy tissues . Here , we investigate this issue in HRas-dependent skin tumors using genetic techniques . Despite the large number of Rho GEFs present in both normal and tumoral epidermis , we demonstrate that the co-expression of the exchange factors Vav2 and Vav3 is critical for the development of this tumor type . We also identify a previously unknown Vav-dependent autocrine/paracrine program that favors keratinocyte survival/proliferation and the formation of an inflammatory state during the initiation and promotion phases of this tumor . Finally , our results indicate that inactivation of Vav proteins is innocuous for the homeostasis of normal epidermis . Taken together , these results imply that Vav protein-based therapies may be of interest for skin tumor prevention and/or treatment . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"oncology",
"medicine",
"signal",
"transduction",
"biology",
"basic",
"cancer",
"research",
"molecular",
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] | 2013 | The Rho Exchange Factors Vav2 and Vav3 Favor Skin Tumor Initiation and Promotion by Engaging Extracellular Signaling Loops |
Blood is a remarkable habitat: it is highly viscous , contains a dense packaging of cells and perpetually flows at velocities varying over three orders of magnitude . Only few pathogens endure the harsh physical conditions within the vertebrate bloodstream and prosper despite being constantly attacked by host antibodies . African trypanosomes are strictly extracellular blood parasites , which evade the immune response through a system of antigenic variation and incessant motility . How the flagellates actually swim in blood remains to be elucidated . Here , we show that the mode and dynamics of trypanosome locomotion are a trait of life within a crowded environment . Using high-speed fluorescence microscopy and ordered micro-pillar arrays we show that the parasites mode of motility is adapted to the density of cells in blood . Trypanosomes are pulled forward by the planar beat of the single flagellum . Hydrodynamic flow across the asymmetrically shaped cell body translates into its rotational movement . Importantly , the presence of particles with the shape , size and spacing of blood cells is required and sufficient for trypanosomes to reach maximum forward velocity . If the density of obstacles , however , is further increased to resemble collagen networks or tissue spaces , the parasites reverse their flagellar beat and consequently swim backwards , in this way avoiding getting trapped . In the absence of obstacles , this flagellar beat reversal occurs randomly resulting in irregular waveforms and apparent cell tumbling . Thus , the swimming behavior of trypanosomes is a surprising example of micro-adaptation to life at low Reynolds numbers . For a precise physical interpretation , we compare our high-resolution microscopic data to results from a simulation technique that combines the method of multi-particle collision dynamics with a triangulated surface model . The simulation produces a rotating cell body and a helical swimming path , providing a functioning simulation method for a microorganism with a complex swimming strategy .
Blood vessels form a dense network throughout the human body with a total length of about 100 , 000 kilometers . The vessels diameter ranges from a few micrometers in capillaries to centimeters in the aorta and veins . Blood contains about 45% ( v/v ) cellular components , which flow with velocities ranging from mm s−1 in capillaries to m s−1 in the aorta . Viscous forces and laminar flow are dominant in blood circulation . In small capillaries , red blood cells ( RBC ) move in a single row , while in larger vessels they are thought to accumulate in the channel center due to hydrodynamic flow effects . Despite these fundamental characteristics , blood composition , temperature , pressure and oxygen content differ significantly between vertebrate species . Nevertheless , the parasitic unicellular trypanosomes prosper in the circulation of all vertebrate classes , from fish to bird . Thus , the parasites have evolved by adapting to very different bloodstream conditions . Some trypanosome species cause deadly diseases in livestock and man , e . g . the African sleeping sickness . Human African Trypanosomiasis ( HAT ) is an exemplary disease of poverty . There are only very few and rather ancient drugs available , which in addition are highly toxic . Most critically , in many sub-Saharan countries , health agencies have essentially lost control of HAT due to social and geopolitical problems; consequential poor public health implementation has resulted in widespread emergence of drug resistance . Thus , new medication is urgently needed . Unraveling the unique cellular and molecular features that distinguish trypanosomes from other eukaryotes has been a prime goal in the search for promising drug targets , however , success has been limited so far . An alternative approach appears to be to study the behavior of trypanosomes in their natural environment , namely the mammalian bloodstream , where the cells are constantly opsonized with antibodies and serum factors . The only barrier that shields trypanosomes from the host is an astonishingly dense cell surface coat , which is made of 107 copies of the same type of lipid-anchored , variant surface glycoprotein ( VSG ) . The trypanosomes use a system of antigenic variation to evade the host's immune response , whereby they randomly switch the exposed variant glycoproteins ( VSG ) and thus escape detection [1] . Due to the large number of structurally related but immunologically distinct VSGs , vaccination against HAT appears impossible . However , a second immune evasion strategy could prove more versatile for intervention as it directly involves cellular motility , which is thought to be essential for the parasites [2]–[4] . Bloodstream form ( BSF ) trypanosomes swim to rapidly remove surface-bound antibodies [5] , [6] . For propulsion , they utilize a single leading flagellum , which emerges from the flagellar pocket , follows the cell body , to which it is attached , and protrudes freely at the anterior of the cell ( Fig . 1A ) . The motion relative to the surrounding fluid generates a hydrodynamic drag , which causes antibody-bound VSGs to drift in the plane of the plasma membrane towards the trailing posterior end and into the flagellar pocket [5] . This invagination of the plasma membrane is the only place where endocytosis , an extremely fast process in trypanosomes , takes place [7] . The mechanism of hydrodynamic protein sorting greatly accelerates the diffusion-limited uptake of host antibodies , provided that trypanosomes exhibit fast directional motion . The aim of the present study was to elucidate in detail the biomechanics of trypanosome motility and relate this to the parasites life in blood .
The swimming behavior of cultivated trypanosomes appears to be highly variable , with few cells showing persistent directional motion ( Table 1 ) . Persistence is defined here by continuous directional movement for at least 150 micrometers , whereas non-persistent motion is characterized by shorter swimming trajectories interrupted by tumbling phases , during which the trypanosome stops translocation and changes its orientation [8] , [9] . In cell culture , the measured mean population velocity was relatively slow ( ( 5 . 7±0 . 11 ) µm s−1; SEM , n = 979 ) . This is slower than the velocity required for efficient removal of host antibodies [5] . In contrast , in blood the cells reached much higher velocities of 30 µm s−1 . Obviously , the physicochemical conditions in blood and cell culture differ greatly . Therefore , we systematically analyzed the motility of trypanosomes in varying environments . Possible factors that influence swimming behavior include chemical cues , oxygen content , pressure , viscosity , flow , confinement and presence of blood cells . In blood freshly drawn from infected mice , virtually all cells swam persistently ( Table 1 ) . In contrast , less than a third of cells were persistent swimmers in standard cell culture medium and velocities varied significantly . We found no difference in the swimming behavior of trypanosomes in blood of pre-infected and uninfected animals , ruling out that the parasites secrete a motility-promoting factor . Blood plasma or serum only marginally influenced the percentage of mobile cells ( Table 1 ) . Thus , chemical cues are unlikely to affect trypanosome motility in the bloodstream . We also observed the reduction of oxygen partial pressure to have no influence on motility . However , when we raised the viscosity of the cell culture medium , the number of persistent swimmers increased , as well as the velocity of trypanosomes ( Fig . 1B ) . Upon addition of 0 . 4% ( w/v ) methylcellulose the viscosity of the medium equals that of blood . Under these conditions the percentage of persistently swimming cells doubled and the mean population velocity almost tripled ( Fig . 1B–D ) . Trypanosomes were capable swimmers even when viscosity was raised to 4000 mPa s , three orders of magnitude higher than that of blood . This compares to mammalian spermatozoa , which are also adapted to move in a broad viscosity range . In sperm , the flagellar beat frequency is decreased in high viscosity medium , but the progressive velocity does not change , as kinetic efficiency rises [10] , [11] . This probably involves the interaction with microstructures contained in the methylcellulose solution [11] . The forces trypanosomes would need to exert in order to move through homogenous fluids of such high viscosity are significant . We assume that the increase in velocity , without the need to produce such high forces , can be explained by methylcellulose forming loose and quasi-rigid networks consisting of long , linear polymer molecules . This was suggested first by Berg and Turner in 1979 for bacteria , cilia and flagella [12] and subsequently mathematically developed for certain bacteria [13] . As these bacteria do , small organisms like trypanosomes , even though 3 to 4 times larger in diameter , also seem to be able to wriggle through these networks without experiencing the resistive force of the apparent macroscopic viscosity . We have estimated the minimal force that the flagellum generates to be 5 pN by bead displacement experiments ( Video S1 ) . We also found that trypanosomes produced a force of maximally 100 pN , as they were not able to bend the various poly-dimethyl siloxane ( PDMS ) -pillars used in our experiments . The force required for bending these pillars is similar to that needed for the deformation of RBC . This agrees with the fact that although RBC can be readily displaced by trypanosomes , erythrocytes cannot be deformed by the parasite ( Video S1 ) . Thus , the force exerted by the flagellum is at least one order of magnitude lower than reported previously [14]; the most likely numbers have recently been measured by optical trapping experiments and are below 10 pN [15] . In fact , for swimming , trypanosomes do not require larger forces; the absolute value of the propulsive force that has to be generated equals the viscous drag force acting in the opposite direction . The drag force F can be calculated by the Stokes equation , assuming the cell body to be a sphere with known radius: ( 1 ) where η is the dynamic viscosity of the medium , r is the radius of the sphere and υ is the swimming velocity . For a trypanosome ( r = 1 . 5 µm ) swimming in cell culture medium ( η = 0 . 95 mPa s ) at a velocity υ of 20 µm s−1 , the viscous drag force is 0 . 54 pN . The viscosity of blood directly depends on the concentration of cellular components [16] , [17] and the presence of particles renders blood a non-Newtonian fluid , meaning that its viscosity changes with flow speed . This fact , in addition to continuous , vigorous self-mixing and formation of erythrocyte stacks ( ‘rouleaux’ ) , makes it difficult to measure cell motility in blood directly [18] . Hence , we aimed to quantify the behavior of trypanosomes in environments that resemble aspects of blood physics , but are defined and more easily manipulable . Initially , we chose suspensions of different kinds of particles that produce viscous fluids similar to blood . We generally observed enhanced cell motilities in the presence of particles , independent of their size , shape and mass . Live and chemically fixed erythrocytes influenced trypanosome motion in a manner similar to polystyrene and metal beads or nanodiamonds ( Video S1 ) . However , blood is a self-mixing system , meaning that the position of cells is continuously randomized due to hydrodynamic flow under confinement . In order to simulate this complex situation we used continuous flow microfluidics , which to date however , is not compatible with high-resolution , quantitative microscopic imaging of fast moving objects in three dimensions . Therefore , we devised a scenario that resembles a “frozen” suspension . Trypanosome motility was measured in arrays of regularly aligned inert PDMS pillars in order to simulate the crowded environment of the bloodstream ( Fig . 2A ) . We observed a striking increase in the percentage of persistently swimming cells and higher velocities for cells mechanically interacting with pillars ( Fig . 2B ) . About 90% of trypanosomes exhibited a fast and directional motion in arrays of pillars whose size ( 8 µm ) and regular spacing ( 4 µm ) was comparable to RBC in blood ( Fig . 2C , D ) . A maximum speed of 40 µm s−1 was measured within these arrays , which is almost 8-times faster than the mean population velocity in cell culture . No difference was found when the experiments were conducted in trypanosome dilution buffer ( TDB ) instead of culture medium , ruling out that compounds from fetal calf serum were involved . We conclude that objects with the size and spacing of RBC are required and sufficient to promote maximum directional forward velocity of trypanosomes ( Table 2 ) and consequently , the effectual removal of cell-surface bound host antibodies . Noteworthy , in the presence of narrower spaced pillar arrays , as well as in artificial collagen networks , around half of the persistent swimmers were observed to move backwards , i . e . with the flagellum trailing . This behavior had never been observed for wild type cells , but only for motility mutants with the outer dynein arms of the axoneme missing [5] , [19] . It was tempting to speculate that pure mechanic resistance in close-meshed environments could initiate a simple but faultless trap escape mechanism by triggering trypanosome backward motion . In order to understand the complex swimming behavior , we detailed the mechanism of movement for trypanosomes swimming either forwards , backwards or tumbling . High-speed transmitted light microscopy imaging allows analysis of cell motility with sufficient resolution in space and time [20] . Here , we introduce fluorescence microscopy utilizing sCMOS technology , which combines kHz frame rates with very high sensitivity . High-speed fluorescence microscopy has the dual advantage of providing defined out-of-focus information as well as high-resolution structural content , allowing the derivation of three-dimensional information at unprecedented temporal resolution . Using fluorescence surface labeling and high speed imaging , we observed the exact course of the flagellum and the cell body around individual pillars during successive flagellar beats , unequivocally demonstrating the adaptation of cell morphology to the surrounding erythrocyte-sized obstacles ( Fig . 2D ) . Only forward swimming cells whose flagellar wavelength and amplitude , as well as the resulting dynamic curvature of the cell body matched the spacing between PDMS-pillars achieved maximum directional velocity . These cells exhibited continuous tip-to-base beats of the flagellum at a regular frequency of ( 18 . 3±2 . 5 Hz ) . The amplitude of the free flagellar tip , defined as half the maximum transverse displacement of the flagellum relative to the anterior-posterior cell axis , was ( 3 . 3±0 . 7 µm ) . This specifically allowed mechanical interaction with correspondingly narrow-spaced objects . Additionally , the moving cell body described a helical path of similar amplitude ( 3 . 6±0 . 2 µm ) , even without obstacles being present ( Fig . 3B ) , revealing that hydrodynamic drag alone translates into rotation of the entire cell . In fact , the trypanosomes exploited the mechanical resistance of pillars periodically acting on opposite sides of the cells for efficient locomotion , The rotation of the parasites allowed interaction of the flagellum with the environment equally in all three dimensions , thereby maximizing the probing space of the flagellar tip ( M . Engstler et al . , unpublished ) . It is the asymmetry of the cell body that causes trypanosomes to rotate . From its origin at the flagellar pocket to the anterior pole of the cell , the flagellum wraps around the cell body in a turn of approximately 180 degrees ( Fig . 3A ) ( Videos S2 , S3 ) . This turn is completed after a flagellar course of 11 µm on average ( Video S2 ) . The remaining process of the flagellum towards the anterior pole of the cell follows the convex side of the cell surface without wrapping around the body significantly . This means that the helical chirality of the trypanosome is based on a flagellum that makes less than one complete turn around the cell; so the trypanosome ( trypanon [gr . ] = auger ) itself does not resemble a corkscrew , but its mode of motion does ( Fig . 3B , C ) . The rotation becomes evident through high-speed fluorescence microscopy analysis . Tracking the relative position of the flagellum along the cell body reveals that it always entered the focal plane on the right side of the cell , respective to the direction of movement , and left it on the left side ( Video S4 ) . Furthermore , by time-dependent tomography , 3D-representations of cells were successfully calculated from high-speed time-lapse series . This novel approach can produce a valid 3-dimensional model of a given body only if this object rotates unidirectionally with constant angular velocity . For a trypanosome the angular rotation was determined to be ( 50±10 ) degrees per flagellar beat , proving that the parasites exhibit a rotating type of motion ( Fig . 4 ) . These results are perfectly consistent with the measured beat frequency of ( 18 . 3±2 . 5 ) Hz ( n = 60 ) , and a mean rotational frequency of ( 2 . 8±0 . 4 ) Hz ( n = 58 ) . This corresponds to ( 8±2 ) single flagellar beats for a full rotation . Video S5 shows representative high-speed data sets selected from several hundred xyt-image series . For persistently moving cells , the trypanosome flagellum produced waves that moved unidirectional from the flagellar tip to the base with a constant frequency of about 18 Hz . Bihelical flagellar waves were not observed [14] . While the amplitude was increasingly damped by the cell body , a postulated change in the frequency of the flagellar beat [21] could not be confirmed ( Fig . 5 ) . Due to the virtual absence of inertia at very low Reynolds numbers [22] , [23] , every single flagellar beat produced a distinct and immediate propulsive force . The resulting locomotion could be visualized in discrete steps , due to the simultaneous rotation of the cell body and the therefore helical path around the axis of movement ( Fig . 5A , Video S8 ) . The unidirectional rotation of the flagellar beat plane is illustrated in Fig . 5B , in comparison to a theoretical bihelical mode [14] . Although the overall impression of these models appears rather similar , the physics underlying these two types of motion are fundamentally different . The major propelling force of trypanosome movement is produced by the beat of the free anterior part of the flagellum . This force in the anterior direction , together with the hydrodynamic drag force consequently produced in the opposite direction , causes rotation of the posterior flagellar 180° left-hand turn and accordingly of the attached cell body . In this context it may be worth mentioning that we did not find any support for the existence of an undulating membrane , which is generally thought to drive trypanosome motion . For various reasons the existence of such a flexible , fin-like extension between cell body and the attached flagellum in T . brucei may be doubted . Firstly , to the best of our knowledge neither the literature nor our own experiments ( see for example Fig . 3A and Videos S2 , S3 ) provide any compelling microscopic evidence for the presence of an undulating membrane . Secondly , the physical tethering of the paraflagellar rod to parts of the subpellicular cytoskeleton renders the connection between flagellum and cell body inflexible to the extent of contrasting with the concept of an undulating membrane driving trypanosome motion . After having detailed the movement of continuously forward swimming cells , we examined trypanosomes not being persistently propelled by tip-to-base beats . In micropillar arrays , about half of swimming trypanosomes were observed to move backwards by effectively reversing their flagellar beat direction ( Video S11 ) . The unique switch to base-to-tip beats is characteristic of trypanosomes and has been postulated to accompany tumbling phases and reorientation of the cell body in cell culture [19] , [20] . Here , for the first time , we show that continuous base-to-tip beats result in persistent backward movement . The base-to-tip beating trypanosomes were translocated in the posterior direction with each beat and the cells rotated counter-clockwise , viewed in the direction of movement ( Video S11 ) . This means the flagellar propulsion and the consequent rotation of the cell body observed in forward movement were reversed in base-to-tip beating cells . The frequency for consecutive base-to-tip beats in backward moving cells ( 13 . 1±0 . 8 Hz ) was lower than for forward swimming parasites ( 18 . 3±2 . 5 Hz ) . The base-to-tip beats typically produced a more irregular wave pattern with frequently higher amplitudes than tip-to-base beats , enabling the flagellum to fold back against itself , producing a hook-like waveform instead of a regular sine-wave . Therefore , backwards swimming trypanosomes followed paths of variable amplitudes , dependent on the constraints that the viscous surrounding or the presence of micropillars presented . Persistent backward motion was only observed with cells swimming in collagen networks , in methylcellulose or between narrow-spaced pillar arrays ( Video S11 ) . Under conditions offering no confinement and hence no mechanical resistance , continuous base-to-tip beating was not sustained . Instead , base-to-tip and tip-to-base beats permanently alternated . This resulted in very short intervals of for- or backward motion interrupted by beat reversal . While beats of changing direction were initiated , the resulting flagellar waves were simultaneously propagated and thereby generated superimposed waveforms . Thus , the appearance of a so-called bihelical mode of trypanosome motion [14] in fact reflects transition periods , that do not contribute to , but , quite the contrary , interrupt directional motion . Importantly , the occurrence of base-to-tip beats was observed to result in rotational movement perpendicular to the anterior-posterior cell axis . In this way , even very few base-to tip beats interrupting the continuous tip-to-base beating of forward moving cells will alter the trypanosomes swimming direction ( Video S12 ) . In cell culture , i . e . in the absence of confinement , frequent reversals of the flagellar beat direction caused successive directional changes that led to the characteristic , albeit artificial tumbling movement of trypanosomes ( Video S13 ) . Many of our conclusions are based on the careful interpretation of high-resolution microscopic data , which is a complex procedure , given that it involves deducing 3-dimensional information from 2-dimensional image series . Therefore , we corroborated our experimental data with a simulation technique combining the recently developed method of multi-particle collision dynamics to simulate viscous fluid flow [24]–[28] with a triangulated surface model [29] . The cell body surface was shaped in such a way , that bending potentials could be applied along the long axis of the body , in order to simulate the stiffness of the trypanosomes cytoskeleton ( Fig . 6A , B ) . A flagellum was defined and a sine wave running from tip to base applied to it , exactly according to our experimental 3d-microscopy data ( Fig . 6C ) . The flow field created by the cell propelled the body effectively in the opposite direction to the flagellar wave . The simulation resulted in the rotation of the whole cell body and in a helical swimming path as observed in our experiments ( Fig . 7 , Video S14 ) . Furthermore , when a sine wave was applied to the flagellum running in the opposite direction ( base to tip ) , the simulated cell body rotated around axes perpendicular to the anterior-posterior axis ( Video S14 ) , exactly as observed above for trypanosomes experiencing intermittent flagellar beat reversals . Without mechanical interaction with the surrounding viscous fluid , the model cell body in the numerical simulations neither translocated along nor rotated around the anterior-posterior axis . This behavior confirms that the course of the flagellum along the cell body as well as the direction of flagellar waves are responsible for the basic steps of trypanosome motility , which are modulated in vivo by the physical micro-environment to produce the apparent complex swimming mode . Having established that the trypanosome's mode of motility is a consequence of its microenvironment leaves us with the question as to whether it is a genetically fixed trait . We have shown that inversion of swimming direction and flagellar beat reversal can be triggered by knock-down of a single gene product ( DNAI1 ) ( [5]; M . Engstler et al . , unpublished ) . For efficient interaction with the microenvironment , however , any gene product contributing to cell shape and stiffness should be considered , which renders a precise genetic analysis difficult . However , there is one last observation that might pave the way for a genetic screen . High-speed analyses clearly showed that all forward swimming cells displayed exactly the same type of rotational movement , in the absence or presence of obstacles ( Fig . 3B , Videos S4 , S5 , S6 , S7 , S8 , S9 , S10 ) . The amplitude and frequency of flagellar waves are identical for cells swimming in microstructures , in blood or in cell culture medium . However , the optimal transduction of force into speed is dependent on a three-dimensional array of obstacles . This means that the mechanical adaptation to the physical topology of the microenvironment could well be genetically fixed , enabling the cells to move with maximal velocities in their natural habitat . In fact , for trypanosomes the selective pressure to swim faster than in cell culture medium is evident; it is only at velocities greater than 20 µm s−1 that the hydrodynamic removal of host antibodies becomes effective .
The physical forces acting in and around cells are moving into the focus of attention , not only of biologists , but also of physicists and engineers . Hydrodynamic flow impacts on cell surfaces even at the scale of molecules [5] . A mechanism exploiting these forces is critical for the survival of a deadly parasite , the African trypanosome . The highly motile cells produce a hydrodynamic flow field that drags surface-bound host antibodies against the swimming direction towards the site of localized endocytosis . This mechanism of antibody removal is only effective if the trypanosomes move directionally with a certain threshold velocity [5] . This speed is usually not reached in cell culture . Therefore , we assumed that trypanosome motion in the natural habitat should be faster than under artificial conditions . So far , the effect of the microenvironment on the motion of trypanosomes , or other cells for that matter , has not been considered adequately . Moreover , it does not even appear to be clear exactly how trypanosomes swim . For more than 150 years , the parasites have been observed microscopically [30] , [31] , and their motion became accepted to be corkscrew-like . Thus , an alternative model of trypanosome swimming came as a surprise , in which the cells are driven by the progression of kinks along the cell body axis [14] . We have quantified in detail the biomechanics of trypanosome swimming , and could not confirm the proposed rocking-type of motion . By using high-speed and high-resolution fluorescence microscopy we show that the free segment of the attached flagellum generates a major part of the force required for directional movement . Since the cell is force and torque free at low Reynolds numbers , the generation of locomotive force by the flagellum is well described by the simple resistive force theory [32] . The force generated by each beat causes immediate translocation as “inertia plays no role whatsoever” [22] , [23] . The translocation results in directional laminar flow around the trypanosome cell body . The attached part of the flagellum largely contributes to the cellular asymmetry and chirality , which causes the moving trypanosome to rotate , as any chiral body will be forced to rotate in a laminar flow field . As has been shown by Shaevitz et al . for Spiroplasma [33] , a bihelical propulsion mechanism requires a periodic change of cellular chirality [34] . In other words , for trypanosomes to move in such a way the handedness of the cell body would have to change over time . This is certainly not the case , as we have analyzed several hundred 3D-fluorescence image data sets without identifying a single trypanosome cell with clockwise chirality ( Fig . 3A ) . The here proposed complex mechanism of trypanosome movement was confirmed by conducting elaborate computer simulations with size parameters defined by the experiments . The sinusoidal two-dimensional beat of the flagellum was applied to a trypanosome cell surface model , established on the basis of three-dimensional microscopic data . The cell model reacted to the flow field created by its motion and moved as observed in the experiments , rotating and describing a helical path . This analysis underlines the predictive and confirmatory power of computer simulations for analyzing complex biological traits such as cell motility . The overall picture of a moving trypanosome indeed resembles a corkscrew ( Fig . 3B , C ) , however , the flagellar tip itself produces a two-dimensional beat ( Fig . 5 ) . We conclude that the term “plane-rotational” would better describe the complex mode of trypanosome locomotion , even though this term may appear paradoxical at first glance . In order to investigate how the trypanosomes plane-rotational type of motility performs under conditions of crowding and confinement , we trapped the parasites in arrays of micropillars . The leading flagellum efficiently pulls the trypanosome through the obstacle matrix , the mechanical interaction between cell body and pillars accelerating the cells to significantly higher velocities . This speed equals that in blood and is sufficient for the hydrodynamic flow-induced antibody clearance in the host . Thus , trypanosomes exploit friction forces for efficient locomotion . These forces were surprisingly also seen to act in the reversal of the trypanosomes swimming direction . Unidirectional tip-to-base beats effectively propel the cell forwards , while interspersed base-to-tip beats lead to a change of swimming direction . Continued interference of base-to-tip beats causes cells to tumble , but in the presence of mechanical resistance , successive , uninterrupted base-to-tip beating can be sustained , which directly causes the trypanosomes to swim backwards , also reversing their rotational movement . Thus , the parasite exhibits versatile three-dimensional movement capabilities , directly exploiting the physical nature of its surroundings . These can easily be envisaged to be of avail for the trypanosomes movement through tight spaces , as the parasite has to be maneuvered through tissue spaces and has to traverse the blood-brain-barrier . The environmentally induced reversal of swimming direction is a foolproof way of avoiding dead ends . The fact that motility contributes to survival in the host and hence represents a virulence factor , points to strong selective pressure acting on the blood parasite . The plane-rotational trypanosome motility can be regarded as a genetically fixed adaptation to the crowded environment in the host . However , trypanosome motion is not an easily traceable quantitative trait , since not only the flagellum is causative for locomotion but also the shape and architecture of the entire cell . The half-turn attachment of the flagellum with its unique tip-to-base beat , the elastic paraflagellar rod , as well as the flexible , cage-like cytoskeleton jointly contribute to the complex motion pattern . Thus , one could argue that evolution has shaped the trypanosome cell for expedient locomotion in blood . This , however , is not entirely true , since throughout its complex life cycle the parasite experiences dramatically different micro-environmental conditions . In mammals , trypanosomes not only thrive in blood , but also swim in tissue spaces , in lymph and even in cerebrospinal fluid . Obviously , the flow regime and the degree of crowding in these areas vary greatly . Furthermore , the parasites have to leave the mammal with the blood meal of the tsetse fly . A series of developmental stage transitions takes place in distinct parts of the insect body . In order to survive the tour de force through the fly , the parasites must adjust their mode of motility . To study the cooperative action of molecular and environmental cues controlling the motion pattern during trypanosome development seems a rewarding task . The unusually homogeneous cell surface architecture and the ease of genetic manipulation render African trypanosomes ideal objects for studies at the crossroads of micro-/nanoflow physics , membrane biochemistry and genetics . Lastly , understanding the microenvironment-dependence of trypanosome motility could offer unforeseen ways of combatting one of the most neglected tropical diseases , the African sleeping sickness [35] .
Wildtype bloodstream form ( BSF ) Trypanosoma brucei brucei , strain 427 [36] , Molteno Institute Trypanozoon antigen type 1 . 6 , were cultivated in suspension at 37°C , 5% CO2 in HMI-9 medium , including a final volume of 10% FCS ( Sigma-Aldrich , Taufkirchen , Germany ) . Cells were kept in the exponential growth phase at a density less than 5×105 cells/ml by dilution with fresh culture medium . Bovine blood cells were washed in isotonic saline solution ( 0 . 9% w/v NaCl ) by centrifugation ( 3000 g , 5 min , 4°C ) and resuspended in FCS to a final volume of 45% v/v . Live cells were surface-stained with 1 mM of AMCA-sulfo-NHS ( Pierce , Rockford , IL ) or Atto488-NHS ( Atto-Tec , Siegen , Germany ) for 10 min , immediately before each experiment . The incubation was carried out on ice and cells were kept in the dark . Unbound dye was removed by washing twice with ice-cold TDB at 2000×g for 90 s . All images were acquired with a fully automated fluorescence microscope iMIC ( TILL Photonics , Gräfelfing , Germany ) , controlled by our own software ( written in C/C++ and Java; Heddergott et al . , in preparation ) and equipped with 100× ( NA 1 . 4 ) and 60× ( 1 . 45 NA ) objectives ( Olympus ) . Images were recorded with the CCD cameras pco . 1600 or sensicam . qe ( PCO AG , Kelheim , Germany ) . For high-speed light microscopy , a Phantom v9 . 1 camera ( Vision Research , Wayne , NJ ) was used and xyt-image series were acquired at 200–1000 fps . A frame-rate of 200 fps was found to be sufficient for quantitative analysis of the fast flagellar beat of trypanosomes . At this rate the minimum required sampling frequency with regard to the Nyquist-Shannon sampling theorem was over-fulfilled 5-times . For high-speed fluorescence microscopy , a beta-version of the sCMOS camera pco . edge ( PCO ) was used at frame-rates of 200–400 fps . For 3D-modeling of fixed cells , xyz-stacks were acquired in 100 nm steps . The cells were fixed in a final concentration of 4% w/v formaldehyde and 0 . 25% v/v glutaraldehyde in 0 . 1 M HEPES buffer over night at 4°C . The stacks were deconvolved using Huygens Essential software ( version 3 . 7 . 0 , SVI , Hilversum , Netherlands ) . 3D maximum intensity projection volume models were generated from these stacks , an edge detection filter ( Sobel ) was applied and the model was false-colored in Amira ( version 5 . 2 . 2 , Visage Imaging , Berlin , Germany ) . Animations of 3D-models and annotated Videos were produced with Amira or Imaris ( version 7 . 2 . 1 , Bitplane , Zurich , Switzerland ) . Flagella were traced and their length measured in Amira . Cells were imaged in a two-dimensional setup of ∼10 µm height between a microscopic slide and a 24×60 mm coverslip , or in free suspension in translucent channels of 200–800 µm height ( ibidi GmbH , Martinsried , Germany ) , allowing unrestricted motion of trypanosomes in three dimensions . We used regularly aligned arrays of chemically inert poly-dimethyl siloxane ( PDMS ) pillars consisting of at least several hundred single pillars distributed across an area of more than 1 mm2 . The pillars had a height of about 20 µm and were covered by a gas permeable film ( lumox , Greiner Bio-One GmbH , Frickenhausen , Germany ) . Arrays of different combinations of spacing ( 3–20 µm ) and diameter ( 5–12 µm ) were used . Cells were applied in a small volume of about 10 µl of TDB ( trypanosome dilution buffer: 20 mM Na2HPO4 , 2 mM NaH2PO4 , pH 7 . 7 , 20 mM glucose , 5 mM KCl , 80 mM NaCl , 1 mM MgSO4 ) onto the pillar array . The in vitro setup was chosen to exclude any undefined chemical cues . Only cells in the focus plane , set to the center of the pillar height , were included in the analysis . Xyt-image series were acquired for at least 30 seconds . From these , the position of individual cells was determined by center-of-mass calculations . Velocities were measured as the sum of Euclidian distances between the single points of these trajectories . All analyses were done using our own software written in Java ( Heddergott et al . , in preparation ) . Xyt-image series of directionally swimming trypanosomes were acquired at frame rates of 500–1000 Hz . Successive flagellar beats were analyzed and one image depicting the beginning of each beat was selected . The two-dimensional view of these images was compared and identical periodically repeating phases were identified . For example , in Fig . 4 , after six flagellar beats the cell had returned to its original spatial orientation . The images selected from this period of movement were used for the tomography , using the 3 ds Max software ( Autodesk Inc . , San Rafael , CA ) . The cell contours were traced in each image and extruded to a three-dimensional object . These 3D-representations of successive beats were then aligned to an anterior-posterior axis . The 3D-models were rotated around this axis by a constant angle per beat and the intersecting regions of the rotated models were calculated and extracted to produce a tomographic 3D-model of the original object . A correct three-dimensional model of a trypanosome cell body was only produced when the rotational angle per beat was in the range of ( 50°±10° ) and the rotation was unidirectional . In order to simulate the spindle-shaped trypanosome cell body we constructed a model cell body using vertices connected by elastic springs and also implemented a bending rigidity with the help of an appropriate bending potential [29] . In detail , we created 20 circles which were spaced with a unit distance along the long axis of the cell body . The body's radius is given by equation ( 1 ) . ( 1 ) Equation ( 1 ) thus defines the thickness of the cell body . As shown in Fig . 6A , along each circle created by equation ( 1 ) , 10 vertices are equally distributed with an angular displacement of π/5 radians . Each circle vertex was connected to the corresponding vertex of an adjacent circle by Hookean springs shown in Fig . 6A as the bond vector between the points A and B . All 10 vertices distributed over one circle were connected to the adjacent circles , except the ones that constitute the beginning and the end of the body . The potential for the Hookean springs is given by: ( 2 ) where κs is the spring constant and l0 is the equilibrium length of the springs . The integrity of the cell body was ensured by interconnecting all 10 vertices of each circle by springs shown in Fig . 6A as bonds between the points B , C and between the points B , D . In order to keep the cell body stable , relatively stiff springs were used throughout the simulations , with the spring constant κs = 107 in appropriate reduced units . A bending potential was applied along the dashed line shown in Figure 6B ( a ) of the cell body . In order to apply the bending potential , the 10 lines which make up the long axis of the cell body were discretized as shown in Figure 6B ( b ) . The bending potential that we applied is given by ( 3 ) where θ is the angle enclosed by the tangent vectors ti and ti+1 as shown in 6B ( c ) , θ0 is the equilibrium value of the angle and κb is the bending stiffness . This configuration mimics the microtubule system of the trypanosome , which runs along the long axis of the cell body . Most microtubules originate at the posterior end of the cell body but do not always extend to the complete length of the cell . This makes the anterior end of the cell body more flexible than the posterior end . To account for this property of the cell body in our model , the bending stiffness was reduced in steps towards the anterior end by a factor of 0 . 95 starting from the center of the cell body . The resulting bending stiffness at the end of the cell body is ( 0 . 95 ) 10κb = 0 . 6κb . The flagellum was attached to the cell body so that it runs straight from the posterior end up to the center of the cell body and then performs a rotation of π radians around the cell body as shown by the blue line in Fig . 6C . Through this flagellum a planar bending wave ( in our case a sine wave ) was passed , given by the potential [37]: ( 4 ) where κw is the strength of the bending wave and R ( α ) is a rotation matrix , which rotates a vector ti by an angle α about the local surface normal , A is the amplitude of the sine wave which is always kept as 1 in all the simulations , D is the distance from the posterior end of the flagellum to the point i on the flagellum , k = 2π/λ is the wave number , λ is the wavelength of the wave , ω is the angular frequency of the wave and t is the time in simulation units . The open posterior and anterior ends were each closed by a semi-sphere of the respective diameter . For the trypanosome's cell body , the length to thickness ratio is about 25 µm/3 . 5 µm = 7 . 14 . This ratio was closely matched in our model ( 7 . 32 ) and kept constant for all simulations reported in this work . The velocities and positions of the vertices with mass m = 1 were always given in dimensionless units . They were updated during a molecular dynamics ( MD ) step by the velocity Verlet algorithm [38] using the time step δtMD , ( 5 ) ( 6 ) Here ri , vi and are the respective position , velocity , and force of the ith vertex of the cell body . To perform the gradient of the spring , bending and bending wave energy Us+Ub+Uw with respect to ri , the energies were discretized in the position variables ri . In order to maintain a stable structure we had to keep δtMD≪1 , so in our simulation we always kept δtMD = 10−4 . The method of multi-particle dynamics [24]–[28] has been widely used to study fluid dynamic problems such as the sedimentation of colloids [39] , star polymers under Couette flow [40] , and the thermal diffusion of a semi-flexible sheet [41] , just to name a few examples . Biologically relevant systems have been addressed also , such as fluid vesicles under shear flow [42] , [43] , the dynamics of model swimmers called squirmers [44] , [45] , and swimming sperm cells both in two [37] and three [46] dimensions . In multiple particle collision dynamics ( MPCD ) the fictitious fluid particles ( point particles ) are distributed in a 3-dimensional simulation box Lbox with periodic boundary conditions . In Fig . 6D we show the average flow field generated by the model trypanosome during swimming in the MPCD fluid . We employed a MPCD version using the Anderson thermostat with angular momentum conservation [38] . We started with a thermally equilibrated fluid by choosing the velocity of each particle from a three-dimensional Gaussian distribution with variance 3kBT/m , where T is the temperature of the fluid and m the mass of the fluid particle . MPCD is divided into 2 different steps , one is called the streaming step and the other is called the collision step . In the streaming step the fluid particles are moved ballistically for a time δt . ( 7 ) Before each MPCD step we performed p MD steps of the cell body , where p = δt/δtMD . As a result of the MD steps and the following streaming step , fluid particles entered the cell body . All the fluid particles inside the cell body were tracked back to the surface of the cell body . Then each fluid particle was given the velocity of the nearest cell body vertex and moved for half the corresponding time step [47] , thereby implementing the no-slip boundary condition . This procedure was continued until all particles were located outside the cell body . The total momentum was conserved during all these collisions with the cell body , taking into account its center-of-mass velocity . In our simulations we always kept δt≤0 . 01 . The streaming step is followed by the collision step . In the collision step we divided the simulation box into cells of length a = 1 and in each cell we changed the velocity vi of the fluid particle according to the following collision rule , which conserves both linear and angular momentum of the cell: ( 8 ) Here the components of vi , ran are Gaussian random numbers , Nc is the number of particles in the cell and V ( t ) is the center of mass velocity of all particles in the cell . To conserve angular momentum the last term is added , where Π is the moment of inertia tensor of the particles in the cell , and ri , c = ri−Rc is the relative position of the particle i in the cell with respect to the center-of-mass mass position Rc of all particles in the cell . The collision also included the vertices of the cell body . Since δt≪1 , artificial correlations developed between particles of each cell [48] , [49] . To avoid them we performed a random shift of the cells before each collision step . In our simulation time was measured in units of . The advantage of the collision rule is that it also acts as a thermostat , which is very important in our case as the cell body is always pumping in energy into our system with its constant beating . One can show that this sequence of streaming and collision steps is equivalent to solving the Navier-Stokes equations . MPCD provides an analytic expression for the viscosity , which depends on the number of particles per cell . In our simulations , we always chose the number of particles per cell as ρ = 10 . The viscosity in the simulations is then given by the streaming ( ηst = 0 . 64δt ) and collision ( ηcl = 0 . 036/δt ) contributions and the total viscosity is η = ηst+ηcl . [50] . We tested our program code for two cases: First , we determined the diffusion coefficient Dsim of the passive cell body by monitoring its mean square displacement . We fitted the line 6 Dsim t to the data , where for Dsim we took the diffusion constant D of a cylinder whose radius is the mean radius of the cell body . The agreement is good ( Fig . S1A ) . Second , we determined the Stokes friction coefficient γ of a sphere of radius a = 4 by dragging it through the fluid ( viscosity 3 . 6 ) with various forces F , which we quantified by the Peclet number Pe . We obtained very good agreement with the exact value γ0 ( Fig . S1B ) . A detailed numerical analysis of the swimming trypanosome cell body in its viscous environment using the MPCD method is presented in [29] . Here , we formulate an estimate for the swimming speed v of our model trypanosome . In [29] we found that v/Lω scales with the sperm number , where L is the length of the cell body and ω the angular frequency of the wave propagating along the flagellum . The sperm number is defined as ( 9 ) where is the friction coefficient of a cylinder of length L and radius rc , which is the mean radius of the trypanosome cell body , and A is the bending rigidity of the body . The sperm number compares frictional to bending forces . In the present work we used δt = 0 . 01 which corresponds to a viscosity of 3 . 6 in MPCD units or in real units to a viscosity a factor of 50 smaller than in water . On the other hand , the angular frequency was ω = 2π*0 . 1 in MPCD units or 2π*330 Hz which is about a factor of 18 larger than the angular beat frequency of 2π*18 . 3±2 . 5 Hz reported in the experimental work . This means that instead of Sp = 0 . 9 in our simulations we have a sperm number Sp = 1 . 17 , when real values are used . From the inset of Fig . 8 in [29] we find for this sperm number v/Lω = 6*10−4 or v = 1 . 7 µm/s . Thus , the swimming speed is smaller than the typical experimentally measured speed of 5 µm/s by a factor of three . We stress that the detailed values depend on a careful tuning of the cell body parameters . Our main goal was to contribute to understanding the swimming strategy of the African trypanosome . To this end , so far we have only modeled cells with a fully developed flagellum attached to the cell body . The analysis of different cell cycle stages will be part of future work . Mice infection experiments are not regarded as animal experiments but as animal usage . The animal protocol used for infection of mice with trypanosomes adhered to German law ( Tierschutzgesetz §4 Abs . 3 ) . The experiments were approved by the appropriate German authorities ( Regierung von Oberbayern , Az 211–2531 . 4 ) . | African trypanosomes swim incessantly in the bloodstream of their mammalian host . We have asked the question how these parasites actually manage to swim and manoeuver in an environment that is so amazingly crowded by blood cells and that reveals rapidly varying fluid flow speeds that are 50–20 . 000 times faster than the trypanosome's swimming speed . Our experiments suggest an astute mechanism by which trypanosomes have perfectly adapted to their hostile microenvironment . We found that the pathogens can readily adjust the beating direction of their single flagellum in response to purely mechanical cues . In the blood they exploit the spacing and shape of blood cells for very efficient forward movement that is required for host antibody clearance . When the parasites get trapped , i . e . in the extracellular matrix , they reverse the beating direction and consequently move backwards . The mechanism of flagellar beat switch is unique in nature and represents a genetically fixed trypanosome virulence factor . By introducing innovative technological advances , we have been able to quantify this complex cell behavior with unprecedented spatial and temporal resolution . These include the first numerical simulation of a cell of this complexity , extending the protozoans suitability as a model organism for the regulation of flagellar and ciliary motility . | [
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] | 2012 | Trypanosome Motion Represents an Adaptation to the Crowded Environment of the Vertebrate Bloodstream |
Infections with cytomegalovirus ( CMV ) can cause severe disease in immunosuppressed patients and infected newborns . Innate as well as cellular and humoral adaptive immune effector functions contribute to the control of CMV in immunocompetent individuals . None of the innate or adaptive immune functions are essential for virus control , however . Expansion of γδ T cells has been observed during human CMV ( HCMV ) infection in the fetus and in transplant patients with HCMV reactivation but the protective function of γδ T cells under these conditions remains unclear . Here we show for murine CMV ( MCMV ) infections that mice that lack CD8 and CD4 αβ-T cells as well as B lymphocytes can control a MCMV infection that is lethal in RAG-1-/- mice lacking any T- and B-cells . γδ T cells , isolated from infected mice can kill MCMV infected target cells in vitro and , importantly , provide long-term protection in infected RAG-1-/- mice after adoptive transfer . γδ T cells in MCMV infected hosts undergo a prominent and long-lasting phenotypic change most compatible with the view that the majority of the γδ T cell population persists in an effector/memory state even after resolution of the acute phase of the infection . A clonotypically focused Vγ1 and Vγ2 repertoire was observed at later stages of the infection in the organs where MCMV persists . These findings add γδ T cells as yet another protective component to the anti-CMV immune response . Our data provide clear evidence that γδ T cells can provide an effective control mechanism of acute CMV infections , particularly when conventional adaptive immune mechanisms are insufficient or absent , like in transplant patient or in the developing immune system in utero . The findings have implications in the stem cell transplant setting , as antigen recognition by γδ T cells is not MHC-restricted and dual reactivity against CMV and tumors has been described .
Cytomegaloviruses ( CMV ) and their respective hosts have co-evolved over long periods of time . During this co-evolution the virus adapted perfectly to the respective host defense systems and vice versa . As a result , virus replication during primary infection is effectively controlled by a multilayered , in large parts redundant , innate as well as adaptive immune response and development of symptoms or disease is prevented [1–3] . In contrast , in individuals with a compromised or immature immune system human CMV ( HCMV ) remains as a significant pathogen . Congenital HCMV infection , for example , is the leading infectious cause of brain damage and sensorineural hearing loss in children [4] . Primary and recurrent HCMV infection also causes significant morbidity and mortality in transplant patients e . g . following hematopoietic cell transplantation [5] . During primary infection of immunocompetent hosts the virus evokes a strong innate and adaptive immune response , which ultimately leads to control of viral replication and establishment of latency [6] . In the adaptive cellular immune response CD8+ and CD4+ T cells are involved . Activation of antiviral CD8+ T cells is considered to be particularly important in this respect . Work with MCMV has shown that antiviral CD8+ T cells can control a primary infection [7] and prevent disease after adoptive transfer in immunocompromised animals [8] . The concept of adoptive transfer of CMV specific CD8+ T cells to prevent CMV-related disease was successfully introduced into clinical management of transplant patients [9] . The role of CD4+ T cells in protection from CMV infection is less clear . In the MCMV system , CD4+ T cells have been shown to be essential for viral clearance from the salivary gland , an important anatomical site for prolonged CMV shedding and transmission [10] . The antiviral effect is most probably mediated by IFN-γ secretion of activated CD4+ T cells [11] . In transplant patients a multifunctional effect of CD4+ T cells has been shown . They provide essential support for CD8+ T cell memory , secrete various cytokines and even kill infected cells [12] . Lastly , the adaptive humoral immune response is also involved in control of viral replication . Immunodeficient RAG-/- mice are protected against MCMV infection by passive transfer of polyclonal or monoclonal antibodies from infected donors or MCMV specific memory B cells [13–16] . Despite the large body of literature on protective capacity of individual immune effector functions that contribute to the control of CMV , none of the innate or adaptive immune functions seems to be essential for virus control . For example , in the MCMV model , long-term depletion of CD8+ T cells showed that CD8+ T cells are not required for clearance of a primary CMV infection and viral latency was initiated with similar kinetics in animals lacking CD8+ T cells [17] . Similar results were obtained following depletion of the CD4+ T-cell subset [10] . Likewise , the course of the primary CMV infection is similar in fully immunocompetent mice and mice lacking antibody [15] . On the other hand , RAG-/- or SCID mice succumb rapidly to the infection , demonstrating that the complete lack of an adaptive immune response is incompatible with virus control [14 , 18] . It is unclear whether the redundancy in immunological control of CMV is universal , i . e . the loss of any antiviral immune effector function can be compensated by others or whether the redundancy is restricted to a certain set of immune cells or antiviral effector function . The definition of critical components of the immune response that cannot be compensated for by others will be important to understand the complex immune control of this virus and may be valuable for the clinical management of the infection in situations where the immune system is not fully functional . Here we systematically explored the contribution of individual cell types of the adaptive immune system to virus control . Surprisingly , our data showed that mice lacking CD4+ and CD8+ T cells as well as B cells still can control the virus for long periods of time . Protection in these animals was found to rely on CD3+CD8−CD4− γδ T cells . Depletion of CD3+ T cells in mice lacking B cells abolished protection from CMV infection and most importantly adoptive transfer of γδ T cells into RAG-/- mice provided long term protection from the otherwise lethal course of the infection . The identification of γδ T cells as a protective component in the anti-CMV response adds another layer to the complex immune control of this virus which may have clinical implications in the transplant setting since the function of γδ T cells is not restricted by the MHC complex .
In a first set of experiments we analyzed the potential effect of a combined absence of CD8+ T cells as well as antibodies for the course of a primary MCMV infection . To this end CD8-/-JHT mice ( S1 Fig . and S1 Table ) were infected with 1×105 pfu of a luciferase–expressing MCMV suitable for in vivo detection of virus infected cells [14] . In CD8-/-JHT mice , luciferase activity was detected at day 3 post infection ( p . i . ) and day 7 p . i . which fell to background levels at day 9 p . i . ( Fig . 1A ) . In these mice , the course of acute infection was slightly prolonged as compared to animals in which either cell type was lacking individually or as compared to immunocompetent C57BL/6 animals [14] . Thus , in mice with a combined lack of CD8+ T cells and B cells acute MCMV infection can be controlled . To analyze a potential contribution of CD4+ T cells in animals lacking CD8+ T cells and B cells for the course of the infection , CD8-/-JHT animals were treated with 250 µg of mab YTS 191 on day -1 , 3 and 8 p . i . [15] . Absence of CD4+ T cells was confirmed by flow cytometry ( S2 Fig . ) . Following infection , the mice showed a markedly higher bioluminescence signal compared to CD8-/-JHT animals at day 7 p . i . ( Fig . 1A ) . However , at day 9 p . i . the signal was greatly reduced indicating control of virus replication in the animals . The control of infection in animals lacking both CD8+ and CD4+ T cells as well as B cells was surprising given the fact that RAG-/- animals , which do not contain functional T and B cells , are not capable of controlling MCMV [14 , 18] . Potential T cell subsets responsible for protection would include CD3+ NKT cells , CD4/CD8-double negative ( DN ) αβ T cells or γδ T cells . To test whether DN T cells were involved in control of MCMV infection , CD3+ cells were depleted in CD8-/-JHT animals . Depletion was confirmed by flow cytometry ( S2 Fig . ) . Following infection , anti-CD3 antibody treated animals showed markedly enhanced bioluminescence compared to untreated or anti-CD4 antibody treated CD8-/-JHT mice at days 7 and 9 p . i . , indicating loss of control of virus replication ( Fig . 1A ) . The bioluminescence signals from anti-CD3 treated animals were comparable to infected RAG-/- mice which exhibit a continuously increasing bioluminescence signal during the first 10 days p . i . ( [14] and Fig . 2B ) . To correlate the in vivo bioluminescence data with virus titers in individual organs , animals were sacrificed at day 13 p . i . and the virus load was determined in selected organs , using a luciferase-based assay . Viral organ titers supported the data from the in vivo imaging . Very low levels of viral titers were observed in CD8-/-JHT mice ( Fig . 1B ) . In CD4+ T cell-depleted animals , the viral titers in organs were slightly elevated at day 13 p . i . compared to CD8-/-JHT animals , indicating that the control of the infection during simultaneous absence of CD8+ , CD4+ and B cells is not as effective as in CD8-/-JHT mice ( Fig . 1B ) . Viral titers in CD3-depleted CD8-/-JHT mice were significantly increased and comparable to titers obtained in RAG-/- animals ( Fig . 1B and Fig . 2C ) . In survival experiments , CD8-/-JHT mice either in the presence or absence of CD4+ T cells exhibited long-term control of the infection further indicating efficient virus control in the absence of CD8+/CD4+ T cells as well as B cells ( Fig . 1C ) . In contrast , and in accordance with our previous results [14] , RAG-/- animals succumbed to the infection between days 21–25 ( Fig . 1C ) . Taken together , the results indicated that mice lacking B cells , CD8+ and CD4+ T cells are still able to control a primary MCMV infection . The protecting cell type in these animals most probably exhibits a CD3+ DN phenotype since protection was lost following depletion of CD3+ T cells . To test whether DN T cells have direct antiviral activity , a standard in vitro cytotoxicity assay was performed with αβ and γδ positive DN T cells in a functional chromium release assay ex vivo without restimulation of cells . To this end , DN TCRαβ+ and γδ T cells were purified from spleen and lymph nodes of infected CD8-/-JHT mice 4 weeks after infection and tested for their cytolytic activity . The results shown in Fig . 2A revealed that both cell types were capable of mediating cytotoxicity in a CD3ε-redirected cytotoxicity assay [19] . DN TCRαβ+ T cells showed negligible lysis of both early and late infected mouse fibroblast target cells ( MEF ) . In contrast , the γδ T cell fraction specifically lysed both types of MCMV infected MEF ( Fig . 2A ) . Early infected target cells were lysed to a higher degree than late infected target cells , indicating that infected cells express antigens that are target of γδ T cell cytotoxicity during all stages of infection but especially in the early phase after infection . To analyze whether the ability of γδ T cells to kill infected targets in vitro correlates to a protective potential of these cells in vivo , an adoptive cell transfer approach was used . To this end γδ T cells were purified from MCMV infected CD8-/-JHT donor animals . Purity assessed by flow cytometry reached >99% . In the experiment shown in Fig . 2B 800 , 000 cells/animal were infused into RAG-/- recipients that had been infected three days before . Animals that received γδ T cells 3 days after infection , exhibited increased bioluminescence signals between day 3 and day 7 p . i . that was comparable to the control group of mice that received no T cells . However , while in the control group the signal intensity further increased between day 3 and day 7 p . i . , the bioluminescence was markedly reduced in γδ T cell treated mice at day 9 p . i . , indicating control of virus replication . When the viral load in organs was assessed at day 12 p . i . , γδ T cell-substituted mice showed a significantly lower viral titer compared to untreated RAG-/- animals ( Fig . 2C ) . In a number of independent experiments using γδ T cell numbers for adoptive transfer ranging from 200 , 000 to 800 , 000 per recipient significant reductions of viral load in all organs analyzed were observed . The absence of CD4+ T cells in the recipient animals 12 days after infection was confirmed by flow cytometry ( S2 Fig . ) , strongly suggesting that the antiviral effect of the adoptively transferred cell population rests on γδ T cells . γδ T cells provided long-term protection , as RAG-/- mice that received 200 , 000 γδ T cells from MCMV-immune donors survived the infection for at least 50 days ( Fig . 2D ) . In contrast , control RAG-/- animals showed a significantly shorter survival after infection ( mean survival time 22 days ) . Interestingly , γδ T cells that were isolated from naive animals were not able to protect the animals from the lethal course of the infection ( Fig . 2D ) . To extend this finding and to investigate whether γδ T cells from wildtype C57Bl/6 mice can protect from MCMV infection after adoptive transfer we transferred 400 , 000 γδ T cells obtained from the spleen of C57Bl/6 mice that were infected with MCMV at least 4 weeks before or from uninfected C57Bl/6 mice . The protective capacity of γδ T cells from infected and uninfected wildtype mice was much more variable , presumably due to the extended time needed for the cell purification from a large number of donor animals . The results show that γδ T cells from infected wildtype mice could clearly reduce the virus load after adaptive transfer into RAG-/- mice and that they provided better protection than γδ T cells from naïve donor mice . The results reached significance only for the lung , however ( S3 Fig . ) . In addition we performed infections with TCRα-/- mice that completely lack αβ T cells . In these experiments we used MCMV157luc in which the MCK-2 mutation was repaired [20] and 106 pfu virus was injected i . v . This virus and infection dose and route caused an even more severe infection in RAG-1-/- mice and infected mice had to be euthanized 12 days after infection ( S4 Fig . ) . TCRα-/- mice , however , were able to control virus infection within 2 weeks with only residual virus infection detectable in the salivary glands ( S4 Fig . ) . The protection of TCRα-/- mice was long-lasting as infected mice survived for more than 5 month without any signs of sickness . In addition we analyzed whether a response after a secondary high dose infection with MCMV is improved in TCRα-/- mice . When mice received a second infection at day 21 after the primary infection with 106 pfu MCMV157luc i . v . , virus spread was controlled immediately and efficiently as hardly any increase of bioluminescence was detectable at 10 days after the second infection ( S5 Fig . ) . Taken together , the results show that γδ T cells are capable of controlling a primary MCMV infection in the absence of additional cells from the adaptive immune system . The results so far indicated a protective capacity of γδ T cells in the absence of additional components of the cellular and humoral immune response . The question arises , whether γδ T cells have also a role in the antiviral response in fully immunocompetent hosts . We addressed this question by using TCRδ-/- mice . Three days after MCMV-infection , TCRδ-/- mice showed significantly higher viral titers in all tested organs as compared to C57BL/6 mice ( Fig . 3 ) . Five days after infection , the difference was less pronounced , being significant only in spleen and liver . These data indicated that during the early phase of infection , the lack of αβ T cells in otherwise fully immunocompetent animals results in higher viral titers . As the production of interferon-γ ( IFNγ ) and interleukin 17 ( IL-17 ) by γδ T cells can influence the outcome of an immune response [21] we analyzed the intracellular IFNγ and IL-17 production during infection in spleen , liver and lung of CD8-/-JHT mice by flow cytometry . As shown in Fig . 4A a high frequency of IFNγ-producing γδ T cells was observed that remained relatively constant in spleen and liver during the infection . In the liver on day 7 after infection a transient increase of IFNγ-producing γδ T cells was observed . IL-17 producing γδ T cells were very rare in these organs , as described before [22] , and the frequency did not change during MCMV infection ( Fig . 4A ) . To examine whether IFNγ or IL-17 play a role in viral control , we performed adoptive transfer experiments as described above using RAG-/- recipients and sorted γδ T cells from previously infected CD8-/-JHT mice . Neutralizing antibodies against IFNγ or IL-17 or isotype control antibodies were given to the animals before and during the time course of the infection . Neutralizing anti-IFNγ antibodies significantly elevated the viral load in RAG-/- control animals that did not receive γδ T cells presumably because NK cell-derived IFNγ , which provided some protection effect , was blocked ( Fig . 4B , red symbols ) . Importantly , however , the protective capacity of γδ T cells was not significantly influenced by neutralizing anti-IFNγ antibodies ( Fig . 4B , blue symbols ) . Neutralizing IL-17 antibodies had no effect on the viral load in RAG-/- mice and did not influence the protective capacity of adoptively transferred γδ T cells ( Fig . 4C ) . The fact that adoptively transferred γδ T cells were able to reduce the viral load in different organs indicated that the protective fraction of the γδ T cells is widely distributed in the organism , most probably via the bloodstream . In the blood and secondary lymphoid system γδ T cells represent only a minor proportion of lymphocytes but they are much more frequent in epithelial-rich organs [23] . γδ T-cell populations were analyzed in peripheral blood , spleen , liver and lung of CD8-/-JHT mice after MCMV infection . Blood samples were taken from different mouse strains on days 0 , 3 , 7 , 10 , 17 , 24 and 32 p . i . and stained for CD3/4/8 , TCRαβ and TCRγδ . Absolute cell counts of γδ T cells were quantified by flow cytometry ( Fig . 5A ) . Between the tested mouse strains , namely C57BL/6 , CD8-/- , CD8-/-JHT and anti-CD4 treated CD8-/-JHT no substantial differences could be detected . γδ T-cell counts were reduced substantially on days 3 and 7 after infection in all strains . Following this general lymphopenia , most likely caused by the early production of type I interferon upon virus infection [24] , the population started to recover to preinfection levels 10 days p . i . and remained relatively constant until the end of the experiment at day 32 . Within spleen , liver and lung the numbers of γδ T cells moderately increased at day 14 and 21 after infection and returned to preinfection levels on day 28 ( Fig . 5B ) . To analyze whether γδ T cells proliferate in vivo after MCMV infection , short-term BrdU pulse experiments were performed at different time points after infection of CD8-/-JHT mice . Six hours after BrdU injection less than 1% of γδ T cells in the peripheral blood incorporated BrdU in uninfected mice , showing that the vast majority of γδ T cells are non-cycling in vivo ( Fig . 5C ) . In mice that were infected with MCMV a significant fraction of γδ T cells present in the peripheral blood incorporated BrdU 6 hours after injection showing that up to 20% of γδ T cells proliferated in vivo in response to MCMV infection . In the organs of infected mice γδ T cells also showed significant proliferation 2 weeks after infection in lymphnodes and liver . Whereas proliferation normalized 4 weeks after infection in spleen , lung and peripheral lymphnodes , in the liver elevated percentages of proliferating γδ T cells are noticeable ( Fig . 5D ) . These data clearly showed that a significant fraction of γδ T cells proliferate in response to MCMV infection in vivo . To phenotypically characterize the γδ T-cell population after infection we determined several known markers for γδ T cells in blood , spleen , liver and lung . As depicted in Fig . 6 , the frequency of CD44+ γδ T cells increased in all organs 14 days after infection , suggesting considerable and long lasting activation of γδ T cells in infected animals . The increased frequency of CD44+ γδ T cells coincided with a switch in the expression pattern of NKG2D , a lectin-like stimulatory receptor originally identified in NK cells [25] and CD27 , a costimulatory receptor of the TNF-receptor superfamily [26] , on the majority of γδ T cells in all organs analyzed ( Fig . 6 ) . Whereas before infection and during the early phase after infection ( days 0–7 p . i . ) the majority of γδ T cells were NKG2D- and CD27high a switch to NKG2D+ CD27low was observed at later times after infection in the majority of cells ( Fig . 6 and S6 Fig . ) . This switch of the phenotype remained constant during the observation period until day 28 p . i . , when MCMV infection was undetectable by bioluminescence . Thus , MCMV infection induced a significant and long-lasting activation and change in the phenotype of the majority of γδ T cells in secondary lymphoid organs and tissues in which MCMV infection is prominent . To determine whether in MCMV infected mice subpopulations of clonally restricted γδ T cells expand , as it has been described for human γδ T cell subpopulations [27] we first determined the Vγ usage by flow cytometry using currently available antibodies . As expected Vγ1 and Vγ4 cells ( according to the nomenclature of Heilig et al . [28] ) contributed to over 80% of γδ T cells in spleen , lymph nodes , lung and liver whereas Vγ5 and Vγ7 cells were hardly detectable in these organs . The only major change in the relative contribution of TCRγ subpopulations was an increase of Vγ1 cells from approximately 50% to up to 80% of all γδ T cells particularly in liver and lung of infected mice at 21 and 28 days after infection in CD8-/-JHT and TCRα-/- mice . To evaluate in detail whether the long-lasting activation and phenotypic change of γδ T cells correlates with a more focused Vγ usage we determined the Vγ repertoire in 5 different organs of two infected ( d28 ) and two uninfected CD8-JHT mice by 454 sequencing of Vγ-Cγ amplicons obtained from cDNA from sorted γδ T cells . As expected , sufficient numbers of reads from lung , liver and lymphoid tissues were obtained only for Vγ1 , Vγ2 , Vγ4 and Vγ6 recombinations . When all amplicons were analyzed for recurrent recombinations we found one major CDR3 clonotype for Vγ4 and Vγ6 present in all organs , constituting up to 60% and 90% of the respective Vγ4 and Vγ6 genes . Importantly , no major difference was observed between infected and uninfected mice ( S7 Fig . ) . Within Vγ1- and Vγ2-products we observed much more diversity particularly in lymphoid organs of uninfected mice . The five most prominent CDR3 clonotypes constitute less than 25% of the whole repertoire ( Fig . 7 ) . For the infected mice , however , a clear expansion of Vγ1- and Vγ2- clonotypes was found particularly in lung and liver ( Fig . 7 ) . In both mice 2 dominant clonotypes were found expanded in lung and liver and to a lesser extend these clonotypes can also be detected in the spleen of the same animal . In peripheral and mesenteric lymph nodes these clonotypes were not particularly expanded , however . Together , these results suggest a long lasting and focused accumulation of clonotypically related Vγ1- and Vγ2 cells after MCMV infection in the organs of virus persistence and latency [29] .
The control of CMV infections relies on multiple and redundant immune effector functions from the innate and the adaptive immune system [3] . In this report , we provide the first direct evidence that γδ T cells , which are regarded as innate-like cells with adaptive-like potential [30] , can provide protection against MCMV infection in the absence of other effector cells of the adaptive immune system , i . e . conventional αβ T cells or B cells . The protection against lethal infection conferred by γδ T cells after adoptive transfer into RAG-/- mice clearly showed some adoptive-like elements as only γδ T cells from MCMV-infected donors could provide protection whereas equal numbers of γδ T cells from uninfected animals were unable to provide significant protection in the RAG-/- mice . The fast and efficient control of MCMV after a high dose secondary infection in TCRα-/- mice can be regarded as an additional evidence for an adaptive and memory-like response of γδ T cells , which shares features to the NK cell response after secondary MCMV infection [31] . Whereas in the case of NK cells the invariant “innate” Ly49H receptor is accountable for the response , our data regarding a focusing of the Vγ1 and Vγ2 repertoire particularly in organs where MCMV is persisting suggest the establishment of a response that is selected for by antigen through the γδ antigen receptor formed by VDJ-recombination . It will be interesting to isolate and identify these receptors from infected mice and to generate transgenic mice to study those γδ T cells in a defined way during infection , similar as it has been done for virus specific αβ T cells and B cells . NKG2D is a C-type lectin found on NK cells and a fraction of γδ T cells and CD8+ αβ T cells . The markedly elevated frequency of NKG2D-positive γδ T cells that remained for several weeks after infection in the MCMV-infected animals could suggest an expansion of a subpopulation of NKG2D-positive γδ T cells that is present in relative low frequency in uninfected animals . An alternative explanation for the increased frequency of NKG2D-positive γδ T cells after infection is that it is merely a reflection of the activated state of the entire γδ T-cell population . It has been shown that NKG2D-expression is upregulated on all CD8+ T cells upon activation after anti-TCR stimulation and on the majority of antigen-specific CD8+ cells after virus infection [25] . The concomitant downregulation of CD27 on NKG2D-positive γδ T cells as well as the upregulation of CD44 further supports the view that the majority of the γδ T-cell population persists in an effector/memory state even after resolution of the acute phase of the infection . CD27-low/negative Vγ9δ2 cells have been shown to belong to the memory and effector/memory compartment in humans [32 , 33] . During MCMV infection γδ T cells contribute to protection as early as three days after infection as described here for wildtype mice . This goes hand in hand with the description as pre-activated and pre-programmed cells that offer a first line of defense [21] . We also saw activation of γδ T cells at much later timepoints during the infection corresponding to the second and later response of γδ T cells observed during murine influenza A infection [34] . Interestingly , also in the case of influenza infection , Vγ1+ T cells , which are normally localized to lymphoid tissues , dominate the later response . In addition , MCMV as a persisting virus might provide constant stimulation of γδ T cells particularly in the major target organs , causing an increase in cell numbers and phenotypic alterations . In this context it is remarkable that a certain γδ T-cell subpopulation expands in healthy aged HCMV carriers [35] . In general , the antigen specificity of the γδ T-cell receptor ( TCR ) recognition remains still enigmatic except for a few cases where clear biochemical binding data have been obtained [21] . In the context of recognition of herpes virus infected cells , early studies with γδ T-cell clones suggested specific recognition of a herpes virus glycoprotein I on infected target cells . Very recent data showed that a γδ T-cell clone with dual reactivity towards HCMV infected cells and epithelial tumors binds to a stress-regulated self-antigen , the endothelial protein C receptor [36] . Whereas only two reports so far established a potential function of γδ T cells in rodent CMV infection [37 , 38] , numerous publications associated γδ T cells with HCMV infections , particularly in renal allograft patients [27 , 39 , 40] , allogenic stem cell transplantation [41] and during HCMV infections of the fetus in utero [42] . However , a causal relationship between γδ T-cell responses and protection from CMV disease has not been established . The data presented in this report strongly argue that γδ T cells can provide an effective control mechanism of acute CMV infections , particularly when conventional adaptive immune mechanisms are insufficient or absent . These could include the developing fetus and the period following organ- or allogeneic stem cell transplantation . Recent developments in graft engineering of allogeneic stem cells for transplantation suggest that depletion of TCRαβ positive cells may have advantages over anti-CD3 depletion [43] . In addition to an anti-tumor effect that might be exerted by γδ T cells [36 , 44] , our findings strongly suggest that the anti-HCMV activities of γδ T lymphocytes could be of benefit for stem cell transplant recipients as suggested previously [41] . Because γδ T lymphocytes are not MHC restricted , adoptive transfer of these lymphocytes from the donor might well represent a new cellular immune-intervention strategy for allogeneic stem cell transplant patients at risk for HCMV infection and reactivation .
C57BL/6 mice were obtained from Charles River . C57BL/6 RAG-1-/- ( RAG-/- ) mice were obtained from Irmgard Förster ( University Munich ) and JHT mice [45] were a gift from Hans-Martin Jäck ( Division of Molecular Immunology , University Erlangen-Nürnberg ) . To obtain CD8-/-JHT double knockout mice , CD8α-/- ( CD8-/- ) mice [46] were obtained from The Jackson Laboratory and crossed with JHT mice and double-homozygous offspring were selected . TCRδ-/- and TCRα-/- mice backcrossed to C57BL/6 were obtained from The Jackson Laboratory . All mice were bred and maintained in the animal facility at the Franz-Penzoldt-Zentrum , University Erlangen under specific pathogen-free environment . MCMV157luc was described before [14] . Additional viruses were used either containing a repaired mutation in MCK-2 [20] or a deletion of m126-m129 . The three viruses showed no differences in replication in vitro or in experimental outcome . Virus was propagated and purified as described [47] . Virus titer was determined by end-point titration using indirect immunofluorescence on mouse embryonic fibroblasts ( MEF ) as described [14] . Individual mice were infected intraperitoneally ( ip ) with 1 × 105 plaque forming units ( pfu ) . In the experiment with TCRα-/- mice infection was done intravenously ( i . v . ) with 1 × 106 pfu of MCMV157luc in which the MCK-2 mutation was repaired as reported by Jordan et al . [20] . In vivo bioluminescence imaging and measurement of organ luciferase activity was done exactly as described [14] . After perfusion spleens , lymph nodes , livers and lungs were harvested . Livers and lungs were digested with 2 mg/ml collagenase D ( Roche Diagnostics , Mannheim , Germany ) and 100 µg/ml DNaseI ( Roche Diagnostics ) for 60 min at 37°C prior to single-cell suspension . Blood was collected in tubes containing Na heparin ( Ratiopharm ) . After erythrocyte lysis ( 5 min in 0 . 15 MNH4Cl , 0 . 02 M HEPES , 0 . 1 mM EDTA for organs or 10 min in BD FACS Lysing Solution ( BD Biosciences ) for blood ) and FcγR blocking ( 5 μg/ml rat anti-mouse CD16/CD32; clone 93 , eBioscience ) , cells were incubated in PBS , 2% FCS , 2 mM EDTA for 30 min at 4°C with varying combinations of the following antibodies: CD3-PE ( 17A2 ) , CD4-PE ( GK1 . 5 ) , TCRγδ-FITC ( GL3 , BD Biosciences ) , TCRβ-PE-Cy7 ( H57–597 , Biolegend , San Diego , CA ) , CD3-APC ( 145–2C11 ) , CD4-Alexa Fluor 700 ( GK1 . 5 ) , CD8-Alexa Fluor 700 ( 53–6 . 7 ) , CD27-FITC ( LG . 7F9 ) , NKG2D-PE ( CX5 ) , TCRγδ-PerCPeFluor 710 ( GL3 , eBioscience ) and Vγ3/5-FITC ( 536 , BD Biosciences ) . Antibodies Vγ1-FITC ( 2 . 11 ) , Vγ4-FITC ( 49 . 2 ) and Vγ7-FITC ( F2 . 67 ) were a kind gift from P . Pereira . To determine absolute cell numbers Trucount Beads ( BD Biosciences ) were added . FACS analysis was performed on LSRII or FACSCalibur machines ( Becton Dickinson ) running CellQuest software and analyzed with FACSDiva software or FlowJo ( Tree Star , Ashland , OR ) . For intracellular cytokine staining single cell suspensions were prepared and cells were incubated with the Cell Stimulation Cocktail ( plus protein transport inhibitors , eBioscience ) diluted 1:500 in cell culture medium for 4 hours at 37°C . After stimulation cell surface markers were stained as described before . For fixation and permeabilization the Fix&Perm Cell Permeabilization Kit ( An Der Grub Bio Research GmbH ) was used according to the manufacturer´s specifications . Intracellular cytokines were stained with anti-IFNγ-APC ( clone XMG1 . 2 ) or anti-IL-17-APC ( clone eBio17B7 ) ( eBioscience ) diluted 1:100 in reagent B of the kit . For the in vivo neutralization of cytokines , γδ T cell transfers were conducted as described above . In addition neutralizing antibodies against IFNγ ( clone XMG1 . 2 ) , IL-17 ( clone 17F3 ) or their isotype controls ( rat IgG1 and mouse IgG1 respectively ) were administered . All in vivo antibodies were purchased from BioXCell . Mice were injected i . p . with 500 µg of antibody in 200 µl PBS every third day during the course of the experiment , starting one day before the γδ T cell transfer ( day 2 after infection ) . For in vivo depletion of CD4+ T cells , mice were injected ip with 250 µg of the monoclonal antibody YTS 191 [48] on day -1 , 3 and 8 after infection; for long term depletion with 300 µg antibody on day -1 , 3 7 , 17 and 27 after infection . For depletion of CD3+ T cells 300 µg of YTS 191 were injected on day -4 and 250 µg of the monoclonal antibody 145–2C11 were injected on day -1 and 2 after infection ( BioXcell; ) . For analysis of whole blood lymphocytes 2 drops of blood were taken from the tail vein in heparinized tubes ( Greiner bio-one ) . Equal volumes of whole blood and buffer ( PBS , 2% FCS ) containing 2 , 500 Truecount counting beads ( BD Biosciences ) and staining antibodies were mixed . BD FACS Lysing solution ( BD Biosciences ) was added . The mixture was analyzed by flow cytometry . Purified TCRαβ+ or TCRγδ+ effector cells were cultured overnight in RPMI-1640 medium with glutamine , penicillin 100 U/ml , streptomycin 0 . 1 mg/ml , 5 µM β-ME , 10 mM HEPES , 7 , 5% FCS and 20U/ml interleukin-2 . Cytolytic activity was measured by a standard 4-h 51Cr release assay with graded numbers of effector cells and with 1 , 000 target cells per 0 . 2-ml microwell . Target cells were 51Cr-labeled MEF infected with centrifugal enhancement with 0 . 2 pfu of MCMV per cell in the presence or absence of phosphonoacetic acid for 22 h . CD3ε producing B cell hybridoma cells ( 145–2C11 ) were used as targets to measure the total cytolytic potential of an effector cell population by antigen-independent polyclonal signaling via the TCR-CD3 complex [19] . Single-cell suspensions of spleens and lymph nodes from at least 6 weeks infected CD8-/-JHT mice were stained with antibodies against CD3 , CD4 , TCRαβ and TCRγδ ( see above ) . CD3+ , CD4− , TCRαβ+ or TCRγδ+ cells respectively were isolated by fluorescence activated cell sorting using a MoFlo cell sorter ( Cytomation ) and analyzed for purity . Purity >99% was achieved . Purified cells were either used for chromium release assay or adoptively transferred into the tail or ocular vein of RAG−/− mice 3 days after infection . Absence of contaminating TCRαβ+ cells was confirmed by flow cytometry in all animals analyzed for organ titer or survival . Mice with detectable contaminations of TCRαβ+ cells were excluded from the analyses . Mice were given i . p . injections of 1 mg BrdU . 6 hours after BrdU injections peripheral blood , or cells from spleen , peripheral lymphnodes , liver and lung were harvested and the incorporation of BrdU into the dividing cells’ DNA was determined by the manufacturer’s protocol ( FITC BrdU Flow Kit , BD Pharmingen ) after surface staining for CD3 and γδTCR . For high throughput 454 sequencing , γδ T cells from spleen , peripheral ( inguinal , brachial , axillary , superficial ) lymph nodes , mesenteric lymph nodes , liver and lung of individual mice were sorted by FACS . Two naive mice and two mice four weeks after infection were compared . RNA of γδ T cells from separate organs was isolated with the RNeasy Mini Kit ( Qiagen ) after homogenizing cells with QIA shredder columns ( Qiagen ) . Before cDNA synthesis with the Transciptor High Fidelity cDNA Synthesis Kit ( version 6 . 0 , Roche ) volume of the samples was reduced through centrifugation in a SpeedVac centrifuge ( Eppendorf ) . cDNA of individual organs was used for polymerase chain reactions . Primers are listed in S2 Table . For each organ sample five PCRs were carried out and products of every organ were tagged with a specific barcode ( multiplex identifier , MID ) during PCRs . Reactions contained 4 µl of cDNA template , 3 µl RediLoad ( Invitrogen ) , 1 µl of a 5 µM stock of each primer , 12 µl 5-Prime polymerase mix 2 , 5x ( 5 PRIME GmbH ) and 9 µl water to obtain a reaction volume of 30 µl . After heating the reaction mixture for 7 min at 94°C , 38 circles under following conditions were performed: 60 sec at 94°C , 60 sec at 54°C and 30 sec at 72°C . 5 min at 72°C after the last circle allowed final elongation . 10 µl of each reaction were analyzed on an agarose gel and 20 µl were purified with QIAquick columns ( PCR purification kit , Qiagen ) after pooling the five reactions of one organ . Pools of different organs were adjusted to the same DNA concentration and combined . To reduce the volume , DNA was precipitated with ethanol . Next Generation Sequencing was performed on the Roche 454 platform by MWG eurofins . After sorting for MID tags for the different samples , individual FASTA files for each individual tissue sample containing 1 . 000–6 . 000 high quality reads were analyzed on the IMGT/HighV-QUEST platform [49] . Output files were imported in Microsoft Excel and the Vγ gene usage , CDR3 length and amino acid composition was analyzed for the different samples filtered for sequence reads that contain functional recombinations using the PivotTable function of Excel . Statistical analysis was performed with Prism 6 ( Graph-Pad Software , Inc . ) . The Mann-Whitney test for the comparison of two groups was used . For the analyses of survival data the Mantel-Cox logrank test was used . The study was performed in strict accordance with German law ( Tierschutzgesetz ) . The protocol was approved by the Committee on Ethics of Animal Experiments at the Bavarian Government ( Az . 54–2532 . 1–57/12 and Az . 54–2532 . 2–3/08 ) . All efforts were made to minimize animal suffering . | Cytomegalovirus is a clinically important pathogen . While infection in hosts with a functional immune system is usually asymptomatic , the virus can cause significant morbidity and mortality in individuals with an immature or suppressed immune system . The virus causes severe clinical complication in transplant recipients and congenital CMV infections are the most common infectious cause of neurological disorders in children . Multiple layers of innate and adoptive immunity are involved in the control of CMV and single deficiencies of one immune cell type can be compensated by other immune cells . Expansions of γδ T lymphocytes , which are regarded as innate-like cells with adaptive-like potential , have been shown to be associated with CMV infections in human transplant patients and neonates . Their role in protective immunity against CMV has been unclear , however . Here we show direct evidence in the murine CMV model ( MCMV ) that γδ T lymphocytes can provide protection against a lethal MCMV infection in the absence of any other cells of the adoptive immune system . Upon infection , γδ T lymphocytes undergo a significant expansion and a prominent and long-lasting phenotypic change . These findings have implications for the development of new cellular therapy regimens in CMV infections in the transplant setting that should be evaluated in the future . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Control of Murine Cytomegalovirus Infection by γδ T Cells |
The transcription factor Oct1/Pou2f1 promotes poised gene expression states , mitotic stability , glycolytic metabolism and other characteristics of stem cell potency . To determine the effect of Oct1 loss on stem cell maintenance and malignancy , we deleted Oct1 in two different mouse gut stem cell compartments . Oct1 deletion preserved homeostasis in vivo and the ability to establish organoids in vitro , but blocked the ability to recover from treatment with dextran sodium sulfate , and the ability to maintain organoids after passage . In a chemical model of colon cancer , loss of Oct1 in the colon severely restricted tumorigenicity . In contrast , loss of one or both Oct1 alleles progressively increased tumor burden in a colon cancer model driven by loss-of-heterozygosity of the tumor suppressor gene Apc . The different outcomes are consistent with prior findings that Oct1 promotes mitotic stability , and consistent with differentially expressed genes between the two models . Oct1 ChIPseq using HCT116 colon carcinoma cells identifies target genes associated with mitotic stability , metabolism , stress response and malignancy . This set of gene targets overlaps significantly with genes differentially expressed in the two tumor models . These results reveal that Oct1 is selectively required for recovery after colon damage , and that Oct1 has potent effects in colon malignancy , with outcome ( pro-oncogenic or tumor suppressive ) dictated by tumor etiology .
Oct1/Pou2f1 is a widely expressed POU domain transcription factor related to the embryonic stem cell master transcription factor , Oct4 [1 , 2] . Oct1 promotes glycolytic metabolism and mitotic stability [3–5] . It also promotes poised gene expression states , i . e . the ability of transcriptionally silent target genes to be readily induced in response to different cues [6 , 7] . Oct1 loss is associated with increased oxidative metabolism , elevated reactive oxygen species , hypersensitivity to oxidative and genotoxic stress , and a modest increase in abnormal mitoses [3 , 4 , 8–10] . Oct1 loss does not compromise cell viability , affect immortalization by serial passage or reduce growth rates in standard culture , however oncogenic transformation in soft agar is strongly reduced [3] . In a well-characterized Tp53 null mouse model , loss of even one Oct1 allele suppresses thymic lymphoma [3] . In a variety of malignancies , Oct1 motifs are enriched in coordinately activated genes [11–14] . These results indicate that Oct1 plays important roles in stress responses and tumorigenicity . We and others have shown that Oct1 promotes somatic and cancer stem cell potency in different systems [9 , 15 , 16] . For example , in the blood system Oct1 loss allows for primary hematopoietic engraftment but compromises serial transplant capacity [9] . Despite the connection between Oct1 , stem cells and oncogenic potential , Oct1 ( Pou2f1 ) mRNA is not elevated in stem cells [17] . Instead , Oct1 is regulated post-translationally , e . g . at the level of DNA binding specificity [5] , sub-nuclear localization [4 , 10 , 18–20] , and protein stability [15 , 21] . Regarding the latter , two Oct1 ubiquitin ligases have been identified , Trim21 and BRCA1 [15 , 21] . A third activity , CKIP-1 , negatively regulates Oct1 by enhancing its association with the proteasome activator REGγ [22] . Although Oct1 is widely expressed , Oct1 protein levels are elevated in mouse and human gastric , small intestine and colon stem cells [9 , 23 , 24] . In malignancy , multiple studies identify a correlation between tumor aggressiveness and elevated Oct1 expression [9 , 11 , 21 , 25–31] , including in the GI tract [24 , 32–34] . Here , we use a conditional Oct1 allele together with two gut stem cell Cre-drivers to determine the role of this protein in the maintenance and regeneration of normal gut cells , and in colon malignancy . The Cre-drivers are tamoxifen-inducible to provide both tissue-specific and temporal control over Oct1 deletion . We find that Oct1 is dispensable for colon homeostasis , but required for the colon epithelium to recover from injury . In the small intestine , Oct1 deletion from Lgr5+ cells in vivo allows for the establishment of cultured gut organoids from isolated crypts , but not their maintenance following passage . In a chemical model of colon tumorigenesis , Oct1 loss greatly diminishes tumor incidence and size . The tumors that do emerge in this model have escaped Oct1 deletion , indicating a requirement for Oct1 . In contrast , a model of colon cancer driven by loss-of-heterozygosity ( LOH ) shows a progressive increase in tumor number , though not of grade , as one or both Oct1 alleles are lost . Comparison of gene expression signatures in the two models identifies a set of genes correlating with the model of origin , indicating that the two models have distinct molecular features . This set is enriched in direct Oct1 targets identified using ChIPseq in human colon cancer cell lines .
Oct1 is widely expressed in colon epithelial cells , but is more strongly expressed in cells at the crypt base that express Lrig1 , which marks the stem cell compartment [9 , 35] . We crossed an Oct1 ( Pou2f1 ) conditional allele [6] to Lrig1-CreERT2 [35] , which upon tamoxifen treatment ( single injection of 2 mg in corn oil by gavage , see methods ) induces Cre activity in stem cells of the colon . Four weeks after treatment , the colon epithelium was superficially normal despite ~90% deletion of Oct1 ( Fig 1A ) . Oct1 immunoblot using isolated colonic crypts and quantification of Oct1 immunohistochemistry ( IHC ) from multiple mice confirmed global deletion ( Fig 1B and 1C ) . Non-epithelial cells in the same tissue sections retained staining ( Fig 1A , asterisks ) . There were crypt structures that escaped deletion ( red arrow ) , but these were rare indicating that Oct1 deletion efficiency was higher than reported previously for Rosa26-LacZ reporter mice [35] . Crypts lacking Oct1 appeared normal compared to adjacent ones that had escaped deletion and compared to those from mice prior to tamoxifen treatment ( Fig 1A , bottom ) . Quantification of crypt depth from multiple mice indicated a small but statistically significant decrease ( Fig 1D ) . In contrast , quantification of goblet cell numbers based on Alcian blue staining showed no difference ( Fig 1E ) . Oct1 deletion and normal colonic architecture could be maintained for 150 days ( Fig 1F and 1G ) . To study proliferation in this tissue , we performed phospho-histone H3 ( pH3 ) IHC on tissue from mice pre-tamoxifen and four weeks post-tamoxifen . There was little difference in proliferation in crypts lacking Oct1 ( Fig 1H and 1I ) . To study the stem cell compartment , we performed immunofluorescence ( IF ) using Lrig1 antibodies . We found that under homeostatic conditions , Lrig1 intensity and numbers of Lrig1+ cells were normal ( Fig 1J and 1K ) , consistent with the normal architecture of the colon over long periods . Overall , these results show that Oct1 loss has minimal effect on the colon epithelium under homeostatic conditions . To determine if Oct1 behaves similarly in the small intestine , we deleted Oct1 using Lgr5-EGFP-IRES-CreERT2 mice [36] . These mice express tamoxifen-inducible Cre and GFP under the control of the native Lgr5 gene , which is expressed in stem cells of the small intestine . Lgr5 encodes a R-Spondin receptor that stabilizes Frizzled receptors , allowing for increased Wnt signaling [37] . As with the colon , deletion of Oct1 in the small intestine preserved a normal-appearing small intestine in H&E and Alcian blue , despite efficient deletion from the epithelium ( Fig 2A ) . Although IHC is not very quantitative , prior to tamoxifen treatment we observed weaker Oct1 staining in the small intestine compared to the colon . Staining was more robust in cells at the crypt base ( arrows ) and tapered off in the villi ( Fig 2A , arrows , Fig 2B ) . Quantification of Oct1 deletion from four pre-tamoxifen and six post-tamoxifen-treated animals confirmed robust Oct1 deletion with tamoxifen ( Fig 2C ) . Consistent with the normal appearance of the small intestine , quantification of average crypt and villus length and goblet cell numbers revealed few differences ( Fig 2D and 2E ) . We also identified Paneth cells at the crypt base in pre- and post-tamoxifen treated mice ( Fig 2F , arrows ) . Across multiple mice , no differences in Paneth cell numbers per crypt were identified ( Fig 2G ) . To study proliferation in the deleted small intestine under homeostatic conditions , we performed pH3 IHC from mice pre- and post-tamoxifen treatment ( Fig 2H ) . Consistent with the normal size of the crypts and villi , no differences in mitoses were noted ( Fig 2I ) . Lgr5 staining intensity and numbers of cells were also similar with and without tamoxifen ( Fig 2J and 2K ) . Cumulatively , these results indicate that Oct1 loss results in minimal differences in small intestinal architecture under homeostatic conditions . DSS models are not widely used in the small intestine , as typical treatments generate less damage compared to the colon [38] . To determine whether Oct1 has effects in the small intestine under non-homeostatic conditions , we used cultured small intestinal organoids . In vitro cultured intestinal organoids are a powerful tool to study intestinal stem cells and their differentiated progeny . They are relatively easy to culture and manipulate [36 , 39] . We profiled the effect of Oct1 loss in the small intestine using organoids from mice 4 weeks post-tamoxifen treatment . Organoids were generated directly ex vivo from intestinal crypts . No difference in size , viability or morphology were noted ( Fig 3A , primary culture ) . However , upon passage Oct1fl/fl;Lgr5-EGFP-IRES-CreERT2 , disaggregated crypts were unable to regenerate new villus and crypt structures to generate complete organoids ( Fig 3A , after passage ) . In contrast , control Lgr5-EGFP-IRES-CreERT2 organoids grew normally following passage . Quantitatively , these changes manifested most strongly as a reduction in the numbers of crypt domains per organoid after passage ( Fig 3B ) . Reductions in crypt length and organoid diameter were also noted in passaged Oct1 deficient organoids ( Fig 3C ) . To test if Oct1 play a role in gut regeneration , we next treated Oct1fl/fl;Lrig1-CreERT2 mice and Lrig1-CreERT2 control mice with 2 . 5% DSS to damage the GI tract and mobilize gut stem cells to regenerate the epithelium ( Fig 4A ) . Mice lacking Oct1 in their colon trended towards greater sensitivity to DSS treatment , though this was not statistically significant ( Fig 4B , day 0–10 ) . Upon switching from DSS to water at 10 days , control mice Oct1 rapidly began to gain weight , while mice lacking Oct1 in the colon continued to lose weight ( Fig 4B , day 12–15 ) and did not recover an epithelial layer ( Fig 4C ) . The failure to regenerate was associated with increased colitis ( Fig 4D ) and loss of barrier function with microbial infiltrate in the spleen ( Fig 4E ) . Collectively , the data indicate that the colon epithelium does not recover from DSS-mediated damage in the absence of Oct1 . Colon cancer mimics a chronically regenerating state in many respects [40 , 41] . To test if Oct1 loss protects mice from malignancy , we used Oct1fl/fl;Lrig1-CreERT2 mice together with a chemical carcinogenesis model driven by the DNA alkylating agent azoxymethane ( AOM ) [42 , 43] ( Fig 5A ) . Colon tumors were efficiently generated in control mice but were fewer in number and smaller in size using Oct1 deleted mice ( Fig 5B ) . Quantification from 6 experimental and 4 control mice is shown in Fig 5C . Control Lrig1-CreERT2 tissue showed robust Oct1 expression in both tumor and gross uninvolved ( GU ) areas ( Fig 5D ) . However , the low-grade Oct1fl/fl;Lrig1-CreERT2 tumors that did occur showed a higher proportion of tumor tissue that retained Oct1 expression compared to GU tissue in the same section ( Fig 5D ) . Quantification from multiple mice indicated that ~90% of tumor tissue retained Oct1 staining , while only ~10% of GU tissue did so ( Fig 5E ) . This result indicates a selection against Oct1 deletion , consistent with Oct1 promotion of tumorigenicity in this model . Oct1 functions physiologically not to promote tumors , but rather to promote stem cell potency . The stem cell properties that Oct1 promotes are largely pro-oncogenic , but in one respect Oct1 can be tumor suppressive: like its paralog Oct4 [44] , Oct1 promotes mitotic stability in some systems [4] . Mitotic stability is a hallmark of stem cells [44–46] . To test the hypothesis that Oct1 loss can accelerate tumor initiation in models of malignancy dependent on mitotic errors and LOH , we used conditional deletion of the Apc gene , which is mutated in a large proportion of human colon cancers [47] . Over time , Apc LOH results in adenocarcinomas in the distal colon that mimic human disease in many respects [35] . We crossed Apcfl mice [48] with Oct1 ( Pou2f1 ) conditional mice , generating an allelic series of Oct1+/+ , Oct1+/fl , and Oct1fl/fl Lrig1-CreERT2 mice with heterozygous Apcfl . Mice were followed for 100 days post-tamoxifen treatment before sacrifice . Progressive deletion of one or both Oct1 alleles progressively increased tumor number in this model ( Fig 6A ) . Quantification of H&E sections from five animals confirmed that tumor number was increased ( Fig 6B ) . Using Oct1 IHC we found that Oct1 was again efficiently deleted from normal ( gross uninvolved ) crypts ( Fig 6C ) , however in contrast to AOM-DSS-mediated tumors , Oct1 was also deleted in most tumor cells ( Fig 6C ) . We also assessed total β-catenin by IHC . Apc protein restricts β-catenin by ubiquitin-mediated degradation [49] , and hence Apc LOH would be predicted to augment β-catenin specifically in tumors in this model . As expected , we observed accumulated β-catenin , including nuclear β-catenin , in the tumor cells in both control and Oct1-deleted tissue ( Fig 6C ) . To study tumor aggressiveness in the control and knockout tumor and adjacent GU tissue , we performed pH3 IHC . The number of mitotic events was low , and equivalent in the presence or absence of Oct1 ( Fig 6D ) . Across multiple animals , no significant differences were noted in mitoses per unit area in tumor tissue , or mitoses per crypt in GU ( Fig 6E ) . Consistent with this result , pathological scoring of tumor sections from Oct1+/+ , Oct1+/fl , and Oct1fl/fl Lrig1-CreERT2 mice with heterozygous Apcfl indicated that despite the increased tumor incidence with Oct1 knockout , there were no significant differences in tumor grade or area ( Fig 6F and 6G ) . The finding that Oct1 loss resulted in opposing effects in the chemical- vs . Apc/LOH-driven colon cancer models suggested that the two models may differ at a molecular level , such that Oct1’s dominant activity can switch from pro-oncogenic to tumor suppressive . To test this hypothesis , we sampled gene expression from control Oct1 wild-type FFPE and frozen tumor samples . We used a custom 60-gene panel enriched in gut-associated stem cell function [50] together with 31 AOM-DSS-induced tumor samples ( 23 FFPE and 8 frozen ) , and 25 Apcfl;Lrig1-CreERT2 tumor samples ( 12 FFPE and 13 frozen ) , all of which were wild-type for Oct1 . The output data is shown in S1 Table . Unsupervised hierarchical clustering of gene expression resulted in interdigitation of the samples ( Fig 7A , top ) . The interdigitation was robust using multiple settings and cutoffs , indicating that tumor-to-tumor gene expression variation is too great to cluster by model using all the genes in the probeset . To identify model-associated molecular signatures , we performed supervised clustering , grouping samples by the model-of-origin and applying an FDR cutoff of 0 . 05 . This analysis identified a subset of the profiled genes whose expression partitions with the model . A group of 17 genes was identified whose expression tended to correlate with tumor model ( Fig 7A , bottom ) . Examples are shown in Fig 7B . The differentially expressed gene list is shown in S1 Table , with and without an additional fold-change cutoff applied reducing the cohort to 10 genes . To further test the robustness of this result , we analyzed a 770 gene “pan-cancer” panel together with 31 AOM-DSS-induced ( 23 FFPE and 8 frozen ) and 25 Apcfl;Lrig1-CreERT2 ( 12 FFPE and 13 frozen ) Oct1 wild-type samples . The total output data is shown in S1 Table . Supervised clustering and applying the same FDR and fold-change cutoffs identified a group of 111 differentially expressed genes . These are shown in S1 Table . We previously identified Oct1 target genes in T cells and embryonic stem cells [6 , 51] . To identify targets in the context of colon cancer , we used Oct1 ChIPseq together with human HCT116 cells . HCT116 cells have the advantage of extensive profiling by the ENCODE ( ENCyclopedia Of DNA Elements ) consortium , including for attributes such as gene expression and DNaseI hypersensitivity [52] . We performed Oct1 and H3K4me3 ChIPseq using HCT116 cells , and input controls , identifying 1484 high-quality Oct1 peaks ( see methods ) . Comparing these peaks to DNaseI-hypersensitivity data from the ENCODE consortium showed greater than 26% ( 398 ) of the 1484 Oct1 peaks intersect with DNaseI-hypersensitive peaks with no gap ( p<0 . 001 , S2 Table ) . Profiling likely target genes near these peaks ( <20 kb ) identified 847 genes ( S3 Table ) . Examples are shown in Fig 8 . The gene associated with the strongest peak in this analysis ( S3 Table ) is Zbtb4 ( Fig 8A ) . The product of this gene is associated with the mitotic checkpoint [53] . Zbtb4 deletion in mice results in aneuploidy [53] . Other shown putative target genes are Gsk3a , Kmt2b , Prdx5 , Met , Rras , Mdh1 and Tet2 ( Fig 8B–8H ) . Analyzing pathways enriched in the gene list identified 127 terms with p<0 . 05 ( S4 Table ) . The top five pathways in this analysis were all heavily influenced by the presence of the multiple histone clusters as Oct1 targets ( S3 Table ) , e . g . , “nucleosome” was the top enriched pathway with 19 genes . Other identified pathways include “cell division” ( #11 , 10 genes ) , “spindle” ( #17 , 6 genes ) , “mitosis” ( #18 , 7 genes ) , “regulation of stem cell differentiation” ( #26 , 2 genes ) , “beta catenin binding” ( #31 , 3 genes ) and “regulation of Notch signaling pathway” ( #60 , 2 genes ) . These pathways are consistent with roles for Oct1 in transcription stem cells , malignancy and mitotic regulation . We compared the Oct1 targets identified in human HCT116 cells with the set mouse genes whose expression clusters with tumor type . Of the ten genes identified in the custom probeset whose expression correlates with the mouse tumor model ( FDR<0 . 05 , fold change>1 . 3 ) , two ( Lin28a and Lrig1 ) are within 20 kb of an Oct1 ChIPseq peak in HCT116 cells ( S5 Table ) . Similarly , of the 112 identified genes in the “pan-cancer” probeset whose expression correlates with the mouse tumor model , 29 correspond directly to human genes within 20 kb of an Oct1 peak ( e . g . , Stat3 , Bax , Tgfbr2 , Tet2 , Vegfa , Jag1 , S5 Table ) . The number of differentially expressed genes that are in the ChIPseq dataset is greater than what would be expected by chance ( Fig 8I ) . Combining the two datasets ( 31 genes ) also shows significant over-representation in the intersection between differentially expressed genes and putative Oct1 targets ( Fig 8I ) . Collectively , these results indicate that genes that tend to be differentially expressed between tumors from the two models are enriched for nearby Oct1 target sites .
Here we show that the transcription factor Oct1 is dispensable for mouse gut epithelial cell homeostasis , but is essential for recovery of the colon following damage in vivo , and for passage of intestinal organoids in vitro . Colonic epithelium lacking Oct1 maintains normal architecture for up to 150 days , consistent with an interpretation that Oct1 is dispensable for maintenance of the normal gut . In contrast to homeostatic conditions , Oct1 is required to recover the colon epithelium following DSS exposure , a situation that requires recognition of tissue damage followed by a high level of proliferation . Either a defect in proliferation and regeneration of the colon epithelium , an increase in cell death during this process , or both could underpin these findings . The findings are consistent with prior work from others indicating that homeostasis and regeneration are molecularly and physiologically distinct [54–58] , and with work showing that Oct1 becomes phosphorylated following stress exposure and associates with target genes containing a variant DNA binding site known as a MORE [5] . Example genes include Ahcy , Blcap , Zmiz2 , Rras , Rras2 , Bmp4 and Abcb1 [5 , 9] . Other work in DLD-1 colon cancer cells shows that Oct1 rapidly changes transcriptional cofactors in response to MAPK signals at the target gene Cdx2 [7] . Intestinal organoids have been used before to study regeneration [59] . Consistent with the finding that Oct1 loss did not affect small intestinal homeostasis in vivo , morphologically normal organoids could be directly explanted from tamoxifen treated mice . Interestingly , unlike control organoids we found that intact organoids lacking Oct1 could not be maintained by passage in vitro . Instead , the isolated crypts structures could close but did not generate new villus and crypt domains . The underlying causes may be similar to those underlying the phenotype of DSS-treated mice in vivo , though more study is required to test this idea . We also show that Oct1 loss has potent effects on tumorigenicity in two different mouse models of colon malignancy . Using an AOM-DSS model , Oct1 loss strongly protects mice from tumors . Elevated Oct1 mRNA expression is a negative prognostic factor in colon but not breast cancer ( Fig 9A ) . The normal appearance of the colon following Oct1 deletion , coupled with the protection afforded by Oct1 loss , suggests a “therapeutic window” in which targeting Oct1-associated pathways could be used to treat certain GI malignancies with minimal side effects . More study will be required to determine the conditions in which targeting Oct1 and its associated pathways in gut malignancy could be beneficial . In a second model driven by Apc LOH ( Apcfl/+;Lrig1-Cre ) , Oct1’s dominant activity is tumor suppressive , with more tumors of equal grade generated . This finding , together with the equivalent amount of pH3 staining in the tumor sections , suggests an increase in tumor initiation though not of progression . The molecular mechanisms underlying the differences in the two tumor models may involve changes in gene expression and/or a role for Oct1 in mitosis ( below ) . Of note , similar opposing roles were recently described for the Drosophila Oct1 ortholog , Nubbin , based on its different isoforms [60] . Different mammalian Oct1 isoforms have been described [61 , 62] , and thus Oct1 may regulate normal and malignant epithelial tissue states through evolutionarily conserved mechanisms . In HeLa cells and MEFs , Oct1 loss slightly increases the rate of mitotic chromosome segregation abnormalities , resulting in increased lagging chromosomes and aneuploidy [4] . Consistent with these prior findings , Oct1 loss accelerated tumorigenesis and increased tumor number in a colon cancer model driven by LOH of the tumor suppressor gene Apc in Lrig1+ cells [35] . In addition to increased LOH , differences in the molecular pathways and vulnerabilities associated with Oct1 in the two tumor models could contribute to the difference . To test this idea , we profiled gene expression in AOM-DSS tumors and tumors in which Apc is deleted in Lrig1+ cells , identifying a set of differentially expressed genes that include Lef1 , Ldha , Lrig1 , Tet2 , Atm and Bax . We performed ChIPseq using HCT116 colon carcinoma cells , identifying ~850 putative target genes associated with metabolism ( e . g . , Prdx5 , Mdh1 , Ahcy ) , transcription ( Taf12 , Tet2 , histones ) , malignancy ( Met , Blcap , Rras , Jag1 , Gsk3a ) and mitotic stability ( Zbtb4 ) . The set of targets includes some genes ( Ahcy , histones , Blcap , Rras ) identified previously in other tissues [5 , 51] , as well as others ( Prdx5 , Tet2 , Mdh1 , Jag1 , Gsk3a ) unique to this analysis . Intersection of the Oct1 target gene set with the set of genes differentially expressed in the two tumors types reveals a significant enrichment . Examples include Lin28a , Lrig1 , Tet2 , Vegfa and Jag1 . Either mitotic regulation or differential regulation of gene expression by Oct1 ( or both ) could therefore explain the opposing effects of Oct1 loss in the two models . A model for the functions of Oct1 in the two tumor models is shown in Fig 9B . In this model , Oct1 promotes AOM/DSS-induced tumors through actions on target genes controlling metabolism and stem cell identity . Because Oct1 promotes mitotic stability and because Oct1 target genes are differentially expressed in the more aggressive Apc-LOH model , Oct1 instead acts as a tumor suppressor . Cumulatively , the findings indicate that Oct1 is a potent regulator of colon malignancy , but that its functions are dictated by the colon tumor model used .
All in vivo experiments were reviewed , approved , and conducted in compliance with the University of Utah’s Institutional Animal Care and Use Committee and the NIH Guide for Care and Use of Laboratory Animals guidelines . Animal protocol: 17–05008 . Animal anesthesia and euthanasia involved the application of isofluorane . All mice used in this study were mixed C57BL/6:129/Sv background . The Oct1 conditional allele has been described previously [6] . The Lrig1-CreERT2 mouse allele [35] was a gift of Robert Coffey ( Vanderbilt ) . The Lgr5-EGFP-IRES-CreERT2 allele [36] was purchased from Jackson labs . Apcloxp exon14 ( Apcfl ) has been previously described [48] and was a gift from Ömer Yilmaz ( MIT ) . Food and water were available ad libitum . Tamoxifen ( 200 μL 10 mg/mL in corn oil ) was administered by oral gavage . All mice were treated at 6–8 weeks of age . Regeneration and azoxymethane ( AOM ) chemical tumorigenesis experiments used a single tamoxifen treatment . The Apc tumor model received three treatments on consecutive days . Dextran sodium sulfate ( DSS , MP Biomedicals ) was provided in drinking water at a concentration of 2 . 0% for AOM-induced tumors , and 2 . 5% for regeneration . AOM ( Sigma ) was provided by IP injection ( 10mg/kg ) . AOM-DSS treatments followed the protocol in [43] . Antibodies used for immunoblots were as follows: Oct1 , Bethyl #A310-610 ( 1:1000 ) ; β-actin , Santa Cruz #sc-47778 ( 1:1000 ) . Colon crypts were isolated as previously described [36 , 50 , 63] , with modifications . Briefly , small colon fragments were incubated in PBS with 4mM EDTA for 30 minutes at 4°C , followed by vigorous shaking . Colon crypts were collected after passing through a 70 μm cell strainer and incubated in lysis buffer for 30 minutes on a rotator at 4°C . After centrifugation at 400 × g for 10 minutes to remove cell debris , the lysates were prepared by boiling in 4X sample buffer . H&E stained sections were reviewed by a gastrointestinal pathologist and assessed for features of dysplasia including cytologic atypia , architectural complexity , and invasive tumors . Scoring criteria were as follows , with no tumors scoring higher than grade 2: grade 1: dysplastic epithelium with simple glandular architecture , with intervening lamina propria between the glands; grade 2: dysplastic epithelium with complex , crowded glandular architecture with cribriform glands , and lack of intervening lamina propria . IHC was performed as in [21] . The slides were developed with DAB peroxidase substrate ( Vector Laboratories , SK-4100 ) as per manufacturer instructions , and were counterstained with hematoxylin . After dehydration ( 3 min washes each of 70% , 85% , 95% and 100% ethanol ) the slides were incubated in xylene for 3 min twice and mounted using Limonene mounting medium ( Abcam #104141 ) . Antibodies used for IHC were as follows: Oct1 , Abcam #178869 ( 1:250 ) , total β-catenin , Santa Cruz #8814 ( 1:300 ) , phospho-histone H3 , Abcam #5176 ( 1:200 ) . Paraffin-embedded tissue slides were rehydrated by incubation in xylene for 4 minutes , followed by xylene , 100% , 95% , 85% and 70% ethanol for 3 minutes each . Antigen retrieval was performed in 10mM sodium citrate buffer , pH 6 . 0 in a steamer . Slides were blocked in PBS + 3% BSA for 1h at RT , followed by primary antibody incubation at 4°C overnight . Antibodies used for IF staining were as follows: Lrig1 , Thermo Scientific #PA547009 ( 10 μg/ml ) and GFP , Abcam #290 ( 1:500 ) . Images were collected and analyzed using an Axio Observer Z1 imaging system ( Carl Zeiss ) . To determine gut barrier defects following DSS-induced damage of the colon , spleens were harvested at end-point and placed into 15 mL conical tubes containing 5 mL of PBS on ice . Spleens were homogenized for 5 seconds using a tissue homogenizer ( Tekmar ) . 10 μL of undiluted homogenate was plated on 5% sheep blood agar plates ( Thermo Scientific #R01200 ) , in parallel with 10 μL of serial 1:10 dilutions to generate a colony counts over multi-log range . Plates were incubated at 37°C overnight . CFUs were counted and the total number of microbes calculated using the dilution factor . Organoids were maintained as previously described [50 , 63] with modifications . Crypts from tamoxifen-treated or untreated mice were plated in 8-well chambered slides in 40μl of Matrigel at a density of ~40 crypts per Matrigel droplet . Organoids were grown for 5–7 days until fully grown . Mature organoids were passaged every 5–7 days . Images were taken using an Olympus IX-50 inverted microscope . Quantifications were performed using Image J software ( National Institutes of Health ) . Gene expression was measured in Oct1 wild-type formalin-fixed , paraffin-embedded ( FFPE ) and snap-frozen tumors using NanoString ( Seattle , WA ) . For frozen samples , tumors were dissected from the normal mucosa under an Olympus SZ61 dissecting microscope in ice cold PBS and snap frozen in liquid nitrogen . Two gene panels were used: the 770-gene “pan-cancer” panel ( 23 FFPE AOM-DSS tumors and 12 FFPE Apcfl/+;Lrig1-CreERT2 tumors ) , and a custom 60 gene panel enriched for gut stem cell-associated genes [50] ( 23 FFPE and 8 frozen AOM-DSS tumors , and 12 FFPE and 13 frozen Apcfl/+;Lrig1-CreERT2 tumors ) . Laser capture microdissection was performed by the Molecular Pathology Core at the University of Utah . Total mRNA was purified with using a Qiagen miRNeasy FFPE Kit ( 217504 ) . Analysis and normalization of the raw data were conducted with nSolver Analysis Software v4 . 0 ( NanoString Technologies ) . Genes whose expression significantly correlated with tumor type were identified using Prism GraphPad Row Statistics . Multiple T-tests were performed on each gene individually , without assuming a consistent standard deviation . Cutoffs were determined using the Two-stage linear step-up procedure of Benjamini , Krieger and Yekutieli [64] , with Q = 5% , and a linear fold change less than 0 . 769 or greater than 1 . 3 fold . For each differentially expressed gene set , ascription of statistical significance for Oct1/Oct4 target gene enrichment was made using a hypergeometric test . Human HCT116 cells ( ATCC ) were cultured in DMEM supplemented with 5% calf serum ( Sigma ) , 5% fetal calf serum ( X&Y Cell Culture ) , 100 U/ml penicillin , 100 μg/mL streptomycin and 2 mM L-glutamine ( Invitrogen ) . Cells were screened monthly for mycoplasma by PCR . Cells were cultured at 37°C in a humidified atmosphere containing 5% CO2 . Cells were prepared for ChIPseq as previously described [65] with modifications . Briefly , protein-DNA complexes were cross-linked with 1% formaldehyde for 8 minutes , followed by quenching with 0 . 125M glycine for 5 minutes . Sonication was performed using the EpiShear Cooled Sonication Platform ( ActiveMotif #53080 ) with 10 pulses consisting of a 20-second sonication followed by a 30-second rest at 25% amplitude on tube coolers to yield a range of products between 200–500 bp . Sonicated chromatin was collected after centrifugation at 16 , 000 × g for 5 min at 4°C . Immunoprecipitation used Oct1 ( Bethyl #A310-610A ) or H3K4me3 ( Cell Signaling #9727 ) antibodies . Cross-links were reversed overnight at 65°C , followed by purification of the enriched DNA using a Qiagen Qiaquick PCR cleanup kit ( Cat # 28104 ) . For both Oct1 and H3K4me3 ChIPseq , three biological replicates of sub-confluent HCT116 cells were performed , with three input controls . Library preparation was performed using an NEBNext ChIP-Seq Library Prep Master Mix Set with Unique Dual Index Primers . Paired-end sequencing took place on an Illumina NovaSeq platform . For sequence analysis , paired end fastq datasets for each sample were aligned to the B37 human reference using bowtie2 [66] , sorted , and duplicates removed with Picard tools ( https://broadinstitute . github . io/picard/ ) . Uniquely mapped , duplicate free , paired end alignments were merged and their center positions extracted using USeq [67] . Both replica specific and merged replica ChIPSeq peak identification was performed following the USeq ChIPseq protocol ( http://bioserver . hci . utah . edu/USeq/Documentation/usage . html ) with methodology detailed in [67] . ChIPseq peaks of moderate and high stringency were identified by setting both an FDR and log2 Ratio threshold; FDR of > = 0 . 05 and log2 ratio > = 0 . 585 for moderate , FDR of > = 0 . 01 and log2 ratio > = 1 for high . Peaks intersecting satellite repeat regions were excluded . This analysis resulted in between 55 and 105 million paired-end sequence reads ( 39 and 55 million merged reads ) , 77 to 86% of which uniquely aligned to the human B37 reference genome . Merging the datasets from the three replicates identified 1484 high-quality Oct1 peaks ( FDR> . 01 , log2 fold enrichment>1 ) . Pathway analysis was performed using the BaseSpace Correlation Engine ( Illumina ) . Raw and processed data have been made available in the Gene Expression Omnibus ( GEO ) public repository ( GEO series record GSE123513 ) . Student’s T-tests were used throughout to determine p-values . Error bars denote ±standard deviation except where noted . A single * indicates p<0 . 05 , ** indicates p<0 . 01 , *** indicates p<0 . 001 . ChIPseq p- and q-values were determined using a binomial distribution and multiple test correction controlled using Storey q-value/FDR [68] . For survival analysis , p-values were calculated using a logrank test . | Colorectal cancer is the second leading cause of cancer death in the United States . Approximately 35% of diagnosed patients eventually succumb to disease . The high incidence and mortality due to colon cancer demand a better understanding of factors controlling the physiology and pathophysiology of the gastrointestinal tract . Previously , we and others showed that the widely expressed transcription factor Oct1 is expressed at higher protein levels in stem cells , including intestinal stem cells . Here we use deletion of a conditional mouse Oct1 ( Pou2f1 ) allele in two different intestinal stem cell compartments to study gut homeostasis . We then proceed to investigate the effect of Oct1 loss in colon regeneration and malignancy . The results indicate that Oct1 loss is dispensable for maintenance of the mouse gut , but required for recovery after damage to the colon epithelium . We also find that Oct1 loss has opposing effects in two different mouse colon cancer models , and further that the two models are associated with different gene expression signatures . The differentially expressed genes are enriched for Oct1 targets , suggesting that differential gene control by Oct1 is one mechanism underlying the different outcomes . | [
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] | 2019 | Oct1/Pou2f1 is selectively required for colon regeneration and regulates colon malignancy |
The study objective is to estimate the epidemiological and economic impact of vaccine interventions during influenza pandemics in Chicago , and assist in vaccine intervention priorities . Scenarios of delay in vaccine introduction with limited vaccine efficacy and limited supplies are not unlikely in future influenza pandemics , as in the 2009 H1N1 influenza pandemic . We simulated influenza pandemics in Chicago using agent-based transmission dynamic modeling . Population was distributed among high-risk and non-high risk among 0–19 , 20–64 and 65+ years subpopulations . Different attack rate scenarios for catastrophic ( 30 . 15% ) , strong ( 21 . 96% ) , and moderate ( 11 . 73% ) influenza pandemics were compared against vaccine intervention scenarios , at 40% coverage , 40% efficacy , and unit cost of $28 . 62 . Sensitivity analysis for vaccine compliance , vaccine efficacy and vaccine start date was also conducted . Vaccine prioritization criteria include risk of death , total deaths , net benefits , and return on investment . The risk of death is the highest among the high-risk 65+ years subpopulation in the catastrophic influenza pandemic , and highest among the high-risk 0–19 years subpopulation in the strong and moderate influenza pandemics . The proportion of total deaths and net benefits are the highest among the high-risk 20–64 years subpopulation in the catastrophic , strong and moderate influenza pandemics . The return on investment is the highest in the high-risk 0–19 years subpopulation in the catastrophic , strong and moderate influenza pandemics . Based on risk of death and return on investment , high-risk groups of the three age group subpopulations can be prioritized for vaccination , and the vaccine interventions are cost saving for all age and risk groups . The attack rates among the children are higher than among the adults and seniors in the catastrophic , strong , and moderate influenza pandemic scenarios , due to their larger social contact network and homophilous interactions in school . Based on return on investment and higher attack rates among children , we recommend prioritizing children ( 0–19 years ) and seniors ( 65+ years ) after high-risk groups for influenza vaccination during times of limited vaccine supplies . Based on risk of death , we recommend prioritizing seniors ( 65+ years ) after high-risk groups for influenza vaccination during times of limited vaccine supplies .
Evidence on the epidemiological and economic impact of vaccination for all age and risk groups from the societal standpoint assists in prioritization of influenza vaccine intervention , especially when vaccine supplies are limited , and minimize the direct cost of clinical care for influenza related health outcomes and indirect cost of productivity loss due to workplace absenteeism . While some studies have analyzed the direct epidemiological and economic impact of vaccine intervention strategies on controlling influenza pandemics [4–13] , other studies have analyzed both the direct and indirect epidemiological and economic impact of influenza vaccination [14–16] . There are also prior studies that focused on the prioritization of vaccination and other interventions among people in different age groups [17–19] . This study adds to the evidence of prior studies by using a detailed agent-based model for estimating the direct and indirect effects of epidemiological and economic impact of vaccine-based interventions . The objective of the vaccine interventions is to minimize deaths , hospitalizations , outpatient visits , and the number of ill people who do not seek medical care . Direct effect is due to the immune protection gained by effectively vaccinated individuals , and indirect effect is due to blocking of the influenza transmission by vaccinated individuals to susceptible individuals in their social network . Cost effectiveness of influenza vaccination for 65+ years [5] , healthy working adults [6 , 7] , and children [9 , 20] with a focus on direct effects have been studied . Prosser et al . evaluate the economic impact of 2009 pandemic influenza vaccine intervention for all age and risk groups [8] . They infer that vaccination of the subpopulation with a high risk of developing influenza related complications in each age group is cost saving , and vaccination of the healthy subpopulation in each age group is cost effective . Other studies have inferred that vaccine administration during previous and potential pandemics produces health benefits in terms of number of averted influenza cases and related health outcomes [10–12] . These studies included the direct cost of hospitalizations , outpatient visits , and deaths , and included the related costs of vaccine production and administration , and lost productivity . Depending on the risk and age group of the subpopulations , geographic region , and analytic methodology , the vaccine interventions may or may not be cost effective [21 , 22] . Indirect effects account for the indirect protection due to vaccine intervention . Effectively vaccinated individuals who develop protective immune response to the prevalent influenza strains , cut off transmission pathways to secondary and subsequent individuals . The indirect effect of vaccinating school children has been found to be significant , due to their high connectivity in the social network and significance of their transmission pathways to their households and community [23–27] . While Medlock et al . recommend influenza vaccine prioritization of school children and adults aged 30 to 39 years [15] , Lee et al . recommend prioritization of vaccinating at-risk individuals first rather than children first by analyzing the 2009 H1N1 influenza pandemic , matching the 2009 ACIP recommendations [14] . The epidemiological benefits and economic costs estimated by taking into account only the direct effect is relatively conservative , in comparison to taking into account both the direct and indirect effects . We improve the fidelity and robustness of the cost-benefit estimates to facilitate optimal prioritization of our vaccine interventions among different age and risk groups . Fig 1 illustrates the evaluation of the epidemiological and economic impact of influenza vaccine intervention using the static model ( direct effects only ) and dynamic model ( direct + indirect effects ) . Meltzer et al . estimate the potential net value of different vaccination strategies , and identify vaccination priorities for different age and risk groups during an influenza pandemic [4] . A Monte Carlo based static model is used to estimate the costs and benefits due to the direct effect of vaccine interventions in the United States . We focus our study on similar influenza related health outcomes , risk levels and age groups as Meltzer’s study . We use an agent-based dynamic model to estimate the direct and indirect epidemiological and economic impact of vaccine interventions during an influenza pandemic in Chicago , and assist in the assessment of vaccine intervention priorities . Population dynamics play an important role in influenza pandemic planning and response . Influenza vaccination not only protects effectively vaccinated individuals who develop a protective immune response from contracting influenza , but also prevents the spread of influenza in the social contact network of people by breaking the transmission chain . To optimally allocate limited resources , it is important to inform decision makers and public health officials about both the direct and indirect effects of influenza vaccine interventions .
The Institutional Review Board at Virginia Tech has given ethics approval ( IRB exempt ) for the research conducted in this study . The Chicago metropolitan area is a major urban area in the United States , and had high influenza incidence during the 2009 H1N1 influenza pandemic [28] . We analyzed the impact of vaccine-based interventions on pandemic influenza in Chicago , using the population distribution of 9 , 047 , 574 people from the census data [29] . The disease diffusion occurs on a collocation based synthetic social contact network for Chicago , based on dynamic agent-based modeling [30–32] . We generated the synthetic population and estimated the social contact network in Chicago through population synthesis , activity assignment , location choice and contact estimation , as illustrated in Table 1 [30 , 33] . The social contact network simulated the movement of individuals throughout the city and estimated the contact times between individuals based on their simultaneous presence at a location . The transmission dynamics of an influenza-like-illness in the population is simulated using the susceptible-exposed-infectious-recovered ( SEIR ) epidemiological model on this synthetic social contact network of Chicago . Each person in the model is in one of the following four health states at any time: susceptible , exposed , infectious , and removed . A person is in the susceptible state until he becomes exposed . If a person becomes exposed , he remains exposed for the duration of the latent period , during which he is not infectious . At the end of the latent period , an exposed person becomes infectious and remains infectious for the duration of the infectious period . A person in the infectious state will probabilistically transmit the disease , based on the transmission rate , to any of his contacts who are in the susceptible state . A proportion of infectious individuals are asymptomatic , and there is a reduction in probability of transmission by an asymptomatic infectious person in comparison to a symptomatic infectious person to a susceptible individual . After the infectious period , the infectious person becomes recovered ( or removed ) . Transmissibility is the probability of transmission per minute of contact with a symptomatic infectious person and is set to 0 . 00008 , 0 . 00009 , and 0 . 0001 to calibrate the simulation for the moderate , strong and catastrophic influenza pandemic scenarios respectively , with attack rates of 11 . 73% , 21 . 96% and 30 . 15% respectively . The simulation parameters for the social contact network and influenza dynamics are illustrated in Table 2 . We estimate the direct and indirect effects of vaccine interventions on influenza pandemics of moderate , strong and catastrophic severities , in comparison to the base case scenario of no vaccine intervention . Influenza related health outcomes for the infected individuals are death , hospitalization , outpatient visits , and ill but not seeking medical care . The risk levels are high and non-high , and the age groups are 0–19 years , 20–64 years and 65+ years . Based on pre-existing medical conditions , influenza infected individuals may be at a high or non-high risk of experiencing influenza related health outcomes . The distribution of the four influenza related health outcomes among the high and non-high risk cases in the three different age groups is based on the study by Meltzer et al . [4] . For the base-case scenario of no vaccine intervention , three different severities of an influenza pandemic were simulated using the dynamic model: moderate influenza with 11 . 73% attack rate , strong influenza with 21 . 96% attack rate , and catastrophic influenza with 30 . 15% attack rate . We use the dynamic model to simulate the epidemic curves for these 3 attack rates for the base-case scenario of no vaccine intervention , based on the average incidence from 25 replicates ( see Fig 2 and Table 3 ) . The simulation timeline of the influenza pandemics are in accordance with prior experiences of influenza pandemics in the United States [34] . S1 Appendix describes the risk space of transmissibility and clinical severity for the pandemic scenarios , as defined by the framework for assessing epidemiologic effects of influenza epidemics and pandemics by Reed et al [35 , 36] . Effectiveness of influenza vaccines varies between 10% to 60% [37] . We analyzed the impact of the vaccine intervention scenario of 40% efficacy and 40% compliance for all age and risk groups , following Meltzer et al . [4] . It took months to develop and distribute the 2009 H1N1 influenza vaccine , and similar scenarios of delay in vaccine introduction , limited vaccine efficacy and limited supplies are not unlikely in future influenza pandemics . Thereby , we analyze delays in the implementation of the vaccine intervention with limited efficacy and compliance rates . Static model is used to estimate the direct benefit of influenza vaccination , that is , vaccination only protects effectively vaccinated individuals who develop protective immune response , but does not account for preventing influenza transmission from effectively vaccinated individuals to their social contact network . Using the simulation results of the base-case scenario of no vaccine intervention from the dynamic model , the influenza attack rates of moderate , strong and catastrophic pandemic scenarios are decreased by the proportional impact of the vaccine intervention at 40% coverage and 40% efficacy . Thereby , the influenza attack rates in the 3 age group sub-populations are decreased by 16% ( 40% efficacy * 40% compliance ) in each of the three pandemic scenarios ( see Table 4 ) . We simulated the vaccine intervention scenarios at 40% efficacy and 40% compliance for all age and risk groups in the dynamic agent-based model . The vaccine intervention is initiated 15 days after the start of the pandemic and is carried out for 60 days . The dynamic model simulates the diffusion of influenza on the population in Chicago . It takes into account the indirect effect of limiting disease diffusion by vaccinated individuals , who develop protective immune response and cut off transmission pathways to secondary and subsequent individuals . The influenza attack rates for the 3 age groups in moderate , strong and catastrophic pandemic scenarios are estimated ( see Table 4 ) . Fig 2 includes the epidemic curves ( based on 25 replicates of each scenario ) for the three pandemic scenarios with the vaccine intervention . The cost of influenza vaccine is estimated to be $28 . 62 , and includes the clinical personnel , non-clinical personnel , and all overhead costs [38] . Direct medical costs and indirect productivity losses were estimated from a prior study , and are presented in Table 5 [39–42] . Based on Meltzer’s study [4] , we developed a decision tree that includes the probability distribution of an influenza case experiencing the influenza related health outcomes of death , hospitalization , outpatient visits , and ill but not seeking medical care , and the cost associated with these health outcomes among the different age and risk groups ( see Fig 3 ) . All costs have been adjusted to 2015 US$ ( see Table 5 ) . We used this decision tree to estimate the cost due to influenza related health outcomes among the different age and risk groups . This cost estimation process is conducted in all the three scenarios: base case scenario of no intervention using dynamic model , vaccine intervention scenario using static model , and vaccine intervention scenario using dynamic model . Within each of these scenarios , for each pandemic severity ( moderate , strong and catastrophic ) , we compute the pandemic cost , pandemic cost per capita , net benefits , and return on investment , as illustrated in Table 6 ( also , see Tables 3 and 4 ) . The pandemic cost is the total cost associated with the health outcomes of influenza cases and the cost of vaccination , and pandemic cost per capita is the average pandemic cost per person . The net benefits are the difference in cost due to improved health outcomes from vaccination and the vaccination cost . Return on investment is the gain in net benefits relative to the vaccination cost . We conducted economic evaluation from the medical and productivity perspective , and includes the direct cost of clinical care for influenza related health outcomes incurred by the health care provider and indirect cost of productivity loss incurred by the patient . To extend this analysis to a societal perspective , costs incurred by the federal government in vaccine distribution , vaccine coverage monitoring , vaccine effectiveness monitoring , vaccine safety monitoring , health communication , and national coordination and technical assistance [43] , productivity loss of volunteers in the influenza vaccine campaign , and costs of global influenza surveillance for vaccine strain selection will need to be included , which are beyond the scope of this study . The values of the simulation parameters and their sources are shown in Table 2 . Each influenza pandemic scenario in the agent-based model is simulated 25 times . The costs of influenza-related health outcomes among the different age and risk groups are estimated using the decision tree ( Fig 3 ) . The agent-based model is executed through SIBEL [44] , a web-based tool to conduct epidemiological disease studies based on realistic social network simulation , and the influenza-related health outcome estimation using decision tree and cost-benefit analysis is executed through the R software for statistical computing and graphics [45] . We conducted univariate sensitivity analysis for vaccine compliance , vaccine efficacy and vaccine start date , and their impact on attack rates and return on investment for catastrophic , strong , and moderate influenza pandemic scenarios with no vaccine intervention ( base case ) , and with vaccine intervention in static model ( direct effect ) and dynamic model ( direct + indirect effects ) .
The pandemic cost per capita is $678 . 10 , $486 . 67 and $255 . 18 for catastrophic , strong , and moderate influenza scenarios respectively ( see Table 3 ) . The attack rate is 30 . 15% , 21 . 96% and 11 . 73% for catastrophic , strong , and moderate influenza scenarios respectively . The reproduction number is 1 . 19 , 1 . 13 and 1 . 06 for catastrophic , strong , and moderate influenza scenarios respectively . The pandemic cost per capita is positively correlated with attack rate and reproduction number , with the highest in catastrophic influenza scenario followed by the strong and moderate influenza scenarios . The vaccine intervention is simulated at 40% compliance and 40% efficacy , using the static model and the dynamic model . The vaccine intervention decreases the pandemic cost per capita , attack rate and reproduction number in the catastrophic , strong and moderate influenza pandemic scenarios in both the static and dynamic models . Fig 4A , 4B and 4C illustrate the comparison of pandemic cost per capita , attack rate and reproduction number in the catastrophic , strong and moderate influenza pandemic scenarios with and without vaccine intervention . In the catastrophic influenza pandemic scenario with vaccine intervention , the pandemic cost per capita , attack rate and reproduction number are $370 . 56 , 16 . 34% and 1 . 09 respectively in the dynamic model , while they are $581 . 09 , 25 . 33% and 1 . 15 respectively in the static model ( see Table 4 ) . In the strong influenza pandemic scenario with vaccine intervention , the pandemic cost per capita , attack rate and reproduction number are $90 . 81 , 3 . 90% and 1 . 02 respectively in the dynamic model , while they are $420 . 28 , 18 . 45% and 1 . 11 respectively in the static model . In the moderate influenza pandemic scenario with vaccine intervention , the pandemic cost per capita , attack rate and reproduction number are $14 . 85 , 0 . 16% and 1 . 00 respectively in the dynamic model , while they are $225 . 83 , 9 . 85% and 1 . 05 respectively in the static model . Molinari et al estimated the annual economic impact ( medical costs and productivity loss ) of seasonal influenza in the United States to be $87 . 0673 billion ( 95% CI: $47 . 2153 , $149 . 5086 ) in 2003 with the vaccine intervention [40] , which relates to an inflation adjusted cost per capita of $392 . 24 ( 95% CI: $212 . 71 , $673 . 54 ) in $2015 . We estimated the pandemic cost per capita with no vaccine intervention to be $678 . 10 , $486 . 67 and $255 . 18 ( in $2015 ) for catastrophic , strong , and moderate influenza scenarios respectively , and with vaccine intervention to be $370 . 56 , $90 . 81 and $14 . 85 respectively . While the vaccine interventions are cost-beneficial in both the dynamic and static models , the return on investment is relatively higher in the dynamic model due to the combined impact of direct and indirect effects , in comparison to the static model which includes only the direct effect ( see Fig 5 and Table 7 ) . Vaccine prioritization criteria includes risk of death , total deaths , net benefits , and return on investment . Table 8 shows the values for risk of death , total deaths , net benefits , and return on investment of high and non-high risk groups among the 0–19 , 20–64 , 65+ years subpopulations for the catastrophic , strong and moderate influenza pandemic scenarios . The prioritization criteria of risk of death , total deaths , net benefits , and return on investment assist in the decision making process for vaccine prioritization among different age and risk groups , as shown in Table 9 . Fig 6A illustrates the prioritization criteria for the vaccine intervention based on the risk of death . In the catastrophic influenza pandemic scenario , the risk of death among the high-risk 65+ years subpopulation is the highest at 392 . 18 deaths per 100 , 000 influenza cases , while it is the lowest among the non-high risk 0–19 years subpopulation at 6 . 63 deaths per 100 , 000 influenza cases . In the strong influenza pandemic scenario , the risk of death among the high-risk 0–19 years subpopulation is the highest at 281 . 08 deaths per 100 , 000 influenza cases , while it is the lowest among the non-high risk 0–19 years subpopulation at 5 . 04 deaths per 100 , 000 influenza cases . In the moderate influenza pandemic scenario , the risk of death among the high-risk 0–19 years subpopulation is the highest at 157 . 85 deaths per 100 , 000 influenza cases , while it is the lowest among the non-high risk 20–64 years subpopulation at 2 . 65 deaths per 100 , 000 influenza cases . Fig 6B illustrates the prioritization criteria for the vaccine intervention based on the proportion of total deaths . In the catastrophic influenza pandemic scenario , the proportion of total deaths among the high-risk 20–64 years subpopulation is the highest at 0 . 45 , while it is the lowest among the non-high risk 0–19 years subpopulation at 0 . 031 . In the strong influenza pandemic scenario , the proportion of total deaths among the high-risk 20–64 years subpopulation is the highest at 0 . 45 while it is the lowest among the non-high risk 0–19 years subpopulation at 0 . 033 . In the moderate influenza pandemic scenario , the proportion of total deaths among the high risk 20–64 years subpopulation is the highest at 0 . 447 , while it is the lowest among the non-high risk 0–19 years subpopulation at 0 . 036 . Fig 6C illustrates the prioritization criteria for the vaccine intervention based on net benefits . In the catastrophic influenza pandemic scenario , the net benefits among the high-risk 20–64 years subpopulation is the highest at $1201 . 38 million , while it is the lowest among the non-high risk 65+ years subpopulation at $68 . 80 million . In the strong influenza pandemic scenario , the net benefits among the high risk 20–64 years subpopulation is the highest at $1462 . 52 million , while it is the lowest among the non-high risk 65+ years subpopulation at $79 . 71 million . In the moderate influenza pandemic scenario , the net benefits among the high risk 20–64 years subpopulation is the highest at $864 . 83 million , while it is the lowest among the non-high risk 65+ years subpopulation at $43 . 02 million . Fig 6D illustrates the prioritization criteria for the vaccine intervention based on return on investment . In the catastrophic influenza pandemic scenario , the return on investment among the high-risk 0–19 years subpopulation is the highest at 249 . 16 ( i . e . , $249 . 16 saved for every $1 invested in vaccine intervention ) , while it is the lowest among the non-high risk 20–64 years subpopulation at 8 . 20 ( i . e . , $8 . 20 saved for every $1 invested in vaccine intervention ) . In the strong influenza pandemic scenario , the return on investment among the high-risk 0–19 years subpopulation is the highest at 367 . 42 ( i . e . , $367 . 42 saved for every $1 invested in vaccine intervention ) , while it is the lowest among the non-high risk 20–64 years subpopulation at 10 . 18 ( i . e . , $10 . 18 saved for every $1 invested in vaccine intervention ) . In the moderate influenza pandemic scenario , the return on investment among the high-risk 0–19 years subpopulation is the highest at 248 . 69 ( i . e . , $248 . 69 saved for every $1 invested in vaccine intervention ) , while it is the lowest among the non-high risk 20–64 years subpopulation at 5 . 64 ( i . e . , $5 . 64 saved for every $1 invested in vaccine intervention ) . We conducted univariate sensitivity analysis for vaccine compliance , vaccine efficacy and vaccine start date , and their impact on attack rates and return on investment for catastrophic , strong , and moderate influenza pandemic scenarios .
Direct effect is due to the immune protection gained by effectively vaccinated individuals , and indirect effect is due to blocking of the influenza transmission by vaccinated individuals to susceptible individuals in their social network . The static model provides a conservative estimate of the epidemiological and economic benefits of influenza vaccine intervention by accounting for only the direct effect . The dynamic model provides a comprehensive estimate of the epidemiological and economic benefits of influenza vaccine intervention by accounting for both the direct and indirect effects . The vaccine intervention has a higher probability of effectively vaccinating individuals who will have otherwise being infected in the absence of the vaccine intervention in more severe pandemic scenarios ( such as catastrophic influenza ) . This is due to relatively higher attack rates and higher proportion of population at risk of influenza infection in comparison to less severe pandemic scenarios ( such as moderate influenza ) . Thereby , the impact of the direct effect decreases from catastrophic , strong to moderate influenza pandemic scenarios ( see Fig 5 ) . The vaccine intervention has a lower probability of breaking transmission pathways in more severe pandemic scenarios ( such as catastrophic influenza ) , because the transmission network is densely connected in comparison to sparsely connected transmission networks in less severe pandemic scenarios ( such as moderate influenza ) . Thereby , the impact of the indirect effect increases from catastrophic , strong to moderate influenza pandemic scenarios ( see Fig 5 ) . Pandemic cost per capita , attack rate and reproduction number are relatively lower in the dynamic model due to the combined impact of direct and indirect effects , in comparison to the static model which includes only the direct effect , in the catastrophic , strong and moderate influenza pandemic scenarios . While the vaccine interventions are cost-beneficial in both the dynamic and static models , the return on investment is relatively higher in the dynamic model in comparison to the static model . We analyzed vaccine prioritization criteria based on risk of death , total deaths , net benefits and return on investment for the high and non-high risk groups among 0–19 , 20–64 and 65+ years subpopulations . The risk of death is the highest among the high-risk 65+ years subpopulation in the catastrophic influenza , and it is the highest among the high-risk 0–19 years subpopulation in the strong and moderate influenza pandemic scenarios . The proportion of total deaths is the highest among the high-risk 20–64 years subpopulation in the catastrophic , strong and moderate influenza pandemic scenarios . The net benefits are the highest among the high-risk 20–64 years subpopulation in the catastrophic , strong and moderate influenza pandemic scenarios . The return on investment is the highest in the high-risk 0–19 years subpopulation in the catastrophic , strong and moderate influenza pandemic scenarios . The proportion of total deaths and net benefits measure the epidemiological and economic impact respectively , and are dependent on the absolute size of the different risk and age group subpopulations . Risk of death and return on investment measure the epidemiological and economic impact respectively , and are independent of the absolute size of the different risk and age group subpopulations . Based on risk of death and return on investment , high-risk groups of the three age group subpopulations are recommended for prioritization of influenza vaccine intervention . Also , the vaccine intervention is cost-beneficial for all age and risk groups . The attack rates among the children ( 0–19 years ) are higher than the attack rates among the adults ( 20–64 years ) and seniors ( 65+ years ) in the catastrophic , strong , and moderate influenza pandemic scenarios , as illustrated in Table 4 . This can be attributed to their larger social contact network and homophilous interactions in schools . Thereby , if we target children for vaccination , there will be higher reduction among the children as well on the overall attack rate in the general population , as also illustrated in prior studies by Hodgson et al [16] , Ferguson et al [17] , and Germann et al [18] . Also , as shown in Table 9 , high risk children have the highest return on investment from the vaccine intervention . The dynamic model provides improved estimates of the epidemiological and economic benefits of vaccine interventions in comparison to a static model , by accounting for both the direct and indirect effects . These comprehensive estimates assist in prioritization of vaccine interventions among subpopulations of different risk and age groups , especially during influenza pandemics with limited availability of vaccines . Decision makers can use the dynamic model simulations to compare the epidemiological and economic impact of using different prioritization criteria of influenza vaccine interventions among different risk and age group subpopulations , thereby optimizing allocation of limited resources and improving evidence-based public health policy and practice . Based on risk of death and return on investment , high-risk groups of the three age group subpopulations can be prioritized for vaccination , and the vaccine interventions are cost saving for all age and risk groups . The attack rates among the children are higher than among the adults and seniors in the catastrophic , strong , and moderate influenza pandemic scenarios , due to their larger social contact network and homophilous interactions in school . Based on return on investment and higher attack rates among children , we recommend prioritizing children ( 0–19 years ) and seniors ( 65+ years ) after high-risk groups for influenza vaccination during times of limited vaccine supplies . Based on risk of death , we recommend prioritizing seniors ( 65+ years ) after high-risk groups for influenza vaccination during times of limited vaccine supplies . We used an agent-based individual model in this study to estimate the direct and indirect epidemiological and economic impact of vaccine interventions during an influenza pandemic in Chicago , similar to related studies [14 , 46 , 47] . Alternatively , a population level compartmental model can also be used to conduct this study , similar to related studies [15 , 48 , 49] . While agent-based individual models add heterogeneity in contact patterns between individuals in comparison to homogeneous mixing in compartmental models , it will be valuable to compare the vaccine intervention priorities derived from these two modeling methods in future studies . We used a mean estimate of $28 . 62 ( inflation adjusted to 2015 US dollars ) for the cost of influenza vaccine , and did not include the range and uncertainty in vaccination costs by location and size of clinical practice . While beyond the scope of this study , this analysis can be extended to additional studies for a range of vaccine compliance and efficacy values at different attack rates of influenza pandemics in different rural and urban areas of the United States and at the country level , to infer objective prioritization criteria for influenza vaccine interventions among different risk and age groups . | The study objective is to estimate the epidemiological and economic impact of vaccine interventions during an influenza pandemic in Chicago , to assist in vaccine intervention priorities . Population dynamics play an important role in influenza pandemic planning and response . To optimally allocate limited vaccine resources , it is important to inform decision makers and public health officials about both the direct benefit among vaccinated population and the indirect benefit among non-vaccinated population . This study adds to the evidence of prior studies by using a detailed agent-based model for estimating the direct and indirect benefits of epidemiological and economic impact of vaccine-based interventions . This study can be extended to analyze for a range of vaccine compliance and efficacy values at different attack rates of influenza pandemics in different rural and urban areas of the United States and at the country level , to infer objective prioritization criteria for influenza vaccine interventions among different risk and age groups . | [
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] | 2017 | Epidemiological and economic impact of pandemic influenza in Chicago: Priorities for vaccine interventions |
Caspases regulate cell death programs in response to environmental stresses , including infection and inflammation , and are therefore critical for the proper operation of the mammalian immune system . Caspase-8 is necessary for optimal production of inflammatory cytokines and host defense against infection by multiple pathogens including Yersinia , but whether this is due to death of infected cells or an intrinsic role of caspase-8 in TLR-induced gene expression is unknown . Caspase-8 activation at death signaling complexes results in its autoprocessing and subsequent cleavage and activation of its downstream apoptotic targets . Whether caspase-8 activity is also important for inflammatory gene expression during bacterial infection has not been investigated . Here , we report that caspase-8 plays an essential cell-intrinsic role in innate inflammatory cytokine production in vivo during Yersinia infection . Unexpectedly , we found that caspase-8 enzymatic activity regulates gene expression in response to bacterial infection as well as TLR signaling independently of apoptosis . Using newly-generated mice in which caspase-8 autoprocessing is ablated ( Casp8DA/DA ) , we now demonstrate that caspase-8 enzymatic activity , but not autoprocessing , mediates induction of inflammatory cytokines by bacterial infection and a wide variety of TLR stimuli . Because unprocessed caspase-8 functions in an enzymatic complex with its homolog cFLIP , our findings implicate the caspase-8/cFLIP heterodimer in control of inflammatory cytokines during microbial infection , and provide new insight into regulation of antibacterial immune defense .
Pattern recognition receptors such as Toll-like receptors ( TLRs ) sense conserved microbial structures including lipopolysaccharide ( LPS ) or peptidoglycans [1] . Bacterial infection triggers MyD88- and TRIF-dependent MAPK and NF-κB signaling , which induces the expression of cell survival and inflammatory programs that are critical for host defense [2] . Activation of TLRs in the presence of pharmacological or bacterial inhibitors of NF-κB results in cell death that is mediated by the cysteine protease caspase-8 [3–5] . This is due to recruitment of caspase-8 to a TRIF/RIPK1/FADD-containing complex via specific homotypic protein-protein interaction motifs [6] . RIPK1 interacts with TRIF by means of RIP homology interaction motifs ( RHIM ) and can bind FADD through shared death domains ( DD ) , which in turn engages caspase-8 via death effector domains ( DED ) [7 , 8] . Upon recruitment to this complex , caspase-8 undergoes dimerization and autoprocessing , which stabilizes the active enzyme , and initiates the proteolytic cascade that ultimately results in apoptotic disassembly of the cell [9] . Spontaneous mutations in human caspase-8 that render it catalytically inactive are linked with primary immunodeficiency and recurrent sinopulmonary and mucocutaneous infections [10 , 11] . Similarly , individuals with mutations in the adaptor FADD suffer from recurrent infections and liver pathology , suggesting a role for caspase-8 and FADD in antimicrobial responses [12] . Initial studies observed that T , B and NK cells from patients with caspase-8 deficiency displayed defects in activation following stimulation through their cell-type specific functional receptors [10 , 13] . Interestingly , reconstitution of a caspase-8-deficient Jurkat T cell line implicated the enzymatic activity of uncleaved caspase-8 in activation of T cells via TCR [13] . However , subsequent work revealed that caspase-8 is critical to protect T cells from programmed necrosis in the setting of TCR stimulation , and that rescuing this survival defect restored the ability of T cells to respond to viral infection [14 , 15] . These studies suggested the possibility that the effect of caspase-8 on activation could relate to its control of cell death , rather than activation of transcriptional signaling machinery per se . The survival function of caspase-8 prevents receptor-interacting serine/threonine protein kinase-3 ( RIPK3 ) -dependent necroptosis , which occurs in the context of developmental and inflammatory cues [15–20] . During homeostasis , RIPK3 is repressed by heterodimers of caspase-8 and its catalytically inactive homologue , cFLIP . However , inhibition of caspase-8 activity or deletion of caspase-8 releases RIPK3-dependent necroptosis [15 , 18] . TLR signaling normally prevents caspase-8- , FADD- and RIPK3-dependent cell death pathways both through transcriptional upregulation of pro-survival genes and through post-translational modification of key signaling proteins such as RIPK1 [21] , as well as inducing caspase-8-dependent cleavage of the pro-necroptotic molecule CYLD [22] . Cell death and inflammatory gene expression are therefore thought to be mutually exclusive programs . However , recent studies have revealed that caspase-8 nonetheless regulates innate anti-microbial responses [23–29] . These studies have primarily investigated the function of caspase-8 in the context of RIPK3 deficiency , and have not addressed the potential scaffolding and enzymatic activities of caspase-8 in controlling these distinct functions . Moreover , it is unclear whether caspase-8 plays a cell-intrinsic role in controlling gene expression in vivo during bacterial infection . Combined deficiency of caspase-8 or FADD and RIPK3 leads to significant reduction in the secretion of a number of pro-inflammatory mediators as well as loss of inflammasome priming and activation in response to some stimuli [23 , 24 , 26 , 29] . Interestingly , while many of these inflammatory mediators are regulated by the NF-κB signaling pathway , whether caspase-8 regulates proximal NF-κB signaling , and even whether caspase-8 acts as a negative or positive regulator of inflammatory gene expression remains unresolved , due to the coupling of caspase-8 deficiency with induction of programmed necrosis . Thus , these studies take place either under conditions where RIPK3 is ablated [23 , 24 , 26 , 29] , or under conditions where programmed necrosis can occur in cells with conditional deletion of caspase-8 [13 , 30–32] . How caspase-8 might function to regulate both cell death and inflammatory gene expression , and whether enzymatic activity plays a role in the latter response is currently unknown . Here we demonstrate that caspase-8 enzymatic activity is necessary for cell-intrinsic control of key inflammatory cytokine gene expression in response to gram-negative bacterial infection as well as multiple TLR agonists . We found that regulation of gene expression by caspase-8 was independent of cell death and caspase-8 apoptotic substrates . Notably , caspase-8 controlled expression of a key subset of TLR-induced genes that regulate inflammation and host defense . To dissect the contribution of caspase-8 activity to cytokine gene expression , we generated CRISPR-based caspase-8 knock-in mice in which the self-cleavage of caspase-8 was abrogated due to a mutation in aspartate 387 to alanine ( Casp8DA/DA ) . Intriguingly , Casp8DA/DA macrophages could not undergo caspase-8-dependent apoptosis , but were functional for caspase-8-dependent control of inflammatory cytokine expression . As Casp8DA/DA has no known enzymatic activity in the absence of the cFLIP , our findings implicate a novel function for the caspase-8/cFLIP heterodimer in induction of innate inflammatory gene expression and provide insight into control of antimicrobial host defense .
Ripk3-/-Casp8-/- mice exhibit severely diminished cytokine responses following infection by a number of gram-negative bacterial pathogens , including Yersinia , in contrast to Ripk3-/- mice , which have no discernible defect [24 , 26 , 29] . Yersinia induces caspase-8-dependent cell death in macrophages , which could promote host defense by release of intracellular alarmins that promote inflammatory cytokine production by bystander cells , or phagocytosis of Yersinia-infected apoptotic cells [26 , 33] . Alternatively , caspase-8 could have a cell-intrinsic effect on inflammatory gene expression , which has not previously been described in vivo during bacterial infection . To distinguish between these possibilities , we generated mixed bone marrow chimeras using transfer of congenically marked WT , Ripk3-/- , and Ripk3-/-Casp8-/- donor bone marrow at 1:1 ratios into lethally-irradiated wild-type ( B6 . SJL ) recipients ( Fig 1A and S1 Fig ) . Eight weeks post-reconstitution , mixed chimeric animals were infected with Yersinia pseudotuberculosis ( Yp ) , which causes a rapid and lethal bacteremia in animals with hematopoietic caspase-8 deficiency [26 , 29] . Strikingly , five days post-infection , we found a significant defect in the percentage of TNF and IL-6 positive Ripk3-/-Casp8-/- inflammatory monocytes isolated from the mesenteric lymph nodes ( mLN ) of mixed BM chimeras compared with either wild-type or Ripk3-/- cells from the same animal ( Fig 1B and 1C and S1B and S1C Fig ) . Moreover , this defect was equivalent to monocytes from recipients receiving only Ripk3-/-Casp8-/- BM , indicating that the presence of caspase-8-sufficient cells did not restore cytokine production to caspase-8-deficient cells in the same animal ( S1B and S1C Fig ) . Interestingly , the mean fluorescence intensity ( MFI ) of Ripk3-/-Casp8-/- cytokine positive cells was significantly lower than Ripk3-/- or B6 cells from the same mouse , indicating a reduced level of cytokine production per cell ( Fig 1D ) . We also observed a significantly lower frequency of Ripk3-/-Casp8-/- TNF-producing neutrophils compared to either Ripk3-/- or wild type neutrophils in the same mouse , indicating that this defect in inflammatory cytokine production in vivo extended to other innate cell types ( Fig 1E and 1F ) . The percent chimerism of these cell types was similar across all the genotypes , indicating similar generation and maintenance of Ripk3-/-Casp8-/- monocytes and neutrophils in a competitive environment ( S1D and S1E Fig ) . Mice reconstituted with Ripk3-/-Casp8-/- bone marrow cannot control Yersinia and harbor much higher bacterial burdens in their lymph nodes and spleen [26 , 29] . Importantly , the presence of WT or Ripk3-/- cells in the Ripk3-/-Casp8-/- mixed BM chimeras provided protection from Yersinia infection , as the Ripk3-/-Casp8-/- mixed BM chimeras had similar bacterial burdens compared to mice that contained only wild-type or a mixture of Ripk3-/- and wild-type bone marrow ( Fig 1G and S1F Fig ) . These data provide direct evidence that caspase-8 plays a key cell-intrinsic role in inflammatory gene expression during Yersinia infection independently of cell death . To further define this response , we investigated the potential contribution of caspase-8 to cytokine production in response to bacterial infection and individual pathogen-associated molecular patterns ( PAMPS ) in bone marrow-derived macrophages ( BMDMs ) . Ripk3-/-Casp8-/- BMDMs showed dramatically reduced IL-6 and IL-12p40 production compared with B6 or Ripk3-/- cells in response to Yersinia and Salmonella infection ( Fig 2A ) . Consistent with this as well as previous observations [23 , 24 , 26 , 29] , Ripk3-/-Casp8-/- BMDMs produced lower levels of secreted IL-6 , IL-12p40 , and TNF , as well as intracellular pro-IL-1β in response to LPS treatment ( Fig 2B and 2C ) . Importantly , Ripk3-/-Casp8-/- BMDMs did not have a global defect in LPS-induced responses , as Ifnb transcript levels were not decreased , suggesting a more selective effect of caspase-8 deficiency ( Fig 2D ) . Peritoneal macrophages isolated from Ripk3-/-Casp8-/- mice and stimulated ex vivo with LPS also showed a significant defect in TNF production relative to B6 and Ripk3-/- peritoneal macrophages ( Fig 2E ) , further supporting a role for caspase-8 in cytokine production by innate cells following TLR stimulation . Caspase-8 plays a role in control of cytokine responses downstream of TLR4 and TLR3 , both of which are coupled to TRIF [23] . Whether MyD88-dependent TLRs also engage caspase-8 for maximal cytokine production is not known . Surprisingly , RIPK3/caspase-8-deficient BMDMs exhibited reduced production of IL-6 , IL-12p40 , and TNF in response to the MyD88-dependent TLR2 and TLR9 ligands Pam3CSK4 and CpG , as well as the TRIF-dependent TLR3 agonist Poly ( I:C ) ( Fig 2F ) . These data demonstrate that caspase-8 mediates gene expression downstream of both TRIF- and MyD88-dependent pathways . These cytokines depend on NF-κB and AP-1 transcription factor family members , and previous studies have suggested that caspase-8 regulates the activation of NF-κB [13 , 29 , 31] . At what step and how caspase-8 might regulate NF-κB is nevertheless unclear . TLR signaling induces assembly of MyD88 into a complex with proteins of the IRAK family called the Myddosome [34 , 35] , which initiates the signaling events that lead to the activation of NF-κB transcription factors to mediate NF-κB-dependent cytokine and chemokine gene expression [34 , 35] . However , we did not detect caspase-8 in the Myddosome , and caspase-8 did not affect IRAK2 localization to the Myddosome in response to LPS-treatment ( S2A and S2B Fig ) , suggesting that caspase-8 is not involved in TLR-proximal signaling events . Initial studies had observed a role for caspase-8 in regulating the NF-κB pathway in lymphocytes [13 , 31] , and Weng et al . reported moderate effects on IκBα degradation in Ripk3-/-Casp8-/- macrophages that could potentially account for a defect in cytokine production . Nevertheless , consistent with Allam et al . [23] , degradation and resynthesis of IκBα in our hands was similar among LPS-treated B6 , Ripk3-/- , and Ripk3-/-Casp8-/- BMDMs at these timepoints ( S2C Fig ) . Moreover , other components of TLR signaling pathways including AKT and MAPK activation were not affected by caspase-8 , as we observed similar levels of AKT or p38 in LPS-treated Ripk3-/-Casp8-/- BMDMs compared to B6 and Ripk3-/- BMDMs ( S2D and S2E Fig ) . Finally , recruitment of the NF-κB family member p65 to caspase-8-inducible gene promoters was not affected in Ripk3-/-Casp8-/- BMDMs ( S2F Fig ) . Together , these data suggest that the role of caspase-8 in gene expression likely does not occur via receptor-proximal effects on classical NF-κB or MAPK signaling . Caspase-8 could potentially regulate inflammatory cytokine production by inducing mRNA transcription , or by promoting mRNA stability . Indeed , LPS-stimulated Ripk3-/-Casp8-/- BMDMs had significantly lower levels of Il1b , Il12b and Il6 mRNA transcript compared to Ripk3-/- or wild type BMDMs ( Fig 2G ) . Importantly however , Ripk3-/-Casp8-/- BMDMs exhibited equal levels of mRNA stability following treatment with the transcription synthesis inhibitor actinomycin D ( ActD ) , indicating that the reduced transcript levels in Ripk3-/-Casp8-/- cells were likely due to decreased transcriptional induction ( S3 Fig ) . Only a limited set of TLR-induced responses have been described to be regulated by caspase-8 [23–26 , 28 , 29] . To address the extent to which caspase-8 regulates TLR-induced gene expression , we transcriptionally profiled B6 , Ripk3-/- , and Ripk3-/-Casp8-/- BMDMs following LPS treatment . Based on our observations that maximal differences between Ripk3-/-Casp8-/- and B6 or Ripk3-/- BMDMs occurred at 6 hours post-infection , we performed RNA-seq analysis on these three genotypes of macrophages 6 hours after LPS stimulation . This timepoint also allowed for detection of both primary and secondary response genes [36] . To define the contribution of caspase-8 to the TLR4-induced transcriptional program , we analyzed the LPS-induced genes by Principle Component Analysis ( PCA ) , Gene Set Enrichment Analysis ( GSEA ) , hierarchical clustering , and Gene Ontology ( GO ) analysis ( Fig 3A ) . The PCA revealed that LPS treatment accounted for almost 96% of the variance among the samples ( PC1 ) , demonstrating that neither RIPK3 deficiency alone , nor combined deficiency of caspase-8 and RIPK3 globally affected LPS-induced gene expression ( Fig 3B ) . However , RIPK3/caspase-8-deficiency contributed to the second highest variance ( PC2 ) , implying a role for caspase-8 in LPS-induced gene expression ( Fig 3B ) . To determine the precise contribution of caspase-8 to the LPS response , we identified LPS-regulated genes that were altered in either Ripk3-/-Casp8-/- or Ripk3-/- BMDMs . Interestingly , 527 ( 8 . 3% ) of the 6379 genes were RIPK3/caspase-8-dependent , whereas only 62 of 6379 ( less than 1% ) were RIPK3-dependent ( S2 Text and S1 Table ) . Importantly , 479 ( 91% ) of the 527 genes affected in Ripk3-/-Casp8-/- BMDMs were unaffected by RIPK3 deficiency alone , implying a specific role for caspase-8 in induction of these genes . Hierarchical clustering of the caspase-8-dependent genes by Pearson correlation revealed two clusters of coordinately regulated genes ( Fig 3C ) . Genes in cluster 1 were more highly expressed in response to LPS in Ripk3-/-Casp8-/- BMDMs , whereas cluster 2 was composed of genes that were not as strongly induced by LPS stimulation in Ripk3-/-Casp8-/- BMDMs compared to B6 or Ripk3-/- BMDMs . Functional enrichment of cluster 2 genes using Gene Ontology ( GO ) analysis revealed genes associated with immune defense and transcriptional regulation ( Fig 3D ) . Genes belonging to the category of transcriptional regulation included JunB , Rel and Stat5a ( Fig 3E ) , while the category of immune defense included Il1a , Il1b , Ccl17 , Il12b , and Tnf ( Fig 3E ) . Although this subset of genes was still upregulated by LPS stimulation in Ripk3-/-Casp8-/- BMDMs , the degree of induction was significantly reduced relative to control cells ( S4A Fig ) . Intriguingly , Il1a , Il1b , Ccl17 and Il12b were among the top 20 most differentially expressed genes in the absence of caspase-8 . Furthermore , Gene Set Enrichment Analysis ( GSEA ) , which provides an unbiased way to identify coordinated changes in gene expression , demonstrated that genes involved in cytokine and chemokine signaling were significantly enriched in wild type BMDMs compared to caspase-8-deficient cells ( Fig 3F ) . These findings demonstrate that caspase-8 deficiency is associated with altered transcriptional responses of LPS-induced genes that play a particularly important role in inflammatory responses to infection . Caspase-8 has been reported to localize to the nucleus of B cells , raising the possibility that nuclear caspase-8 might play a role in gene expression [37] . However , we did not observe caspase-8 in the nucleus , despite seeing robust nuclear localization of the AP-1 family member JunB , suggesting that caspase-8 does not play a direct role in transcriptional activation of these target genes ( S4B Fig ) . Furthermore , both Ripk3-/- and Ripk3-/-Casp8-/- BMDMs phagocytosed and cleared Salmonella and Yersinia equivalently to wild-type cells , indicating that direct microbicidal mechanisms remain largely intact in Ripk3-/-Casp8-/- macrophages ( S4C Fig ) . Together , these data indicate that the loss of caspase-8 is associated with altered expression of a significant number of LPS-induced genes that play a critical role in inflammatory responses , potentially providing an explanation for the profound susceptibility of caspase-8-deficient animals to bacterial pathogens . TLRs , TNFR , and the IL-1R receptor family engage both shared and specific signaling modules to induce gene expression programs [38 , 39] . Notably , IL-1R and TLRs utilize the shared signaling adaptor MyD88 . Nevertheless , TNF and IL-1β stimulation does not induce the same genes in macrophages that are induced by TLRs , indicating important distinctions exist in the way that cells respond to these extracellular cues [39–41] . Indeed , whereas TLR signaling induces a large number of genes including inflammatory cytokines as well as chemokines in macrophages , stimulation of IL-1R and TNFR alone primarily induces expression of chemokines but not cytokines [38–40] . In order to test the potential role of caspase-8 in induction of IL-1R or TNFR-dependent genes , we therefore examined genes that were commonly induced in BMDMs by TLR4 , IL-1R , or TNFR stimulation . Interestingly , despite the role of caspase-8 in regulating cell death and survival decisions in macrophages in the context of TNFR as well as TLR signaling [42] , caspase-8 deletion did not affect the expression of Cxcl2 or Ccl22 in response to TNF , even though caspase-8 was necessary for optimal expression of these genes downstream of TLR engagement ( S4D and S4E Fig ) . Similarly , Cxcl1 , whose induction requires caspase-8 following TLR stimulation , was induced to a similar degree and with similar kinetics in response to IL-1R engagement in caspase-8-deficient BMDMs ( S4F Fig ) . Caspase-8 was previously shown to negatively regulate type I IFN in response to Sendai Virus ( SeV ) in human fibroblasts , in part through cleaving RIPK1 and shutting off RIPK1 signaling [27] . However , caspase-8 was dispensable for SeV-induced expression of IL-6 and IL-12p40 in BMDMs ( S5A Fig ) . Ripk3-/-Casp8-/- mice also had equivalent levels of cytokine responses in the lung , similar viral burdens , and similar kinetics of weight loss over the course of the infection ( S5B–S5D Fig ) . Thus , while caspase-8 is important for maximal inflammatory gene expression in response to bacterial infection , it is dispensable for innate responses against and clearance of SeV . Caspase-8 can be recruited to multiple protein complexes that carry out diverse functions and are assembled in response to specific extracellular cues . Caspase-8-dependent activation of caspase-1 during NLRP3 inflammasome activation does not depend on caspase activity , suggesting that in this context , caspase-8 plays a scaffolding role , potentially by recruiting caspase-1 to undergo auto-processing [43] . However , whether caspase-8 enzymatic activity is important for induction of inflammatory gene expression is currently unclear [13 , 14] . Caspase-8 homodimerization results in activation of apoptosis , whereas heterodimerization of caspase-8 with its catalytically inactive homolog cFLIP prevents both apoptosis and regulated necrosis [18 , 44] . Conditions that induce extrinsic apoptosis induce assembly of caspase-8 homodimers or oligomers , wherein caspase-8 undergoes autoprocessing [45 , 46] . While homodimerization is sufficient to activate the enzyme , subsequent autoprocessing stabilizes the cleaved dimer , and is required for caspase-8 to cleave its downstream apoptotic targets , Bid , caspase-3 and -7 [9] . In contrast , the caspase-8/cFLIP heterodimer requires catalytic activity but not autoprocessing to prevent necrosis [18] . Blocking caspase activity in the context of TNF or TLR stimulation therefore leads to programmed necrosis that depends on RIPK3 [47] . As RIPK3-deficient cells do not undergo cell death in response to blockade of caspase activity , we treated RIPK3-deficient cells with the pan-caspase inhibitor zVAD-fmk and the caspase-8-specific inhibitor IETD-fmk to address whether caspase-8 scaffolding or enzymatic activities were responsible for caspase-8-dependent gene expression . Intriguingly , zVAD-fmk significantly reduced production of IL-12p40 , IL-6 and pro-IL-1β in LPS-treated Ripk3-/- BMDMs ( Fig 4A–4C ) . Notably , Ripk3-/-Casp8-/- BMDMs exhibited significantly blunted responses to LPS , which were not substantially altered by inhibitor treatment . QVD-oph is another widely used pan-caspase inhibitor that does not display the same cytotoxicity as zVAD-fmk [48] . Critically , QVD-oph also significantly reduced IL-12p40 and IL-6 production following LPS treatment in both B6 and Ripk3-/- BMDMs ( Fig 4D ) . QVD-oph did not induce cytotoxicity in either LPS-treated or untreated B6 or Ripk3-/- BMDMs , and as expected , Ripk3-/- cells did not undergo cell death in response to zVAD-fmk ( Fig 4E ) . Importantly , in addition to broad spectrum caspase inhibitors , specific inhibition of caspase-8 activity with IETD-fmk in Ripk3-/- BMDMs also significantly blunted IL-12 , IL-6 and TNF production ( Fig 4F ) . These findings demonstrate that caspase activity contributes to LPS-induced gene expression independently of cell death . Moreover , BMDMs lacking both caspase-3 and -7 or treated with the caspase-3/7 inhibitor DEVD-fmk , were competent to induce inflammatory cytokine expression following LPS treatment , demonstrating that caspase-8 activity regulates TLR-induced gene expression independent of its downstream apoptotic caspases ( S6 Fig ) . While caspase-8 inhibits RIPK3-regulated necrosis via a caspase-8/cFLIP complex , mutation of RIPK3 or inhibition of RIPK3 kinase activity can also promote caspase-8-dependent apoptosis [8 , 49–51] . Given that under some circumstances , RIPK3 can potentiate some functions of caspase-8 , the impact of caspase-8 deficiency on gene expression could be the result of a combined loss of both RIPK3 and caspase-8 . However , due to the RIPK3-induced embryonic lethality of caspase-8-deficient animals , the effect of single deficiency in caspase-8 has been difficult to isolate . Conditional deletion of caspase-8 in vitro also results in significant toxicity due to induction of RIPK3-mediated necrosis even in cultured cells [52] . To address the possibility that a dual contribution of RIPK3 and caspase-8 was responsible for the deficiency in TLR-induced gene expression in Ripk3-/-Casp8-/- BMDMs , we sought to delete caspase-8 while maintaining RIPK3 expression . Critically , deletion of caspase-8 in the presence of RIPK3 is possible in the absence of Mixed Lineage Kinase Like ( MLKL ) , which is an essential effector of RIPK3-dependent necrosis [53–55] . Intriguingly , Mlkl-/-Casp8-/- BMDMs also produced significantly lower levels of IL-6 , IL-12p40 and TNF in response to LPS , Pam3CSK4 and CpG , relative to Mlkl-/- BMDMs ( Fig 5A–5C ) . These data demonstrate that caspase-8 controls cytokine expression independently of RIPK3 . Caspase-8 activity could potentially regulate TLR-induced gene expression either in the context of a cleaved caspase-8 homodimer , or as a caspase-8/cFLIP heterodimer . To distinguish between these possibilities and to gain further insight into how caspase-8 regulates TLR-induced gene expression , we generated a caspase-8 mutant knock-in mouse , in which aspartate 387 is replaced with an alanine residue ( Casp8DA/DA ) ( Fig 6A ) . Although caspase-8 deficiency is embryonically lethal , mice expressing non-cleavable caspase-8 are viable , because the enzymatic activity of caspase-8 in the context of a caspase-8/cFLIP heterodimer inhibits programmed necrosis [18 , 56 , 57] . Interestingly , this heterodimer can also process caspase-8 substrates , and the substrate preference of caspase-8/cFLIP differs from that of the caspase-8 homodimer [58 , 59] . Therefore , the Casp8DA/DA mouse provides a means to distinguish the roles of the caspase-8 homodimer and caspase-8/cFLIP heterodimer in gene expression . Notably , Casp8DA/DA BMDMs were unable to process caspase-8 in response to Yersinia , which induces caspase-8 cleavage and cell death in wild type or Casp8DA/+ macrophages that depends on the Yersinia effector protein YopJ ( Fig 6B–6D ) [60] . Importantly , Yersinia-induced caspase-3 cleavage , which is caspase-8-dependent [26] is specifically abrogated in Casp8DA/DA BMDMs ( Fig 6C ) . Surprisingly , despite the absence of caspase-3 cleavage , Casp8DA/DA macrophages exhibited equivalent levels of cell death in response to Yersinia infection ( Fig 6D ) . Critically , Casp8DA/DA BMDMs but not Casp8+/+ BMDMs were protected from Yersinia-induced cell death upon treatment with the RIPK3 inhibitor , GSK’ 872 ( Fig 6D ) . Together , these data demonstrate that caspase-8 cleavage is necessary for Yersinia-induced apoptosis in macrophages , and in the absence of this cleavage , Yersinia-infected Casp8DA/DA BMDMs undergo RIPK3-dependent necrosis . These data imply that caspase-8 D387A does not activate apoptosis , but likely forms a heterodimer with cFLIP in macrophages . Interestingly , Casp8DA/DA and Casp8DA/+ BMDMs responded equally well to stimulation with LPS , Pam3CSK4 or CpG compared to littermate control WT BMDMs , as we observed similar frequencies of IL-12p40+ , IL-1β+ and IL-6+ cells among Casp8+/+ , Casp8DA/+ and Casp8DA/DA BMDMs , and Casp8DA/DA BMDMs produced WT levels of TNF in response to LPS , Pam3CSK4 or CpG ( Fig 6E–6H ) . Altogether , our findings indicate that caspase-8 enzymatic , but not its auto-processing , activity plays an important role in optimal production of TLR-dependent inflammatory cytokines . Caspase-8 autoprocessing is required to stabilize the active enzyme for cleavage of its apoptotic targets [9] . However , uncleaved caspase-8 interacts with cFLIP to prevent programmed necrosis , and the enzymatic activity of the caspase-8/cFLIP heterodimer is required for this function [18] . Whether uncleaved caspase-8 functions together with cFLIP or in a previously undescribed homodimeric complex to mediate inflammatory gene expression has not been determined . Importantly , cFLIP is required to limit both caspase-8-mediated apoptosis and RIPK3-mediated necrosis , as combined deficiency of cFLIP and RIPK3 results in caspase-8-dependent embryonic lethality [56] . To test the potential role of cFLIP in promoting caspase-8-dependent gene expression , we knocked-down cFLIP in Ripk3-/-Casp8DA/DA BMDMs , which allowed us to limit expression of cFLIP without the potential confounding effects of inducing programmed necrosis . Importantly , IL-12 expression was similar in Casp8DA/DA and Ripk3-/-Casp8DA/DA BMDMs ( Fig 7A and 7B ) . As expected , untreated cells expressed very low levels of the long and short forms of cFLIP ( cFLIPL and cFLIPS ) , and expression of both isoforms increased in response to TLR stimulation ( Fig 7C ) . Critically , we observed significantly reduced levels of both cFLIPL and cFLIPS protein , but not caspase-8 itself , in the cFLIP siRNA-treated compared with control scramble ( scr ) siRNA-treated cells following LPS or CpG treatment ( Fig 7C and 7D ) . Notably , although knockdown of cFLIP was not complete , it resulted in significantly lower levels of IL-12 secretion as well as reduced frequency of IL-12 positive cells relative to cells treated with scr siRNA ( Fig 7D and 7E ) . Altogether , these data implicate cFLIP as an important regulator of caspase-8-dependent expression of inflammatory cytokines downstream of TLR signaling , and suggest that cFLIP and caspase-8 function together to promote inflammatory gene expression during sensing of microbial infection by TLRs .
Caspase-8 is a central regulator of cell fate decisions in the context of bacterial infection and inflammatory stimuli . The precise nature of the interactions between caspase-8 and a number of signaling and adapter proteins , such as cFLIP , RIPK1 , RIPK3 and FADD , in the context of specific extracellular cues , determines whether the cell undergoes apoptosis , RIPK3-dependent programmed necrosis , or initiates inflammatory gene expression . Infection by the bacterial pathogen Yersinia induces innate immune cells to undergo caspase-8-mediated apoptosis , and it has been suggested that apoptosis of bacteria-infected cells promotes immune defense against infection [33 , 61 , 62] . However , whether caspase-8 controls anti-bacterial immune defense by regulating cell death or control of inflammatory cytokine production , as well as how caspase-8 controls inflammatory cytokine production during bacterial infection , remain unclear . Germline mutations in human caspase-8 or its key adapter FADD cause a primary immunodeficiency associated with severe recurrent bacterial infections , encephalopathy and hepatopathy [10 , 12] . While caspase-8 has been proposed to regulate NF-κB signaling in lymphocytes following antigen receptor activation [13 , 31 , 63] , Ripk3-/-Casp8-/- T cells do not exhibit defects in NF-κB activation or antigen-specific IFNγ production [14 , 15 , 23 , 64] . This suggested that caspase-8 regulates T cell responses by limiting RIPK3-dependent necrosis rather than controlling T cell activation itself . Nevertheless , several recent studies examining macrophages and dendritic cells from Ripk3-/-Casp8-/- animals have linked caspase-8 to the production of innate cytokines as well as to inflammasome activation [13 , 23 , 28 , 29 , 31 , 64–69] . Interestingly , caspase-8 has a non-enzymatic , scaffolding , role in NLRP3 inflammasome activation [43] . Recent studies have come to different conclusions about the contribution of caspase-8 to direct activation of the IKK complex and IκB degradation in innate cells [23 , 29] . Moreover , while caspase-8 mediates apoptosis and prevents RIPK3-necrosis downstream of the TLR4- or TLR3-TRIF axes [3–5 , 52 , 70] , the extent to which caspase-8 regulates the program of TLR-induced gene expression has not been defined . Here , we report a pleiotropic role for caspase-8 in the control of gene expression downstream of Yersinia and Salmonella infection , as well as all TLRs that we tested , including those that signal through MyD88 . We also find that while caspase-8 enzymatic activity is necessary , caspase-8 autoprocessing is dispensable for its function in regulating inflammatory gene expression . As uncleaved caspase-8 acts in an enzymatically active complex with its homolog cFLIP , our findings support a model ( Fig 8 ) , whereby the enzymatic activity of a caspase-8/cFLIP complex promotes TLR-induced inflammatory gene expression . Precisely how caspase-8 might be coupled to MyD88 downstream of TLR signaling is currently unclear , but both FADD and TRADD can associate with MyD88 through their death domains , providing a potential mechanism by which this could occur [71–73] . We nevertheless have not been able to detect endogenous caspase-8 at MyDDosomes in BMDMs , suggesting that if this interaction occurs it is either transient or unstable . It is possible that caspase-8 contributes to other innate signaling pathways , such as the NOD1/NOD2-RIPK2 pathway that detects the cytosolic presence of bacterial cell wall components . However , stimulation of macrophages with NOD2 ligands alone does not induce inflammatory cytokines; rather , NOD2 synergizes with TLR stimulation to enhance cytokine production [74–76] . A potential role for caspase-8 in facilitating this synergy is possible , but is difficult to distinguish from direct effects of caspase-8 on TLR signaling . Caspase-8 regulates a critical subset of LPS-induced genes that individually are known to have key roles in inflammation and anti-microbial immune defense . Notably , IL-12 , IL-6 , pro-IL-1α , pro-IL-1β , as well as multiple chemokine genes were among the LPS-induced genes in cluster 2 that required caspase-8 for optimal induction . The 527 genes in both cluster 1 and 2 whose expression was significantly impacted by caspase-8 deletion collectively constitute 8 . 3% of total LPS-responsive genes in WT cells . Interestingly , cluster 1 genes were upregulated in the absence of caspase-8 relative to control B6 or Ripk3-/- cells . GO analysis of the genes in cluster 1 did not reveal significant enrichment for any specific biological pathways or terms . However , it is possible that elevated expression of some of these genes could play a negative feedback role in regulating the genes in cluster 2 . Mechanistically , our data also demonstrate that caspase-8 enzymatic activity , most likely in the context of a caspase-8/cFLIP heterodimer , plays a key role in the induction of gene expression independently of apoptotic caspases or any other effects on cell death signaling . The precise targets of this activity remain to be defined , but do not involve the known apoptotic substrates caspase-3 and -7 . Moreover , our findings that the gene expression defect of caspase-8-deficient cells is cell-intrinsic exclude a model in which the diminished cytokine production by caspase-8-deficient cells results from reduced release of intracellular alarmins . The profound loss of anti-microbial immune defense and severe susceptibility to bacterial infection in caspase-8-deficient animals is therefore likely the result of compound defects in antimicrobial cytokine production , rather than deficient apoptosis per-se . It is possible that some of the effects we observe are the result of the dual deletion of RIPK3 and caspase-8 . RIPK3 has been implicated in inflammatory responses independent of cell death , as RIPK3 itself can promote IL-1β expression in dendritic cells via induction of mitochondrial ROS [69] . Similarly , conditional deletion of caspase-8 in dendritic cells resulted in elevated production of IL-1β due to RIPK3-dependent activation of the NLRP3 inflammasome [77] , and led to systemic autoimmunity [78] . Caspase-8 and RIPK3 may play cell type-specific roles in regulating inflammatory gene expression . However , the increased inflammatory responses observed in the setting of conditional deletion of caspase-8 or FADD in vivo are likely due to de-repression of RIPK3-dependent necrosis , because they are abrogated in a RIPK3-deficient setting [17 , 20 , 79] . We did not observe a contribution of RIPK3 to the caspase-8-dependent gene expression program , in that loss of RIPK3 alone did not substantially affect expression of pro-IL-1β or other inflammatory cytokines , either in vitro in BMDMs , or in vivo in monocytes or neutrophils in response to bacterial infection . While this manuscript was under review , the kinase activities of RIPK1 and RIPK3 were found to control inflammatory cytokine production in response to TLR4 stimulation via recruitment of Erk1/2 to the necrosome platform [80] . Interestingly , in BMDMs , this was observed only in the presence of caspase inhibitors , which is consistent with our observation that blockade of caspase activity in cells that are protected from programmed necrosis abrogates inflammatory gene expression . Additional evidence further supports a role for caspase-8 in regulating gene expression independent of RIPK3: first , Mlkl-/-Casp8-/- mice have a significant defect in production of pro-inflammatory cytokines in response to multiple TLR agonists , despite having functional RIPK3 . Second , treatment of RIPK3-sufficient B6 or Ripk3-/- BMDMs with a caspase-8 selective inhibitor , or the pan-caspase inhibitor QVD-oph , which do not potentiate programmed necrosis , also significantly reduced cytokine production in response to TLR stimulation . Additionally , reduced inflammatory gene expression in Ripk3-/-Casp8-/- cells is not due to a developmental defect in these cells because acute inhibition of caspase activity in Ripk3-/- cells was sufficient to inhibit TLR-induced gene expression . The role of caspase-8 as a key modulator of gene expression makes it functionally analogous to RIPK1 , which is also a central regulator of apoptosis , necrosis and gene expression [8] . It is possible that like RIPK1 , distinct post-translational modifications or recruitment to distinct complexes mediate the switch between caspase-8-dependent apoptosis and gene expression . Caspase-8 may regulate gene expression through a RIPK1-containing complex distinct from complex II . However , since RIPK1 plays a central scaffolding role in receptor-proximal activation of the IKK complex , the effect of RIPK1 deficiency on TLR-induced gene expression is likely more profound than that of caspase-8 . Like TLR signaling , TNFR activation can trigger survival and cytokine production , apoptosis , or necrosis [7 , 8] . Caspase-8 controls cell death in response to both TLR and TNFR signaling pathways . Nevertheless , the common chemokine genes Cxcl2 and Ccl22 that are induced by both LPS and TNF in BMDMs , required caspase-8 for maximal induction in response to LPS but not TNF . Whether caspase-8 contributes to TNFR-dependent gene expression in other cell types remains to be determined . Furthermore , although caspase-8-deficient BMDMs had a defect in their ability to respond to infection by gram-negative bacteria as well as multiple TLR agonists , Ripk3-/-Casp8-/- BMDMs produced equivalent levels of cytokines in response to Sendai virus infection , which engages the cytosolic PRRs RIG-I and MDA5 . Our findings suggest that macrophages do not require caspase-8 for induction of inflammatory cytokines by cytosolic nucleic acid sensors . Thus , while caspase-8 regulates apoptosis and programmed necrosis downstream of multiple receptors , in macrophages , caspase-8 specifically controls gene expression in response to TLR signaling . The defect in production of IL-6 , TNF , IL-12p40 and proIL-1β by Ripk3-/-Casp8-/- cells in response to MyD88-dependent TLR agonists such as CpG and Pam3CSK4 could be due to the recently reported role of TRIF participating in signaling downstream of classical MyD88-dependent TLRs , as was suggested in the case of TLR2 [81] . However , TRIF does not contribute to TLR2-dependent induction of IL-6 or TNF [81] , which are reduced in caspase-8-deficient cells in response to the TLR2 ligand Pam3CSK4 . Therefore , caspase-8 may participate at a distal step of TLR signaling in a manner that affects both MyD88- and TRIF-dependent gene expression . We did not observe caspase-8-dependent regulation of receptor-proximal signaling events , such as Myddosome formation , IκBα degradation , or activation of the MAPK p38 . Moreover , p65 was recruited equivalently to promoters of caspase-8-dependent genes in Ripk3-/-Casp8-/- cells , suggesting that nuclear localization of p65 is not affected by caspase-8 deficiency . These observations , along with the finding that IκBα resynthesis , which depends on NF-κB itself , was unaffected by caspase-8 deficiency , are consistent with our transcriptional profiling studies indicating that over 90% of LPS-induced gene expression remains unaffected in caspase-8-deficient cells . It is currently unclear why caspase-8 plays a role in inflammatory cytokine expression by TLRs , but not IL-1R , which also signals through MyD88 , or TNFR , which engages caspase-8 for apoptosis . However , differences in the response of macrophages to these stimuli are well established . For example , inflammatory cytokines are not induced in macrophages in response to TNF or IL-1β [38] . Recent studies revealed differences in the latent enhancers activated in macrophages in response to LPS , CpG , IL-1β , and TNF stimulation , suggesting that differences in signaling output exist between these stimuli despite their use of shared signaling components [40] . The engagement of caspase-8 by TLRs for transcriptional activation may indeed be one of the mediators of these differences . Differences in the stoichiometry or composition of specific downstream signaling components , such as IRAK-1 and IRAK-2 or particular TRAFs could potentially account for these differences . It is also possible that caspase-8 plays cell-type specific roles in response to stimulation of some receptors versus others . Identifying the potential caspase-8 substrates responsible for inflammatory cytokine induction may shed further light on this question . Another unexpected finding here was that while Casp8DA/DA macrophages did not undergo apoptosis in response to Yersinia infection , they still undergo RIPK3-dependent necrosis , rather than being protected from death . This is in contrast to Casp8DA/DA macrophages treated with LPS , which do not have increased levels of programmed necrosis . This is likely due to the activity of YopJ in the context of Yersinia infection , which potently inhibits the IKK complex , and therefore likely impacts the ability of IKK complex to provide a transcription-independent prosurvival signal to RIPK1 [21] . Future studies will investigate the impact of switching cell death from apoptosis to necrosis in the context of Yersinia infection on host defense and inflammatory responses . The precise target or targets of caspase-8/cFLIP activity that mediate this gene regulatory function remain to be identified . Nevertheless , our findings provide the first demonstration that enzymatic activity of caspase-8 plays a key cell-intrinsic role in TLR-dependent gene expression and control of Yersinia infection , independent of cell death . Together , these data provide new mechanistic insight into the non-apoptotic function of caspase-8 in innate immune defense , which may account for the severe susceptibility of mice and humans lacking caspase-8 to microbial infections .
C57BL/6 . SJL mice were obtained from Jackson Laboratories . The Ripk3-/-Casp8-/- mice used in these studies were previously described [26] and provided by Doug Green ( St . Jude Children’s Research Hospital ) . Ripk3-/- mice were provided by Vishva M . Dixit ( Genentech ) . Casp8f/- mice were provided by Rasq Hakem ( University Health Network ) . Mlkl-/- mice were provided by Warren Alexander ( The Walter and Eliza Hall Institute of Medical Research ) . Casp8+/- mice that were used to generate the Mlkl-/-Casp8-/- mice were provided by Steve Heddrick ( UCSD ) . Casp3fl/flTie2-Cre+ and Casp7-/- mice were previously described [82 , 83] . Six- to eight-week-old C57BL/6 . SJL mice were lethally irradiated with 1100 rads and 2–5 x 106 Ripk3-/- , Ripk3-/-Casp8-/- or C57BL/6 ( Jackson Laboratories ) congenic bone marrow ( BM ) cells were transferred i . v . BM chimeras were allowed to reconstitute for eight to ten weeks . For other animal experiments , age- and sex-matched six-to-eight-week old mice were used . Casp8DA/DA mice were generated using the reagents and protocol described by Henao-Mejia and colleagues [84] . Briefly , single guide RNA ( gRNA:ACAGAACCACACTTTAGAAG GTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCTTTTTT ) and Cas9 RNA were in vitro transcribed , purified and injected into B6xSJL F1 embryos with repair oligoDNA containing 50 base pairs of flanking regions on each side ( TTGCCTCATCATCTCACAAGAACTATATTCCGGATGAGGCAGAT ) . The complementary gRNA sequence is underlined , mutated base pairs are underlined and in italics , and the codon change for D387A is in boldface . Embryos were transferred to pseudopregnant females and pups were screened for mutagenesis using Surveyor Mutation Detection Kit ( IDT ) . Founders were identified by cloning PCR products of tail DNA ( primer F: TTCACTGGTTCAAAGTGCCC , primer R: ACTTTGCCAGAGCCTGAGGG ) according to manufacturer’s instructions ( M13 primers , TOPO TA Cloning Kit for Sequencing , ThermoFisher ) , followed by sequencing using T3 and T7 primers . Mice were backcrossed onto C57BL/6J for 5 generations until a minimum of 95% B6 was achieved by SNP analysis ( Jax Genome Scanning Service ) . Casp8DA/+ x Casp8DA/+ mice were crossed and littermates were used for experiments . For Yersinia infections , mice were fasted for 12–16 hours and infected orally with 1–4 x 108 Yersinia ( 32777 ) . Mice were sacrificed and tissues were harvested on days 3 and 5 post-infection , as indicated . Bacterial load was determined by plating dilutions of tissue homogenates on LB+irgasan plates and single cell analysis was performed by flow cytometry . For in vivo Sendai virus infections , mice were anaesthetized using ketamine and infected with 104 of SeV strain 52 ( low defective viral genomes ) per mouse [85] . Mice were weighed every one to two days . On days 3 and 10 , lungs were harvested and homogenized in Trizol Reagent ( Invitrogen/ThermoFisher ) for RT-qPCR analysis . For animal studies , mesenteric lymph nodes were isolated and plated in complete-DMEM containing brefeldin A ( Sigma ) and monensin ( BD ) in a 37°C humidified incubator for 5 hrs . For BMDMs , cells were plated in 48- or 12-well suspension dishes and pre-treated with zVAD-fmk ( 100 μM ) for 1 hr prior to PAMP stimulation , where indicated . BFA and monensin were added 1 hr later and samples were harvested for analysis 4–5 hrs later . BMDMs were harvested using PBS with EDTA ( 2mM ) prior to staining . Cells were washed with PBS , stained for viability ( Zombie Yellow , BioLegend ) and then stained with the following antibodies from BioLegend: Ly6G ( clone 1A-8 PE-Cy7 ) , IL-6 ( MP5-20F3 APC/FITC ) , F4/80 ( clone BM8 Pacific Blue ) , BD Biosciences: CD45 . 1 ( clone A20 APC-Cy7 ) , CD45 . 2 ( clone 104 FITC ) , MHCII ( clone M5/114 BV650 ) , ThermoFisher: CD11b ( clone M1/70 . 15 PE-Texas Red ) , eBioscience: Ly6C ( clone HK1 . 4 PerCPCy5 . 5 ) , CD11c ( clone N418 AF700 ) , F480 ( clone BM8 APC-eF780 ) , TNF ( clone MP6-XT22 eF450 ) , IL-12p40 ( clone C17 . 8 PE ) , proIL-1β ( clone NJTEN3 APC/FITC ) , or cleaved caspase-3 ( Asp 175 , Cell Signaling Technologies ) . Inflammatory monocytes and neutrophils were gated as follows live CD45 . 1+2+/CD45 . 2+ , CD11c- , CD11bhi , Ly6Chi or Ly6G+ . BMDMs were gated on Live/dead- , singlets , and in Fig 7 also CD11b+F480+ . Resident peritoneal exudate cells ( PECs ) were isolated from naïve six- to eight-week-old sex-matched mice with cold PBS . PECs were incubated with LPS ( 10 ng/mL ) , BFA and monensin for 4hrs and large peritoneal macrophage responses were examined based on gating strategy as published by Ghosn et al . [86] . Surface staining was performed in FACS buffer ( PBS with 1% BSA , 2mM EDTA ) and sample fixation and permeabilization prior to intracellular staining were all performed according to manufacturer’s instructions ( BD ) . Samples were run on an LSRFortessa and analyzed using FlowJo Treestar software . Bone marrow-derived macrophages ( BMDMs ) were grown as previously described [87] in a 37°C 5% CO2 humidified incubator in DMEM supplemented with 10% FBS , HEPES , sodium pyruvate ( complete-DMEM ) and 30% L929 supernatant for 7–9 days . 16–20 hrs prior to infection/treatment cells were re-plated into 96- , 48- , 24- , 12- or 6-well dishes in complete-DMEM containing 10% L929 supernatant . Yersinia were grown overnight with aeration in 2xYT broth at 26°C . Salmonella were grown overnight in LB medium at 37°C with aeration . Bacteria were washed three times with pre-warmed DMEM , added to the cells at an MOI of 20:1 , and spun onto the cells at 1000 rpm for 5 min . Cells and bacteria were incubated at 37°C for 1 hr post-infection followed by addition of 100 μg/mL gentamicin . zVAD-fmk , QVD-oph , IETD-fmk or DEVD-fmk ( 100 μM , SM Biochemicals ) were added 1 hr prior to treatment with LPS ( 100 ng/mL ) . GSK2399872A ( GSK’872 , 3 μm , GSK ) was added 1 hr prior to infection where indicated . For in vitro Sendai virus infections , cells were washed twice with warm PBS , and infected with Sendai virus Cantell ( high defective viral genomes ) in a low volume of serum-free media as previously described [88] . 1x106 BMDMs/well were plated in 6-well tissue culture-treated dishes 16 hrs before the experiment . Cells were treated with 10 ng/mL mIL-1β ( eBioscience ) for 2 hrs or with 10 ng/mL mTNFa ( BioLegend ) for 6hrs . For RNA stability experiments , BMDMs were stimulated with LPS ( 100 ng/mL ) for 2 hrs , and then treated with Actinomycin D ( Sigma , 5 μg/mL ) . Samples were lysed in Trizol Reagent ( Invitrogen/ThermoFisher ) and RNA was extracted using phenol/chloroform method . RNA was resuspended in RNAse-free water and cDNA synthesis was performed using High Capacity RNA to cDNA kit ( ThermoFisher ) as per manufacturer’s instructions . qPCR was run using Power Sybr Green Master Mix ( ThermoFisher ) on a QuantStudio Flex6000 ( ThermoFisher ) . Primer sequences used are listed in S2 Table . BMDMs were prepared as described above and 1x106 BMDMs/well were plated in 6-well tissue culture-treated dishes and placed in a 5% CO2 37°C incubator for 16 hours . BMDMs were stimulated with LPS ( 100 ng/mL ) for 6 hrs . RNA was isolated using the RNeasy Mini Kit ( Qiagen ) and processed as per manufacturer’s instructions . mRNA-seq libraries were prepared using the TruSeq Stranded Total RNA LT Kit with Ribo-Zero Gold , according to the manufacturer’s instructions . Samples were run on Illumina NextSeq 500 to generate between 151 base-pair , paired-end reads with a Q30 score of ~80% , resulting in 25–50 million fragments/sample . All data processing and analyses were carried out using the R programming language ( Version 3 . 2 . 2 ) and the RStudio interface ( Version 0 . 99 . 489 ) , as described previously [89] and can be reproduced using the supplementary code file . Briefly , raw fastq files were aligned to version 79 of mouse reference genome GRCm38 using the Subread aligner [90] in the RSubread package . BAM files were summarized to genes using the featureCounts algorithm [91] . Raw data is available on the Gene Expression Omnibus ( GEO ) ( accession #GSE86922 ) . Differentially expressed genes ( ≥1 . 5-fold and ≤5% false discovery rate ) were identified by linear modeling and Bayesian statistics using the VOOM function [92] in the Limma package [93] . Gene Ontology ( GO ) was performed using the Database for Annotation , Visualization and Integration of Data ( DAVID ) [94 , 95] . Gene Set Enrichment Analysis ( GSEA ) [96] was performed against the Molecular Signatures Database ( MSigDB ) [97] using the C2 canonical pathways collection . BMDMs were seeded in 48-well tissue culture-treated dishes 16–20 hrs prior to transfection . The transfection protocol was modified from the “HiPerFect Transfection Reagent Handbook , Qiagen” for macrophage lines . Briefly , scramble or cFLIP pools of siRNA ( 25 nM , Dharmacon ) were mixed with HiPerFect Transfection Reagent ( Qiagen ) in serum-free media , incubated at room temperature to allow RNA complex formation and added dropwise to cells cultured in an equal volume of complete DMEM ( with serum , see above ) . Cells were incubated for 6 hrs under normal growth conditions . 2X volume of complete DMEM was carefully added and 18 hrs later , cell culture medium was replaced with complete DMEM containing 10% L929 supernatant . Cells were incubated for a further 24 hrs before treatment with PAMPs . BMDMs were treated with 100 ng/mL E . coli LPS ( Sigma ) , 1 μg/mL Pam3CSK4 ( Invivogen ) , 1 μg/mL CpG ( Invivogen ) , 50 μg/mL HMW Poly ( I:C ) ( Invivogen ) . Release of proinflammatory cytokines was measured by enzyme-linked immunosorbent assay ( ELISA ) using capture and detection antibodies against IL-6 ( BD ) , IL-12p40 ( BD ) or TNFα ( BioLegend ) . LDH release was quantified using the Cytotox96 Assay Kit ( Promega ) according to the manufacturer's instructions . Cytotoxicity was normalized to Triton ( 100% ) and LDH release from untreated cells was used for background subtraction . All animal studies were performed in compliance with the federal regulations set forth in the Animal Welfare Act ( AWA ) , the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health , and the guidelines of the University of Pennsylvania Institutional Animal Use and Care Committee . All protocols used in this study were approved by the Institutional Animal Care and Use Committee at the University of Pennsylvania ( Multiple Project Assurance # A3709-01 , Protocols #804523 and #805061 ) . | TLR signaling induces expression of key inflammatory cytokines and pro-survival factors that facilitate control of microbial infection . TLR signaling can also engage cell death pathways through activation of enzymes known as caspases . Caspase-8 activates apoptosis in response to infection by pathogens that interfere with NF-κB signaling , including Yersinia , but has also recently been linked to control of inflammatory gene expression . Pathogenic Yersinia can cause severe disease ranging from gastroenteritis to plague . While caspase-8 mediates cell death in response to Yersinia infection as well as other signals , its precise role in gene expression and host defense during in vivo infection is unknown . Here , we show that caspase-8 activity promotes cell-intrinsic cytokine expression , independent of its role in cell death in response to Yersinia infection . Our studies further demonstrate that caspase-8 enzymatic activity plays a previously undescribed role in ensuring optimal TLR-induced gene expression by innate cells during bacterial infection . This work sheds new light on mechanisms that regulate essential innate anti-bacterial immune defense . | [
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] | 2016 | Activity of Uncleaved Caspase-8 Controls Anti-bacterial Immune Defense and TLR-Induced Cytokine Production Independent of Cell Death |
Little is known about extrinsic signals required for the advancement of motor neuron ( MN ) axons , which extend over long distances in the periphery to form precise connections with target muscles . Here we present that Rnf165 ( Arkadia-like; Arkadia2; Ark2C ) is expressed specifically in the nervous system and that its loss in mice causes motor innervation defects that originate during development and lead to wasting and death before weaning . The defects range from severe reduction of motor axon extension as observed in the dorsal forelimb to shortening of presynaptic branches of the phrenic nerve , as observed in the diaphragm . Molecular functional analysis showed that in the context of the spinal cord Ark2C enhances transcriptional responses of the Smad1/5/8 effectors , which are activated ( phosphorylated ) downstream of Bone Morphogenetic Protein ( BMP ) signals . Consistent with Ark2C-modulated BMP signaling influencing motor axons , motor pools in the spinal cord were found to harbor phosphorylated Smad1/5/8 ( pSmad ) and treatment of primary MN with BMP inhibitor diminished axon length . In addition , genetic reduction of BMP-Smad signaling in Ark2C+/− mice caused the emergence of Ark2C−/−-like dorsal forelimb innervation deficits confirming that enhancement of BMP-Smad responses by Ark2C mediates efficient innervation . Together the above data reveal an involvement of BMP-Smad signaling in motor axon advancement .
The assembly of neural circuits is complex and highly specific . Progenitors and early postmitotic neurons acquire an intrinsic genetic program that controls circuit assembly steps including axonal path finding and synaptic partner recognition [1]–[3] . In the brain , extrinsic signals that control axon initiation and advancement are beginning to be identified [4] , [5] . Motor axons follow precise paths through peripheral tissues [6] , and extrinsic signals acting directly upon the motor axons [7] or upon adjacent sensory axons [8] have been implicated in steering their advancement . Additionally , intrinsic properties of different MN subtypes produce varying responses to extrinsic signals creating a highly specific pattern of innervation [9] . However , the developing peripheral tissue expresses many cytokines for its own development and the role of many of these in the manipulation of motor axon growth has not been fully addressed . Transforming Growth Factor ( TGF ) β signaling is essential for embryonic development and adult tissue homeostasis in both vertebrates and invertebrates . BMP and Nodal/Activin are distinct classes of ligands within the TGFβ cytokine family; they signal through serine/threonine kinase receptor complexes , composed of type I and type II receptors , which activate by phosphorylation the Smad1/5/8 ( pSmad1/5/8 ) and Smad2/3 ( pSmad2/3 ) effectors , respectively . PSmads complex with Smad4 to translocate to the nucleus where they function as transcription factors [10] . Alternatively , TGFβ ligands can signal in a Smad-independent fashion [11] . Loss of function mutations of most components of the signaling pathway in mice result in multiple defects and early embryonic lethality [12] , preventing the elucidation of their role in later aspects of development such as neuronal connectivity . Nevertheless emerging evidence supports a role , particularly for the BMP class of ligands , in axon guidance [13] . Smad-independent BMP signaling is associated with growth cone collapse and axon repulsion in the spinal cord [14] , [15] . Less clear is the role of BMP-Smad signaling in connectivity , particularly of axons extending in the periphery . BMP/Smad1 signaling has been shown to have a role in sensory neuron axon regeneration in adult mice and axon regrowth in cultured dorsal root ganglia ( DRG ) [16] , [17] , but its role in neuromuscular connectivity during development has not been addressed . Moreover , ligands released by peripheral synaptic targets have also been shown to activate neuronal Smads in a retrograde manner and specify cellular identity within sympathetic and trigeminal sensory neurons [18] , [19] . Retrograde Smad-dependent BMP signaling has also been shown to be required for MN synapse growth and plasticity in Drosophila [20]–[22] . However , the role of this pathway in mammalian MNs remains unknown . Nodal/Activin and BMP ligands are well-known for their dose-dependent effects during development and for the multiple mechanisms , both extracellular and intracellular , that regulate their signaling activity [23]–[30] . Key intracellular negative regulators include the inhibitory Smad6/7 and the nuclear co-repressors Ski and SnoN . Smad6/7 mediate the degradation of receptors and reduce the phosphorylation of effector Smads , while Ski/SnoN directly interact with the pSmad/Smad4 complex and recruit histone deacetylases to the promoters of target genes , thereby inhibiting transcription [29] . As these negative regulators are upregulated by Smads , the pathway activates a negative feedback loop “auto shut-off” mechanism . The degradation of such intracellular repressors provides a mechanism to reduce negative feedback and the requirement for continuous stimulation by the ligand . Arkadia/RNF111 , an E3 ubiquitin ligase , mediates the ubiquitin-proteasome degradation of Smad6/7 and Ski/SnoN [31]–[33] . Arkadia is required for head/anterior development in the mammalian embryo [34] , [35] , which also depends on the establishment of high Nodal-Smad2/3 signaling [30] . Interestingly , Arkadia specifically acts to derepress the Smad2/3 effectors because it removes Ski/SnoN only when they block these effectors . However , a similar mechanism for derepression of Smad1/5/8 and consequent intracellular enhancement of their transcriptional responses remained unknown . Here we present the identification and molecular characterization of RNF165/Ark2C , an E3 ubiquitin ligase with homology to Arkadia that derepresses specifically Smad1/5/8 responses . Ark2C is expressed in the nervous system in mice , and its loss leads to MN axon extension and muscle innervation deficits . In Ark2C+/− mice genetic reduction of BMP signaling by removal of one allele of BMPRII causes dorsal forelimb innervation defects such as those observed in Ark2C−/− mice . A similar phenotype is observed in Ark2C+/− mice upon deletion of Smad8 , an effector with a limited region of expression in the nervous system , which in the spinal cord includes subsets of MN . These results along with findings that limb motor pools in the spinal cord harbor nuclear phosphorylated Smad1/5/8 , and that treatment of MN in culture with BMP and its inhibitors enhances and reduces axon length respectively , support a role for this pathway in axon elongation . Furthermore , it appears that in a subset of MN this role of BMP depends on intracellular enhancement of the pathway by Ark2C .
The Arkadia-like locus contains two tandem promoters , separated by poly-adenylation sites , expressing two different genes ( Figure 1A ) : here named Arkadia2N ( Ark2N ) , with homology to the N-terminus of Arkadia , and Arkadia2C ( Ark2C ) , with homology to the C-terminus of Arkadia . Ark2C contains a RING domain previously annotated as Rnf165 , which is 85% identical to that of Arkadia . Other highly conserved domains include a nuclear localization signal and the NRG-TIER domain , which is involved in substrate interactions ( Figure 1B ) [32] , [33] , [35] . Sequence-alignment detected two additional Arkadia-like genes ( Figure S1A ) ; both were found to contain stop codons and were therefore considered to be pseudogenes . To address the function of Ark2C in vivo we generated mice carrying a gene-trap in the first intron ( Figure 1C and D ) . The expression of the gene-trap lacZ-reporter , which depends on the endogenous Ark2C promoter , revealed that Ark2C is expressed specifically in the nervous system ( Figure 1E and F and Figure S1B ) , including the spinal cord in both embryo ( Figure 1G ) and adult ( Figure 1H ) . Quantitative RT-PCR analysis of wild-type ( wt ) and homozygous ( Ark2C−/− ) embryonic brain RNA showed loss of expression in homozygous mutants , indicating that the gene-trap disrupts the transcription of Ark2C throughout the gene ( Figure 1I ) producing a null mutation . On the contrary , the gene trap does not affect the expression of the adjacent Ark2N as shown by quantitative RT-PCR ( Figure 1J ) . Therefore , this gene-trap strain can be used for Ark2C-specific loss of function studies . Approximately 10% of Ark2C−/− mice die at birth . The remaining Ark2C−/− pups are the same size as their littermates at birth ( Figure 2A ) , however they fail to thrive and grow , reaching only 50% of the size of their siblings at postnatal day 15 ( Figure 2B and C ) . Null pups are always born with relaxed forepaws and reduced dorsiflexion ( Figure 2A ) , while more severe phenotypes , including hind limb defects , can also be observed ( Figure 2C ) . The Ark2C−/− pups on a 129Sv/Ev genetic background all thin and die during the first 3 postnatal weeks . Postmortem examination revealed dehydration , an empty stomach and gut , and mild cyanosis , suggesting inefficient feeding and breathing . In outbred/mixed backgrounds , a small percentage ( around 5% ) of null animals survive past weaning age . These mice also display mild hypoxia as shown by an increase of blood plasma lactate ( mean of 6 . 11 mM versus 8 . 08 mM , 0 . 05>p>0 . 01; Figure 2D ) confirming inefficient breathing . As observed in newborns , Ark2C−/− mice examined at P21 or 3–6 months are unable to extend and spread forepaw digits ( shown by the absence of clear toes in pawprints in Figure 2E ) . Measurements of toe prints indicate that this is a significant trait of Ark2C−/− mice ( 6 . 5% of footprints have clear toes compared to 100% of wt littermates prints , p<0 . 01; Figure 2F ) . In addition , while the maximum stride width and length of Ark2C−/− animals are normal , a reduction in mean foot length due to reduced toe extension was observed ( 6 . 2 mm compared to 8 . 06 mm , p<0 . 01; Figure 2G and H ) . Furthermore , these animals exhibit atrophy in the forelimb muscles that control forepaw and digit movement ( Figure 3A ) . As P0 mice display relaxed paws prior to the adult phenotype , developing forelimb muscles were examined in Ark2C−/− embryos ( Figure 3B ) , however no abnormalities that could account for the observed muscle atrophy were observed . X-gal staining showed that Ark2C expression is restricted in the nervous system , including the entire spinal cord throughout development and in the adult ( Figure 1F–H and unpublished data ) . Furthermore , quantitative RT-PCR confirmed absence of Ark2C expression in wt embryonic ( E12 . 5 ) forelimb RNA ( Figure 3C ) . Together these observations indicate that the defect in Ark2C−/− mice is of neuronal origin and that Ark2C is involved in neuromuscular connectivity . As the majority of the homozygous mice thin and die before weaning , it is unlikely that this function is limited to motor innervation of the forelimb . We first analyzed the defective innervation of the forelimb by introducing into Ark2C mutant animals the HB9-eGFP transgene [36] , which is expressed specifically in MN . We found that at embryonic day 11 . 5 ( E11 . 5 ) Ark2C−/− embryos do not exhibit gross forelimb innervation abnormalities ( n = 22 forelimbs ) ; motor nerves exit the brachial plexus and project into both the dorsal and ventral forelimb ( Figure 4A ) [37] . Measurement of these projections found that both the length and width of the more dorsal radial and median nerves are reduced in Ark2C−/− embryos compared to wt . No compensatory increase in the size of the more ventral ulnar or thoracodorsal nerves was observed , suggesting that there is no misrouting of axons within the limb bud ( Figure 4B–D and Figure S2A–D ) . Three-dimensional projections of the innervation were also examined for abnormal sprouting of nerves from the brachial plexus , no misrouting was found ( n = 12 and 14 forelimbs; unpublished data ) , and the volume of the brachial plexus was not found to differ significantly in size between genotypes ( Figure 4E ) . As the embryo grows , this phenotype appears to increase in severity and become more specific to nerves innervating the dorsal region of the limb . By E12 . 5 , the radial nerve thins , and axons do not reach the more distal dorsal target muscles in 21 out of 24 Ark2C−/− limbs examined ( Figure 4F ) , while the ventral axon projections ( ulnar and median nerves ) parallel those of wt embryos . Similarly , at E13 . 5 , Ark2C−/− limbs exhibit dorsal muscle innervation deficits to varying degrees with increased severity towards the distal muscles controlling the digits ( Figure 4G and 4F; Figure S2G; Movies S1 and S2 ) . We compared confocal images from wt and Ark2C−/− forelimbs focusing on the presence and the intensity of the major partition points of the radial nerve . These partitions are clearly visible at E13 . 5 and correspond to specific forelimb muscle groups ( mapped by backfills , T . M . Jessell , personal communication ) as shown in the diagram ( Figure 4G ) . The distal partitions ( arrowheads 1 and 2 , Figure 4G ) innervate muscle groups such as the extensor digitorum communis ( EDC ) and extensor digiti quinti ( EDQ ) , while proximal partitions ( arrowheads 3 and 4 ) innervate muscles including the extensor carpi radialis longus ( ECRL ) and brevis ( ECRB ) . All Ark2C−/− forelimbs examined exhibit deficits in the distal partitions and more than half also exhibited proximal deficits ( Figure 4H ) . Forelimb motor defects were observed in all ( 250+ ) Ark2C−/− embryos and newborns ( Figure 2A ) analyzed to date independent of genetic background , however a similar phenotype has never been observed in heterozygote siblings ( unpublished data ) . The above data suggest that Ark2C is not required for the initial axon projection of the radial nerve from the brachial plexus into the dorsal forelimb mesenchyme , but it is essential for further motor axon advancement . The requirement for Ark2C increases as the embryo grows and is most obvious in motor axons that are required to grow the furthest to innervate more distal muscles . While there is thinning of the main radial nerve bundle and its partitions to various extensor muscles , the path taken by the nerve and the positions of characteristic points of bifurcation are not altered in the absence of Ark2C . These observations support a role for Ark2C in axon growth and not in path or target finding . The requirement for Ark2C function differs between nerves and embryonic stages: the radial nerve shows an increasing requirement with age , the median nerve requires Ark2C at E11 . 5 , but this need lessens with age while the ulnar nerve appears unaffected by loss of expression . These differences in requirement for Ark2C between MN are likely to be due to intrinsic differences in specification of the cells , the properties of the peripheral substrate that their axons elongate through , or an interaction between these two factors . To examine the extent of the requirement for Ark2C in MN projections , we analyzed the innervation of the diaphragm by the phrenic nerve , as this may be involved in the milder hypoxia phenotype observed in Ark2C−/− animals ( Figure 2D ) . In both Ark2C−/− embryos and surviving adults , the phrenic nerve forms synapses throughout the length of the diaphragm muscle ( n = 25; Figure 5A–B ) , however the synapses are clustered together around the intramuscular nerve fiber appearing to be at a greater density than in the wt ( Figure 5B ) . This increased density could reflect either an increase in the number of synapses formed or their distance from one another . Therefore , the number of synapses per mm of phrenic nerve and the width of the synaptic band were measured . By P17 Ark2C−/− animals are significantly smaller than their littermates , affecting the size of muscles and nerves , however at E18 . 5 Ark2C−/− embryos are of normal size . At this age loss of Ark2C expression produced a slight increase in number of synapses ( 351 . 0 synapses/mm of axon in wt and 373 . 5 synapses/mm in Ark2C−/− ) within the diaphragm; in addition , comparison of a single region of wt and Ark2C−/− diaphragms ( Figure 5A ) revealed that the width of the synaptic band is reduced in Ark2C−/− diaphragms ( Figure 5B ) , further increasing the density of synapses . The length of individual axons from the phrenic nerve to the synapse within the synaptic band was also reduced in both embryo and pup ( mean length at E18 , 117 . 30 µm and 84 . 75 µm , p<0 . 01; mean length at P17 , 193 . 61 µm and 125 . 02 µm , p<0 . 01; Figure 5B and C ) , suggesting that loss of Ark2C affects the extent rather than the directionality of terminal branch growth in the embryonic diaphragm . Quantitative RT-PCR showed that similar to the developing limb , Ark2C is not expressed in the diaphragm muscle at E19 . 5 , the stage when the innervation phenotype becomes apparent ( Figure 5D ) . This result supports a neuronal origin of the diaphragm innervation defect . The synapses in the Ark2C−/− P17 diaphragm have a pretzel-like morphology , suggesting normal activity-dependent maturation occurs postnatally . However , from the above analysis we cannot estimate the impact of short presynaptic motor branches on the function of the neuromuscular synapse and diaphragm muscle . It is possible that in the absence of Ark2C similar defects exist in MN that innervate additional muscles involved in breathing and that the observed hypoxia phenotype is cumulative from a number of innervation defects . The analysis of the forelimb and diaphragm innervations in Ark2C−/− embryos and adult mice shows that Ark2C functions primarily during development and growth and is required for efficient MN neuromuscular connectivity within target muscles . The enhancement of connectivity by Arkadia2C ranges from the advancement of major nerves such as the radial nerve through the developing limb bud to a more subtle growth of the presynaptic branches of the phrenic after entering the diaphragm muscle . Next we investigated the cause of the variable requirement of Ark2C between MN that innervate the dorsal and ventral forelimb at E12–13 . 5 . In the spinal cord , the lateral and medial divisions of the Lateral Motor Column , ( LMCl and LMCm respectively ) consist of adjacent motor pools that innervate the dorsal and ventral limb , respectively [37]–[39] . Unique combinations of transcription factors mark the various motor pools ( Figure S3A ) [38] , [40]–[43] . We excluded the possibility that the specificity of the innervation defect in the dorsal limb is due to restricted Ark2C expression only in the lateral LMC because Ark2C ( β-gal ) was found to be present in both lateral and medial LMC pools ( expressing FoxP1 ) in E13 . 5 Ark2C+/− brachial spinal cord ( Figure 6A ) . Furthermore , we examined whether loss of Ark2C either specifically causes loss of LMCl neurons or causes misspecification to an LMCm identity . The motor pools were examined at E13 . 5 ( n = 13 embryos from each genotype; Figure 6B and C and Figure S3B and C ) as there is a substantial loss of dorsal innervation at this age ( Figure 4F ) . LMCm cells co-express FoxP1 and Isl1 , while LMCl cells express FoxP1 and high levels of Hb9 in their nuclei . The number of cells in these domains was found to be the same in wt and Ark2C−/− embryos ( Figure 6C; 47 . 8% compared to 47 . 6% , p = 0 . 93 and 51 . 9% compared to 52 . 6% , p = 0 . 89 , respectively ) . Therefore , dorsal-ventral MN misspecification cannot account for the substantial loss of radial nerve projections to the forelimb in Ark2C−/− embryos . At E11 . 5 when the limb innervation defect is initiated in Ark2C−/− embryos , MN have not formed synapses with other distant neurons [44] , [45] , implying that Ark2C functions either within the LMC itself or in adjacent cells . Secretion of a signal by adjacent cells that is required to a greater degree by LMCl than LMCm neurons would allow these cells to indirectly affect predominantly the LMCl axonal projection in the limb . Alternatively , as the defect becomes more localized to dorsal innervation between E11 . 5 and 13 . 5 , Ark2C may be required within the LMC neurons for the interpretation of a guidance signal from the periphery . This putative signal could be unevenly distributed between the dorsal and ventral limb , creating a greater requirement for Ark2C in LMCl than LMCm . Both hypotheses implicate a signal involved in axon projection . The above analysis of the Ark2C−/− forelimb innervation defect suggests that Ark2C regulates either the production of a signal by spinal cord cells adjacent to MN , or the response of MN to a signal from the periphery . In both cases , the signal promotes or sustains axon extension . To find the signaling pathway that is affected by Ark2C , we examined its homology to other proteins . The C-terminal domain of Ark2C contains its most conserved domain , the RING , which is highly homologous to that of Arkadia ( Figure 1B ) . Arkadia enhances the responses downstream of the Nodal-TGF-β pathway , transduced by the Smad2/3 effectors by mediating the ubiquitin/proteasome degradation of negative regulators of the pathway [33] , [35] . To assess the similarity of Ark2C to Arkadia we used Arkadia-null mouse ES cells and embryonic fibroblasts to measure transcriptional activation of a Smad2/3-dependent luciferase reporter by Ark2C . In these null cells , expression of Arkadia ( GFP tagged; GArk ) can restore high levels of Smad2/3-dependent transcription , but Ark2C ( GFP tagged; GAkd2C ) cannot ( Figure 7A and unpublished data ) . Moreover , Ark2C cannot enhance the Smad2/3 reporter activity in a neuronal context after electroporation in the embryonic chick spinal cord ( Figure 7B ) . However , Ark2C enhanced by 2-fold the activity of a BMP-Smad1/5/8 responsive ( BRE ) luciferase reporter in chick spinal cord ( Figure 7C and D ) . As an enzymatically inactive mutant form of Ark2C ( GΔRING ) did not increase BRE-luciferase activity above the level of the control GFP ( Figure 7D ) , we concluded that this enhancement depends on the ligase activity of the Ark2C RING domain . Furthermore , Arkadia ( GArk ) could not activate the BRE-luciferase reporter even in the presence of BMP4 ( Figure 7E ) , confirming that Arkadia enhances specifically the Smad2/3 branch of TGF-β signaling . Therefore , Ark2C not only shares homology with the Arkadia RING domain but also functions in a similar way . However , while Arkadia enhances the Smad2/3 transcriptional responses , Ark2C specifically enhances Smad1/5/8 via its RING/ubiquitin ligase domain . Given that the chick spinal cord expresses endogenous Ark2C ( unpublished data ) , the 2-fold enhancement that we observed upon overexpression is significant . Furthermore , Smad1/5/8 effectors regulate several endogenous target genes downstream of BMP ligands , suggesting that the sum of transcriptional enhancement by Ark2C could have a phenotypic impact . To investigate Ark2C molecular function , we first studied the Smad1/5/8 activation profile under BMP4 stimulation . For this we used HEK293T cells ( 293T ) that do not normally express endogenous Ark2C , and 293T stably expressing low levels of GArk2C or transiently transfected with FLAG-Ark2N or FLAG-Ark2C ( Figure 8A and B and Figure S4A and B ) . Smad1/5/8 were activated earlier in the presence of GArk2C or FLAG-Ark2C but not with FLAG-Ark2N ( Figure S4A and B ) , indicating that faster effector phosphorylation is a property of Ark2C . Arkadia mediates the ubiquitin/proteasome degradation of inhibitory Smad6/7 [31]; we therefore examined the levels of these negative regulators in the above experiment during BMP4 stimulation . At 1 hour , there was a reduction in the levels of Smad6/7 in the presence of Ark2C ( Figure 8C and D and Figure S4C and D ) , suggesting that these inhibitory Smads are also substrates of Ark2C . Therefore , the two Arkadia proteins act in a similar manner . Smad6/7 inhibits signaling by mediating the degradation of the receptors and by interfering with the phosphorylation of pSmad1/5/8 . Although Ark2C degrades Smad6/7 ( Figure 8C and 8D ) , its presence does not have a profound effect on the steady state of pSmad1/5/8 levels ( Figure S4G and S4H ) . The initial high levels of pSmad1/5/8 in the presence of Ark2C are not maintained most likely due to an autoregulatory transcriptional loop in which Smad6/7 are up-regulated by pSmads [46] . According to this model , when pSmads levels are increased Smad6/7 levels are also up-regulated , leading to a reduction of receptors and Smad1/5/8 phosphorylation . It appears that this feedback loop is capable of correcting intracellular signal fluctuations and absorbs the initial effect caused by the presence of Ark2C . Therefore , the reduction of Smad6/7 by Ark2C at the initial phase of signal activation is unlikely to be the major underlying cause of the null phenotype and cannot account for the 2-fold transcriptional enhancement that we observe 24 h after electroporation of Ark2C in chick spinal cord ( Figure 7C and 7D ) . The co-repressors Ski/SnoN are degraded by Arkadia , when they are in a complex with pSmad2/3 [32] , [33] , [44] . We therefore examined if they are also degraded by Ark2C . During the first 2 h under BMP4 stimulation , the protein levels of the co-repressor Ski were found to be reduced ( Figure 8E and 8F ) in a reproducible manner ( Figure S4E and S4F ) . The levels of the Ski-like factor , SnoN , remained unchanged ( Figure 8E ) , suggesting that in the 293T cell context Ark2C shows a preference for Ski , which is consistent with similar data on Arkadia [47] . The co-repressors Ski/SnoN form complexes with pSmads on the promoters and repress transcription by recruiting histone deacetylases . A simple reduction in the overall levels of nuclear Ski/SnoN cannot reverse this repressive mark from the promoters , and physical removal of these repressed complexes is required . Recurrent clearance of the promoters from this repression allows fresh and unrepressed pSmads to bind and recruit p300 and other co-activators to re-initiate transcription throughout signal stimulation ( Figure 8G ) . Ark2C and Arkadia appear to do exactly this: degrade Ski/SnoN repressors specifically when they are interacting with and repressing pSmads . This function results in enhancement of the transcriptional responses from pSmad target gene promoters in the presence of Arkadia proteins and can account for a continuous requirement of Ark2C and most likely for the 2-fold enhancement of the BRE-reporter transcription observed 24 h after electroporation of Ark2C in chick spinal cord ( Figure 7C and 7D ) . The amount of Smad6/7 degraded by Ark2C appears low because there is rapid protein recovery achieved due to the resultant increase of signaling by pSmads , which transcriptionally up-regulate Smad6/7 in an autoregulatory loop . The reduction of Ski protein also appears small as Ski degradation by Ark2C is restricted to the fraction that is actively repressing pSmads potentially when they are associated with the promoters of target genes . However , the removal of this likely quite small fraction of total Ski is expected to cause profound derepression of promoters and enhancement of gene transcription . We investigated how directly Ark2C interacts with the above candidate substrates using immunoprecipitation ( IP ) with GFP antibody in HEK293T cells stably expressing low levels of GArk2C . These experiments showed that Ark2C interacts with endogenous pSmad1/5/8 ( Figure 9A ) , with the repressors SnoN and Ski ( Figure 9A ) , and with the inhibitory Smad6/7 ( Figure 9B ) , but not with endogenous pSmad2/3 ( Figure S5A ) . Interestingly the overexpression of either SnoN or Ski increased the fraction of endogenous pSmad1/5/8 interacting with Ark2C ( Figure 9A ) , suggesting that Ark2C interacts with complexes between repressors and effectors rather than the individual proteins . Furthermore , the presence of Ark2C , and not that of Ark2N or a RING-less version of Ark2C , increased polyubiquitination of the above proteins ( Figure 9C–F ) , implying that these are the substrates of Ark2C ubiquitin ligase activity . Collectively the above results confirm that the substrates of Arkadia and Ark2C comprise the same set of negative regulators , but that Ark2C mediates their degradation in the presence of pSmad1/5/8 . We next visualized the interaction of Ark2C with endogenous protein substrates within the cell using proximity ligation assay ( PLA ) , a technique that detects protein–protein interactions in situ . This showed that Ark2C ( FLAG-Ark2C ) is in close proximity with endogenous pSmad1/5/8 and Ski in the nucleus of 293T cells in the presence of BMP4 stimulation , while there is no formation of complexes in the presence of BMP inhibitor ( Figure S5B ) . Furthermore , the above molecular interactions were confirmed by PLA in the MN-like environment of the cell line NSC-34 [48] , where endogenous Ark2C is expressed ( Figure S6A ) . Transfected GArk2C interacts with endogenous pSmad1/5/8 ( Figure 10A ) and also with Ski ( Figure 10B ) only in the presence of BMP4 stimulation . Collectively , the above data position Ark2C specifically downstream of the BMP-Smad1/5/8 branch of TGF-β signaling and are consistent with the notion that it functions by derepression and ubiquitin-mediated degradation of negative regulators of the pathway . To address the role of BMP in MN axon advancement we first used NSC-34 cells [49] , which share many of the morphological and physiological properties of motor neurons; within 24 h under 1% Fetal Bovine Serum ( FBS ) , NSC-34 cells extend axons and express MN markers ( Figure S6B ) . NSC-34 respond to BMP treatment , as shown by immunoblot ( IB ) for pSmad1/5/8 and PLA for pSmad1/5/8–Smad4 complexes , which form only in the presence of BMP stimulation ( Figure 11A and B ) . We assayed the effect of BMP4 or inhibitor on axon growth and elongation by measuring axon length after 48 and 72 h of treatment . At 48 h the number of cells with long axons ( >65 µm from the center of the cell; method shown in Figure S6D ) was not significantly different between treatments ( BMP:150/400 and Inh:189/419 , Figure S6C ) . However at 72 h ( Figure 11C–D ) , the number of cells with long axons was reduced by 3 . 4-fold on treatment with inhibitor compared to BMP4 or by 2 . 4-fold when compared to the untreated control ( Inh , 67/422; BMP4 , 227/421; Untr , 157/405 difference between treatments , p<0 . 01 ) . The axon growth in the “untreated” cells is likely due to the fact that all cells were maintained in 1% FBS , which normally contains BMP ligands and activates pSmad1/5/8 to a small degree ( unpublished data ) . The delayed effect ( 3 d ) of the inhibitor on axon growth suggests that BMP signaling in NSC-34 cells is required for motor axon elongation rather than MN differentiation or initiation of axon growth , and that it involves nuclear transcription and Smads . The experiment was repeated with primary MN from HB9-GFP embryos ( E13 . 5 ) and showed also that BMP enhances , while inhibitor delays , motor axon advancement in culture ( Figure 11E and F ) . It should be stated that the primary MN were allowed to initiate axon growth before being subjected to the treatments so that we could assess the effect on axon elongation and not on axon regrowth . To address whether BMP signaling also plays a role in motor axon elongation in vivo , we examined signaling activation in motor pools using PLA . PSmad1/5/8-Smad4 complexes were detected in the nuclei of spinal cord roof plate cells , where BMP ligands are abundant , and also in the brachial ventral horn containing the LMC ( Figure 11G; series of spinal cord sections in n = 3 E11 , E12 , and E13 embryos ) . Co-staining for pSmad1/5/8 and FoxP1 showed that signaling occurs in all LMC neurons ( Figure 11H; multiple sections from n = 6 wt and 6 null embryos ) . There was no obvious reduction of pSmad1/5/8 in the LMC/FoxP1 domain of Ark2C−/− embryos ( Figure 11H ) , which is consistent with the molecular findings that Ark2C enhances the downstream transcriptional responses by derepressing promoters rather than by changing the steady state level of pSmads . Collectively the above results support an involvement of BMP-Smad signaling in MN axon projections . BMP ligands are abundant in the periphery ( i . e . , limb ) and the dorsal spinal cord but not in the motor pools [50] , [51] . Therefore , it is likely that the critical role of Ark2C is to enhance the response of ventral spinal cord cells ( i . e . , MN ) to BMP signals originating at a distance . Several BMP ligands are present in the developing limb [52]; however , their expression is dynamic , indicating a spatio-temporal variation in signal availability in the periphery ( Figure S7 ) . It is therefore possible that Ark2C functions within MN to maintain signaling responses when ligand availability or signaling ability fluctuates . We used genetics to test this hypothesis , taking advantage of the fact that loss of only one allele of Ark2C ( Ark2C+/− ) does not lead to any forelimb posture and movement defects . We examined how forelimb innervation is affected when we reduce BMP signaling genetically . BMP type II receptor ( BMPRII ) is an essential and unique core component of BMP signaling . BmprII null mice die early during gastrulation , but mice with one allele breed successfully and are normal [53] . Analysis of the offspring from crosses between Ark2C+/−;BmprII+/− and Ark2C+/− mice showed that fewer than expected Ark2C+/−;BmprII+/− double heterozygotes were born ( genotyped within 24 h after birth ) . However , all those born survived to adulthood ( Table S1A ) . MN development was analyzed via expression of the HB9-eGFP transgene in embryos from the same cross . This showed that single heterozygous mice ( Ark2C+/+;BmprII+/− ) have robust innervation of the dorsal limb , while a subset of E13 . 5 double heterozygotes ( n = 2 out of 6 examined ) exhibit innervation defects in the dorsal forelimb similar to the severely affected Ark2C−/− embryos ( Figure 12A ) . We also assessed limb weakness in Ark2C−/− mice and compound mutants by measuring the time that the mouse could suspend itself from a cage lid ( Figure 12D ) . This test requires repeated gripping and releasing of the bars and therefore unlike standard grip strength tests measures the function of both the extensor and flexor muscles . Ark2C−/− animals performed poorly in the test with mean hanging times of less than 15 s ( n = 8 ) compared to wt mice ( n = 7 ) , which have mean times close to 60 s , the end-point of the test ( Figure 12D ) . A subpopulation ( 12 . 5% of n = 16 ) of Ark2C+/−;BMPRII+/− mice had a performance of less than 30 s , suggesting that surviving double heterozygous mice exhibit weakness in forelimbs , albeit less severe than that observed in Ark2C−/− mice . Together the analysis in embryos and adult mice supports the hypothesis that the dorsal limb innervation defect is caused by a reduction in BMP signaling and that Ark2C enhancement plays a compensatory role to maintain high levels of the BMP-activated downstream response . As BMPRII is expressed both in neurons and muscle [54] , [55] , the above genetic interaction experiment does not reveal whether signaling reduction within MN or the muscle is responsible for the defective axon elongation in the transheterozygote forelimbs . In addition , BMPRII can signal independently of Smads [55]; therefore , it remained unclear whether the phenotype is Smad1/5/8 dependent . Smad1/5 are expressed broadly in the mouse embryo , including the spinal cord , but Smad8 expression is highly restricted and is nearly absent from the developing nervous system [56] . Smad8−/− mice survive and have been maintained by homozygous breeding [56] . Using this stock of Smad8−/− mice and Ark2C+/− we first generated Ark2C+/−;Smad8+/− double heterozygotes . These were then bred with Smad8−/− mice to generate Smad8−/− ( Smad8 null ) , Smad8+/− ( Smad8 het ) , Ark2C+/−;Smad8+/− ( double het ) , and Ark2C+/−;Smad8−/− ( het hom ) compound mutants . Analysis of the above offspring showed that Ark2C+/−;Smad8−/− mice are born at lower than expected numbers and exhibit reduced survival prior to weaning ( Table S1B ) . Up to 50% of these ( Ark2C+/−;Smad8−/− ) compound mutant adult animals exhibit forelimb movement and posture defects reminiscent of those observed in Ark2C−/− , but not Ark2C+/− mice ( Figure 4F–H ) . Assessment of limb weakness on the cage lid test showed that Ark2C+/−;Smad8−/− mice exhibit a wide range of performances with 55% of animals failing to reach 30 s ( n = 11 ) compared to 7% of littermate controls of mixed genotype ( n = 15 ) ( Figure 12D ) . Analysis of the forelimb muscles of poorly performing individuals revealed atrophy with unilateral penetrance in the muscles controlling the digits and wrist ( Figure S8C ) . These are the same muscles that were found to atrophy in a bilateral manner in Ark2C−/− postnatal stage animals ( Figure 3A ) . Furthermore , MN axon defects in the forelimbs were examined with the HB9-eGFP marker in the above genetic crosses . Thirty out of 52 embryos studied were either Smad8+/− , Smad8−/− or Ark2C+/−;Smad8+/− and did not exhibit innervation defects in any of their 60 forelimbs . The remaining 22 embryos were Ark2C+/−;Smad8−/− . Of these 44 forelimbs , 10 forelimbs exhibited a reduction or loss of the most distal innervation ( red and blue arrowheads in Figure 12B , and Figure S8A and B ) with five showing additional reduction in the innervation of more proximal muscles ( green arrowhead , Figure 12B and Figure S8A and B ) . Quantitation of the results is shown in Figure 12C . The levels of the BMP-Smad signaling response are most likely reduced in Ark2C+/− mice , but they remain above the threshold that is required for efficient advancement of motor axons . However , a reduction of one BMP-effector in these Ark2C+/− animals brings the signaling response below the threshold , thereby reproducing the dorsal forelimb innervation deficit otherwise observed only in Ark2C−/− individuals . The data support the hypothesis that Ark2C enhances BMP-Smad signaling responses in vivo and that this enhancement is required for MN axon elongation particularly in the dorsal forelimb . Smad8 expression is absent from the muscle and restricted in ventral horns of the brachial spinal cord ( Figure 12E ) , while Ark2C is expressed in the entire spinal cord ( Figure 1G–H ) . As both these factors are involved in controlling dorsal forelimb innervation , their interaction must take place within the spinal cord and specifically in the Smad8 expression domain . We therefore used a lacZ-reporter knocked in to the Smad8 locus [56] to examine the specificity of this expression in detail . This showed that Smad8 is expressed in a few cells of the ventral spinal cord at E12 . 5–15 . 5 ( Figure 12E and F and unpublished data ) . Motor pool marker analysis showed that the Smad8-lacZ positive neurons are present within the LMC ( FoxP1 domain in Figure 12F ) . These must be the MN that require Ark2C and Smad8 for their axonal projection . The spinal cord has been shown to express broadly both Smad1 and Smad5 [56]; therefore , the activation of the third effector Smad8 in a subset of MN suggests a special requirement for higher BMP signaling responses in these cells . Collectively , the above genetic experiments show that Ark2C function is to enhance BMP-Smad signaling within MN and that it is essential for the efficient advancement of motor axons in the dorsal forelimb .
Motor axons are amongst the longest in the body and both intrinsic and extrinsic factors have been shown to play a role in their elongation . However , a full understanding of the many signaling pathways that affect the process has not been achieved . In this study we present a collection of evidence supporting that Ark2C and BMP-Smad signaling are involved in motor axon elongation during development . Ark2C is shown to enhance BMP-Smad signaling responses by gain of function experiments in the chick spinal cord in vivo ( Figure 7 ) , and a mechanism whereby Ark2C derepresses the pathway by mediating the degradation of intracellular repressors is described ( Figures 8 and 9 ) . Genetic interaction experiments confirm that reduction of Ark2C in vivo along with BMP signaling components recapitulates forelimb innervation deficits otherwise observed only in the complete absence of Ark2C in mice ( Figure 12 ) . The proposed role of Ark2C in motor axon projection during development is supported by analysis of Ark2C−/− embryos , showing phenotypes that range from severe reduction of the innervation in the dorsal forelimb to the shortening of presynaptic branches as observed in the phrenic ( Figures 4 and 5 ) . Additionally , active BMP-Smad signaling is present in the brachial ventral spinal cord including the LMC ( Figure 11 ) and treatment of cultured NSC-34 or primary MN with BMP-inhibitor diminishes axon elongation ( Figure 11 ) . Although the presence of pSmad1/5/8 is high in DRG during development and recent findings support a role of Smad-dependent BMP signaling in sensory neuron axon regeneration in vivo and in culture [16] , [17] , the role of this pathway in axon growth in the periphery during development and neuromuscular connectivity remained largely unknown . Moreover , it has been reported that MN do not harbor activated BMP-Smads , and conditional deletion of one of the four BMP type I receptors , BmprIa , revealed that while it has an essential role in limb mesenchyme patterning , it is not required in MN [57] . These experiments do not exclude a role of BMP in motor neurons acting via a different type I receptor . Our finding that phosphorylated Smad1/5/8 are present in motor pool nuclei is based on both immunostaining with a-pSmad1/5/8 ( Figure 11H ) and PLA , a technique that detects protein interactions ( Figure 11G ) . The latter technique was adapted specifically to detect pSmad1/5/8 and Smad4 complexes that form only upon ligand stimulation [58] . These results confirmed that pSmad1/5/8 are present in motor pool nuclei including the entire LMC and reveal the likely involvement of canonical BMP signaling in MN . As Ark2C functions to boost signaling intracellularly , its loss is not expected to abolish BMP signaling but instead exposes which cells ( MN ) require an enhancement of BMP-Smad signaling activity to reach the desired cellular outcome . Despite the expression of Ark2C throughout the spinal cord , the phenotype produced upon its loss indicates that it is required in only a subset of MN ( predominantly in the LMCl ) for neuromuscular connectivity ( Figure 4 ) . This suggests that in other MN , BMP signaling is sufficiently high that the desired threshold is reached without intracellular enhancement by Ark2C . The requirement of intracellular boosting of signaling in a subset of LMC neurons is supported by the observed expression of Smad8 in a subpopulation of the LMC in addition to Smad1 and 5 , which are widely expressed [51] , [56] . When Smad8 expression is lost along with one allele of Ark2C in mice , LMCl axons exhibit inefficient projections to their most distal target muscles of the forelimb ( Figure 12B and C ) . Together , these observations propose a hypothesis that the requirement for BMP-Smad signaling in MN axon elongation is broad , but a subset of neurons cannot reach the vital threshold by ligand stimulation alone . Instead , these cells require intracellular enhancement of the pathway , achieved by the presence of the third effector Smad ( Smad8 ) and by a dependency upon Ark2C . The specificity of the requirement for BMP-Smad intracellular enhancement in the dorsal forelimb compartment may be due to fluctuations in BMP ligand availability or in the activation of BMP signaling antagonists . Measurements of the diameter of the dorsal and ventral nerve trajectories into the forelimb at E11 . 5 ( Figure 4A–D and Figure S2A–F ) and 3D projections of innervation to and from the brachial plexus ( Figure 4E ) do not show evidence of misguidance in Ark2C null mutants . Additionally , there is no evidence of major misspecification of LMCl motor neurons to an LMCm identity to account for the severe reduction of LMCl projections in the absence of Ark2C ( Figure 6 and Figure S3 ) . Furthermore , in the absence of Ark2C the dorsal forelimb innervation deficit is not focused only on one muscle group involving a specific LMCl subpopulation . Instead it alters during development and from E13 . 5 onwards affects predominantly dorsal muscles to a varying degree dependent upon their proximal-distal location ( Figure 4F–H ) . As misrouting and misspecification cannot account for the phenotype in the Ark2C mutants , reduced axon growth is the most plausible explanation . Several lines of evidence including biochemistry ( Figures 8 and 9 ) , chick spinal cord functional assays ( Figure 7 ) , and genetics ( Figure 12 ) support that Ark2C functions via the BMP-Smad pathway and that this signaling is activated in motor neurons ( Figure 11 ) . The above evidence together with the finding that treatment of MN in culture with BMP4 or an inhibitor results in positive or negative effects on axon growth , respectively ( Figure 11C–F ) , suggest that Ark2C-enhanced BMP signaling is involved in axon growth . BMP ligands are abundant in additional peripheral synaptic targets other than the limbs and have already been shown to activate Smads within sympathetic and trigeminal sensory neurons in a retrograde manner [18] , [19] , [59] . However , in mammalian MN , this has not been observed . Our findings support the possibility that BMP-Smad stimulation of cells within the LMC also originates from ligands in the periphery . In the case of such retrograde activation , axons projecting to distinct limb compartments would be exposed to variable ligand stimulation . During development several BMP ligands and their antagonists are expressed with dynamic patterns within the limbs as they regulate limb growth and patterning ( and Figure S7 ) [60] , [61] . This changing expression pattern may explain the differences in phenotypic severity observed over time ( Figure 4 ) but might also contribute to the initial requirement for Ark2C-mediated signaling enhancement . Prolonged exposure of MN growth cones and synapses to BMP ligands is expected to activate intracellular negative feedback mechanisms [26] , [29] that lower the downstream responses , in this case axon extension . Additionally , the peripheral tissue that produces the ligand is expected to activate extracellular antagonists as part of a negative feedback mechanism [23] , limiting the amount of BMP available to the growth cones and axons . Under these conditions the presence of Ark2C in MN could derepress BMP signaling and counteract certain negative feedback mechanisms . In this manner the presence of Ark2C in all MN safeguards the levels of BMP signaling responses maintaining the growth of motor axons through an environment with variable and dynamic ligand stimulation . Minor variations in the amount of ligand present in the limb or activation of negative feedback between individual embryos may also explain the variability of the axon extension defects observed in congenic strains of Ark2C−/− embryos or between innervation in different limbs within the same embryo . In Drosophila , retrograde BMP Smad-dependent signaling is required for synaptic growth and plasticity in MN [20]–[22] . However , BMP signaling is not essential for specification , initial axon elongation , or synaptogenesis . The phenotype seen upon reduction of BMP signaling in Drosophila MN is associated with the expansion and plasticity of the synapse . This shares aspects of the terminal branch elongation defect observed in the Ark2C−/− phrenic nerve ( Figure 5A–C ) . It is therefore tempting to speculate that BMP signaling is involved in axon and presynaptic lengthening along with synapse expansion in both organisms . We have revealed that BMP-Smad signaling is involved in MN axon elongation and identified Ark2C as a positive regulator of the pathway participating in this process . Our research is expected to focus future studies on determining whether BMP signaling is involved in MN axon plasticity or degeneration/regeneration and whether it can be used to modulate these events to prevent disease . Future studies could also address how broad the role for BMP signaling is in developmental axon elongation and whether it might underlie disorders associated with neuromuscular connectivity .
Mice carrying the gene-trap in Ark2C gene were generated from the P9-3f ES cells of the International Gene Trap Consortium ( http://www . genetrap . org/; Soriano Lab Gene Trap Database ) . Ark2C mutant mice were crossed with lines carrying Hb9-eGFP [62] ( gift from K . V . Anderson , Memorial Sloan-Kettering , NY ) , Smad8 LacZ [56] ( gift from E . J . Robertson , University of Oxford ) , and mutated BMP type II receptor [53] ( gifts from K . Miyazono , University of Tokyo ) . Genotyping was carried out using PCR , as described for Smad8 and BMPRII lines [53] , [56] , and for Ark2C , with primers: F , 5′-GCTGGGTGCTGTCCTAGAAG-3′; R ( wt ) , 5′-CCGGGGTATATGCAATTCTG-3′; R ( mut ) , 5′-ACTGGAAAGACCGCGAAGAG-3′; and the following conditions: 94°C for 5 min , then 39 cycles of 94°C for 30 s , 58°C for 60 s , 72°C for 90 s , and 72°C extension for 5 min . DNA was separated on 1 . 5% agarose gels resulting in bands at 320 bp ( wt ) and 575 bp ( null allele ) . Tagged Ark2C constructs were generated by fusing full-length mouse Ark2C or amino acids 68–346 of human Ark2C in frame with GFP ( pEGFP-cI; Clontech ) . The GFP-Ark2C RING deletion mutant was constructed by deleting the last 52 amino acids , which include both zinc fingers , from the human Ark2C . The various tagged Ark2C sequences were subcloned into pTriEx2-hygro ( Novagen ) at the SmaI site ( GFP , GFP-mArk2C ) or the NcoI site ( GFP-hArk2C ) . The following reporter constructs were used in in ovo luciferase assay: 12×-CAGA-Lux , BRE-lux , and pRL-SV40 . The expression vectors for mycSnoN , mycSki , FLAG-Ub , mycSmad4 , mycSmad6 , and mycSmad7 are described previously [31] , [32] . The expression vectors pCDEF-FLAG-Ark2N and pCDEF-FLAG-Ark2C are gifts from K . Miyazono . Human embryonic kidney cells ( 293T ) , NSC-34 , and Arkadia-null mouse embryonic fibroblasts were cultured in Dulbecco's Modified Eagle's Medium supplemented with 10% fetal bovine serum , L-glutamine , and penicillin/streptomycin at 37°C in a 5% CO2 atmosphere . Transient transfections were performed using Lipofectamine 2000 according to the manufacturer's instructions . Total RNA was extracted and purified using the RNeasy Mini kit with on-column DNaseI treatment ( Qiagen ) . Reverse transcription reactions were carried out using the Superscript III First Strand Synthesis System for RT-PCR ( Invitrogen ) . Primers used are shown in Table S2 , and reactions were normalized against GAPDH or YWHAZ . Whole-mount tissues and 100 µm vibratome sections were fixed in X-gal fix , washed in X-gal rinse , and stained as described [34] . Post X-gal stain immunocytochemistry was carried out using Ultra Sensitive ABC Peroxidase Kit and Metal Enhanced DAB Substrate ( Thermo Scientific ) . Constructs were electroporated into the neural tube of HH stage 11–12 chick embryos . The renilla luciferase was used at 0 . 1 µg/µl , luciferase reporter and test DNA at 1 µg/µl each . When exogenous ligand was added to the DNA mix , it made up 1/10 of the test DNA . The spinal cord was harvested after 22–24 h and luciferase activity measured using the Dual Luciferase Reporter Assay ( Promega ) [63] . The firefly/renilla luciferase ratio for each chick spinal cord or well of cells was calculated; graphs show mean for each population and the standard error of the mean . Two-tailed homoscedastic Student's t tests were used to calculate the probability of different populations being identical . Whole-mount in situ hybridization of Ark2C was carried out as described [40] , [64] using a 3′ UTR probe described in http://mouse . brain-map . org/brain/Gm96 . html . BMP probes were used as described [65]–[67] . Whole-mount antibody staining of mouse forelimb motor axons was performed as described [40] using a-GFP ( 1∶500 , Invitrogen , A11122 ) and a-myosin-32 ( 1∶500 , Sigma , M4276 ) . Dissections were carried out at different times of day to obtain intermediate stage embryos . GFP-labeled motor axons were imaged using a Leica SP5 confocal microscope , stacks were taken through the entire extent of the forelimb ( approximately 500 µm depth ) , and images stitched using Leica software . The 3D projections were created using ImageJ software and movies using VideoMach ( under the advice of Dirk Dorman microscopy laboratory , MRC CSC ) . Measurements of both major dorsal and ventral axon bundles originating from the brachial plexus and the spinal nerves were made at E11 . 5 using ImageJ . Width of spinal nerves was measured at the point of amalgamation with another nerve ( Figure S2A and B ) , and the area of the spinal nerves and brachial plexus was measured as shown in Figure S2A . Length measurements of nerves within the limb were made following the approximate trajectory of axons from a line drawn across the brachial plexus made using landmarks to ensure consistent positioning . Width of the axon bundle was measured at three points along its length , avoiding regions proximal to the plexus , branch points , or growth cones where the bundle was wider . Volumetric measurements of the brachial plexus were obtained using Imaris software . Whole-mount staining of diaphragms was carried out as described [68] using a-neurofilament ( 1∶1 , 000 , Chemicon , AB1981 ) , a-synaptophysin ( 1∶100 , Zymed/Invitrogen , 18-0130 ) , and Alexa Fluro 488 α-Bungarotoxin ( 1∶250 , Invitrogen , B13422 ) . Image stacks were taken at a ventral location within the diaphragm muscle to prevent secondary nerve branches complicating measurements . Stacks were flattened and the number of synapses present and length of phrenic nerve within the image were measured . Fifteen synapses towards the outer edge of the endplate were chosen at random and their terminal branches were traced using Neuron J ( 1 wt and 2 Akd2C−/− diaphragms at E17 . 5 , 2 wt and 2 Akd2C−/− diaphragms at P17 ) . Antibody staining was performed on 14 µm cryosections using guinea pig a-FoxP1 ( 1∶16 , 000 ) , rabbit a-FoxP1 ( 1∶32 , 000 ) , guinea pig a-Hb9 ( 1∶16 , 000 ) , rabbit a-Isl1 ( 1∶4 , 000 , all gifts from T . Jessell ) , rabbit a-Pea3 ( 1∶5 , 000 , gift from S . Arber ) , a-pSmad1/5/8 ( 1∶1 , 000 , Cell Signaling , 9511 ) , and rabbit a-β-galactosidase ( 1 µg/ml , MP Biomedicals , 08559762 ) . Multiple sections from 13 embryos of each genotype were analyzed ( in total 2 , 042 wt and 2 , 452 Akd2C−/− FoxP1 expressing cells were counted , average of 51 . 16 and 49 . 32 cells per section ) , and the mean percentage of Foxp1 positive cells expressing a second gene was calculated . The forepaws of the mouse were painted with black food colouring and the mouse walked on paper for 30 cm . Animals with abnormal trails were tested at least twice . The resulting pawprints were then analysed using some of the parameters described in [69] . Resting adult mice were warmed at 39°C for 15 min and 100 µl of blood were taken from the tip of the tail . Blood plasma was separated by centrifugation for 15 min at 4 , 000 rpm . Lactate levels in the plasma were measured using a Lactate Colorimetric Assay Kit ( Abcam ) . A mouse was placed on the wire cage-lid , and the lid was inverted and held over the cage at about 25 cm . The time before the mouse fell was measured with a cut-off time of 60 s [70] . Two tests were carried out 20 min apart; this was repeated at weekly intervals at least three times . To check for increasing phenotype severity as the animals aged , the protocol was carried out weekly for 4 months . For the IP assays , cells were treated with 2 µM dorsomorphin ( DM; a selective inhibitor of BMP type I receptors; Merck ) to achieve BMP inhibition , 25 ng/ml BMP4 for stimulation ( R&D ) , 20 µM SB431542 ( S4317 ) to inhibit TGF-β signaling , or 10 ng/ml Activin A ( A4941 , Sigma ) for stimulation . All treatments were done in serum-free medium for 1 h in the presence of 20–50 µM of the proteasome inhibitor MG132 ( Sigma ) . After the treatments , cells were lysed in lysis buffer ( 20 mM Tris−HCl , pH 7 . 5 , 150 mM NaCl , 10% glycerol , and 1% Triton X-100 ) supplemented with protease and phosphatase inhibitors ( Roche ) , and 50 µM MG132 . Cell lysates were incubated with an a-GFP antibody ( Roche , 11814460001 ) and dynabeads ( Invitrogen ) for 4 h at 4°C . After extensive washes with lysis buffer , the proteins were eluted with sample buffer and analyzed by IB . For the protein kinetics , cells were treated with 2 µM DM or 25 ng/ml BMP4 in serum-free medium or left untreated in medium containing 10% FBS . The cells were lysed in the above lysis buffer and the samples were analyzed by IB . Quantitation was performed with the ImageJ software . The antibodies used in the IB are a-pSmad1/5/8 ( 1∶500 ) , a-Smad1/5/8 ( 1∶500 , Santa Cruz , sc-6031-R ) , a-pSmad2 ( 1∶500 , Cell Signaling , 3101 ) , a-Smad6/7 ( 1∶1 , 000 , Santa Cruz , sc-7004 ) , a-Ski ( 1∶3 , 000 , Millipore ) , a-SnoN ( 1∶5 , 000 , Santa Cruz , sc-9141 ) , a-PCNA ( 1∶10 , 000 , Chemicon , MAB424 ) , a-myc ( 1∶500 , Sigma , M5546 ) , a-FLAG ( 1∶1 , 000 , Sigma , F1804 ) , and a-GFP ( 1∶1 , 000 , Invitrogen ) . Experiments were performed two to three times . Cells were treated with 20 µM MG132 for 4 h , harvested in PBS , and lysed in 1% SDS . Lysates were boiled for 5 min at 95°C and diluted with dissociation buffer ( 1% TritonX-100 , 0 . 5% sodium deoxycholate , 120 mM NaCl , 50 mM Hepes pH 7 . 2 , supplemented with protease inhibitors and 50 µM MG132 ) to a final SDS concentration of 0 . 1% . The cleared lysates were incubated with a-myc antibody ( Santa Cruz , sc-789 ) and dynabeads ( Invitrogen ) for 4 h at 4°C . Bound proteins were washed extensively with dissociation buffer and eluted in sample buffer . The bound proteins as well as the total lysates ( TLs ) were analyzed by IB using a-FLAG and a-myc antibodies ( Sigma ) . Experiments were performed twice and representative images are shown in Figure 7C–F . In situ proximity ligation was performed using the Duolink or Duolink II kit according to the manufacturer's instructions ( Olink Biosciences ) and as described [58] . E13 . 5 mouse embryos were dissected , fixed in 4% PFA for 2 h on ice , and embedded in 4% agar . We prepared 100 µm vibratome sections from the brachial level . The cells and the vibratome sections were permeabilized with PBS-TritonX-100 0 . 5% and then incubated with blocking solution ( Olink Biosciences ) . The samples were incubated with the primary antibodies [a-pSmad1/5/8 ( 1∶100 ) , a-Ski ( 1∶100 ) , a-FLAG ( 1∶100 ) , and a-Smad4 ( 1∶100 , Santa Cruz , sc-7966 ) ] for 1 h at 37°C . The secondary probes used were Duolink anti-Rabbit PLUS ( 90302 ) and Duolink anti-Mouse MINUS ( 90301 ) or Duolink II PLA probe anti-Rabbit PLUS ( 92002 ) and Duolink II PLA probe anti-Mouse MINUS ( 92004 ) . The detection reagents used were Duolink detection kit 563 ( 90104 ) or Duolink II Orange ( 92007 ) , and images were acquired using a Leica SP5 confocal microscope . The quantification of PLA signal was performed using the ImageTool software ( Olink Biosciences ) . NSC-34 cells were differentiated as described [71] on coverslips coated with laminin ( Sigma ) and lysine ( Sigma ) . The cells were cultured in DMEM supplemented with 1% FBS , 1% FBS , and 2 µM dorsomorphin or 1% FBS and 25 ng/ml BMP4 for 48 or 72 h . The concentration of dorsomorphin was chosen after titration ( 0 . 5–5 µM ) to avoid toxicity . The cells were stained with a-Neurofilament ( 1∶1 , 000 ) , and images were acquired using a Nikon Eclipse E1000M microscope at 4× and 20× magnification . Neurite outgrowth was measured as the percentage of cells that have axons grown more than 65 µm from the center of the cell body ( Figure S5D ) to the total number of cells for each condition . Axons were measured from at least five images of six different experiments ( 400–425 cells in total ) . Two-tailed homoscedastic Student's t tests were used to calculate the probability of different populations being identical . Primary MN were isolated from Hb9-eGFP mouse embryos at E13 . 5 and purified by density centrifugation according to a modified protocol by Mazarakis and Schiavo based on published protocols [72] , [73] . The cells were plated on Permanox chamber slides ( LabTek ) coated with poly-L-ornithine and laminin ( Sigma ) . MN were maintained in Neurobasal medium ( Invitrogen ) supplemented with GDNF ( 10 µg/ml ) , CTNF ( 10 µg/ml ) , and BDNF ( 10 µg/ml ) ( Alomone Labs ) . After an overnight incubation the cells were treated for 72 h with 2 µM dorsomorphin or 25 ng/ml BMP4 and then fixed with 4% PFA and stained with a-GFP ( Roche ) . GFP-labeled motor axons were imaged using a Leica SP5 confocal microscope , and their length was measured using the NeuronJ plugin of ImageJ . Two-tailed homoscedastic Student's t tests were used to calculate the probability of different populations being identical . GenBank ( http://www . ncbi . nih . gov/Genbank/ ) accession numbers for the genes discussed in this paper are Bmpr1a ( NM_009758 ) . | Motor neurons control movement via long axons that extend from the spinal cord to muscles as far as in distant limbs . Little is known about factors that regulate this extensive axonal growth in the periphery . Here we report that the ubiquitin ligase Ark2C ( Arkadia2 ) is expressed in neurons and can serve to amplify neuronal responses to specific signals . We find that these signals belong to the Bone Morphogenetic Protein ( BMP ) family of secreted factors , which are highly expressed in the periphery and known to regulate the development of the limbs . Loss of Ark2C gene function in mice results in inefficient growth of motor axons to distant muscles , and we show that this process is regulated by BMP signaling . Ark2C targets BMP inhibitors for destruction , and therefore the presence of Ark2C helps to enhance BMP signaling , which in turn is necessary for the innervation of distal muscles . Our experiments reveal a previously unknown function of BMP in motor axon growth and describe a molecular mechanism for how axons and limbs coordinate their growth . | [
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] | 2013 | Rnf165/Ark2C Enhances BMP-Smad Signaling to Mediate Motor Axon Extension |
In the European Union ( EU ) , animal welfare is seen as a matter of great importance . However , with respect to animal experimentation , European citizens feel quite uninformed . The European Directive 2010/63/EU for the protection of laboratory animals aims for greater transparency and requires that a comprehensible , nontechnical summary ( NTS ) of each authorised research project involving animals is published by the respective Member State . However , the NTSs remain sleeping beauties if their contents are not easily and systematically accessible . The German web-based NTS database AnimalTestInfo is a unique channel for scientists to communicate their work , and provides the opportunity for large-scale analyses of planned animal studies to inform researchers and the public . For an in-depth meta-analysis , we classified the duly completed NTSs submitted to AnimalTestInfo in 2014 and 2015 according to the International Classification of Diseases and Related Health Problems ( ICD ) system . Indexing the NTSs with ICD codes provided a fine-grained overview of the prospective uses of experimental animals . Using this approach , transparency , especially for highly controversial animal research involving , for example , nonhuman primates , is fostered , as it enables pinpointing the envisaged beneficiary down to the level of the addressed disease . Moreover , research areas with many planned projects involving animals can be specified in detail . The development of 3R ( replacement , reduction , and refinement ) measures in these research areas may be most efficient , as a large number of experimental animals would benefit from it . Indexing NTSs with ICD codes can support governments and funding agencies in advancing target-oriented funding of 3R research . Data drawn from NTSs can provide a basis for the development , validation , and implementation of directed 3R strategies as well as guidance for rethinking the role of animal research models .
Many citizens , particularly in Europe , see animal welfare as a matter of great importance [1] . At the same time , a majority of Europeans do not feel well informed with respect to animal experimentation [2 , 3] . Thus , more detailed information on the purposes of animal experiments and their harms is needed to effectually inform the public . Aimed at improving transparency , the European Directive 2010/63/EU for the protection of laboratory animals [4] requires that researchers provide a nontechnical summary ( NTS ) for each proposed project , including information on its objectives and potential benefits , expected harm , number of animals , species , and a demonstration of compliance with the requirements of the 3R principle ( replace , reduce , and refine ) [5] . Competent authorities ensure that the contents of the NTS are accurate and correspond to the project application . Animal numbers provided in NTSs are approximate estimates of the numbers of animals expected to be used over a maximum period of 5 years . However , as part of the project application , researchers have to ascertain , on a statistically sound basis , that the minimum number of animals necessary to obtain reliable results is used ( Article 22 Directive 2010/63/EU ) . Anonymised NTSs of all authorised projects must be published by each Member State of the EU to inform the public about research involving animal experiments without jeopardizing intellectual property and confidential information regarding researchers and institutions . In Germany , the Federal Institute for Risk Assessment ( BfR ) has been tasked with publishing NTSs in an appropriate format . The BfR has decided to make NTSs available in a searchable web-based database , AnimalTestInfo [6] , so that anyone can easily access information about planned and authorised projects involving animals . To our knowledge , this is the first web-based NTS database , and it is currently growing by 2 , 900 entries per year . Users can browse the database based on experimental purpose , species , number of animals used , year of publication , or any key word . However , the contents deposited in AnimalTestInfo offer the opportunity to generate more information for research and the public . Processing and analysing the structured information from the extensive and continuously growing number of NTSs allow us to take a bird’s eye view of research involving animal testing [7] and will contribute to evaluating and improving practices in the field of in vivo biomedical research . The first goal of our pilot study was to demonstrate that it is possible to comprehensively extract additional information embedded in the NTSs about the objectives and expected benefits of authorised projects using an objective classification system . The second goal was to evaluate whether this information can be used to identify research areas in which the development of directed 3R strategies would be most efficient . At present , valid strategies to specify areas of interest in need of 3R measures ( e . g . , areas in which many animals are used or severe experiments are performed ) and objective criteria to monitor their success are lacking . However , this is important information for researchers when rethinking 3R strategies and for third-party donors to fund 3R-relevant research . Currently , the recall of relevant information from AnimalTestInfo based on key word searches is highly variable , but a complete recall of relevant contents is essential to carry out systematic analyses . While metadata deposited in the database , such as animal numbers and species , can be retrieved in a quantitative manner , scientific details specified in free-text fields may be missed because of linguistic pitfalls . Classification-based searching is a standard solution to support the quantitative recall of contents , which is implemented in patent ( e . g . , Espacenet [8] ) and scientific ( e . g . , PubMed/Medline [9] ) databases . Therefore , in a first approach we classified duly completed NTSs submitted in 2014 ( 2 , 328 NTSs ) and 2015 ( 2 , 970 NTSs ) by assigning classification codes of the International Classification of Diseases and Related Health Problems ( ICD ) system established by the World Health Organization ( WHO ) [10] , based on the beneficiary/target population mentioned in the NTS . We chose the ICD , i . e . , the German modification ICD-10-GM-2016 , because a preliminary survey of the statements in the NTS fields ‘benefits’ indicated that the majority of projects address specific human diseases . In addition , by indexing with ICD-10 codes , information can be retrieved without the linguistic pitfalls of free-text sections . The ICD has several advantages: it is globally accepted , well thought out , and unambiguous assignment to different diseases is possible ( for further benefits of ICD-10 , see S2 Text ) . In addition , free online training tools and supporting tools for the actual classification procedure are available [11–13] . In the present study , we first assessed the percentage of NTSs indicating patients as beneficiaries and allocated ICD-10 codes according to S1 Table . The remaining NTSs were classified according to the indicated beneficiaries . To assess whether it is possible to identify research areas encompassing studies involving a high or low number of animals , we compared the average number of animals per NTS for 9 distinct research areas . As biomedical research involving nonhuman primates is highly controversial , we present the allocation of NTSs authorised in 2015 for the use of nonhuman primates to ICD-10 codes separately .
First , we determined the percentage of duly completed NTSs submitted in 2014 ( n = 2 , 328 ) and 2015 ( n = 2 , 970 ) that could be assigned an ICD-10-GM-2016 code . Therefore , statements in the fields ‘title’ and ‘benefits’ of each NTS were manually evaluated to determine the beneficiaries . We were able to assign roughly 4/5 of the NTSs , i . e . , 81% and 79% in 2014 and 2015 , respectively , to an ICD-10 code in the target group ‘patients’ ( Fig 1 ) . The remaining NTSs ( 19% and 21% , respectively ) were sorted according to their intended target , i . e . , ‘pure basic research’ ( 5% in 2014 and 7% in 2015 ) , ‘consumers’ ( 1% each ) , ‘farm and domestic animals’ ( 4% each ) , ‘laboratory animals’ ( 1% each ) , and ‘wild animals’ ( 2% and 3% , respectively ) , as visualised in Fig 1 ( see Materials and methods for definitions ) . A small percentage of NTSs ( 6% and 4% , respectively ) could not be assigned to a unique target group owing to incomplete or ambiguous statements in the NTS sections describing the project benefits ( see Fig 1 ) . The distribution of numbers of authorised animals included in NTSs is presented in S1 Fig . Notably , of all duly completed NTSs ( 2 , 328 in 2014 and 2 , 970 in 2015 ) , some involved 10 , 000 or more animals , i . e . , 29 NTSs in 2014 and 74 NTSs in 2015 . As NTSs applying for 10 , 000 or more animals were expected to have a substantial impact on the results in analyses of animal numbers , we excluded them from further analyses and evaluated these NTSs separately ( see S2 and S3 Figs ) . NTSs assigned to the target group ‘patients’ ( 1 , 873 NTSs in 2014 and 2 , 319 NTSs in 2015 ) were further classified into 1 of the 22 ICD-10 chapters , which yielded a general overview of the proposed and authorised animal experiments . As shown in Fig 2 , roughly 2/3 of NTSs assigned to the target group ‘patients’ were distributed across 6 ICD-10 chapters . The 6 most allocated ICD-10 chapters , in descending order of the number of NTSs assigned , were chapter II , Neoplasms , chapter IX , Diseases of the circulatory system , chapter VI , Diseases of the nervous system , chapter I , Certain infectious and parasitic diseases , chapter IV , Endocrine , nutritional and metabolic diseases , and chapter XIX , Injury , poisoning and certain other consequences of external causes . The percentages of the remaining ICD-10 chapters varied between <1% ( chapter XV , Pregnancy , childbirth and the puerperium ) and 5% ( chapter XI , Diseases of the digestive system ) . A low percentage of NTSs in 2014 and 2015 were allocated to more than one ICD-10 chapter ( labelled ‘multiple chapters’ ) . It is noteworthy that the proportions of chapters were very similar in both consecutive years , although the number of submitted NTSs increased considerably from 2014 to 2015 . The distributions of the corresponding animal numbers are shown in S4 Fig . To demonstrate that ICD-10 classification provides a fine-grained picture of envisaged in vivo biomedical research , we further allocated the NTSs to blocks of 3-character categories predefined in the ICD-10 classification . NTSs submitted in 2015 for the 3 most frequent ICD-10 chapters , i . e . , chapter II , Neoplasms , chapter IX , Diseases of the circulatory system , and chapter VI , Diseases of the nervous system , were used as an example ( Fig 3 ) . As the largest group , NTSs attributed to chapter II , Neoplasms , were also analysed for 2014 to assess whether the distribution was constant over time ( Fig 3A ) . Within the dominantly attributed research area Neoplasms , the highest percentage of NTSs in both years was assigned to category C76–C80 , Malignant neoplasms of ill-defined , secondary and unspecified sites , followed by categories C15–C26 , Malignant neoplasms of digestive organs , C81–C96 , Malignant neoplasms , stated or presumed to be primary , of lymphoid , haematopoietic and related tissue , and C69–C72 , Malignant neoplasms of eye , brain and other parts of central nervous system ( see Fig 3A ) . Two categories , C00–C14 , Malignant neoplasms of lip , oral cavity and pharynx , and D10–D48 , Benign neoplasms and neoplasms of uncertain or unknown behaviour , as well as Multiple were assigned only to NTSs submitted in 2015 , despite that the distribution of NTSs over the blocks of 3-character categories was highly congruent between years . Within the research area Diseases of the circulatory system , nearly 1/3 of NTSs were allocated to the category I30–I52 , Other forms of heart disease , followed by the categories I60–I69 , Cerebrovascular diseases , I70–I79 , Diseases of arteries , arterioles and capillaries , and I20–I25 , Ischemic heart diseases ( Fig 3B ) . Within the research area Diseases of the nervous system ( Fig 3C ) , 1/4 of NTSs were assigned to the category G35–G37 , Demyelinating diseases of the central nervous system . The second largest group belonged to G30–G32 , Other degenerative diseases of the nervous system , followed by the categories G10–G14 , Systemic atrophies primarily affecting the central nervous system , and G40–G47 , Episodic and paroxysmal disorders . To assess whether it is possible to specify research areas for projects requiring animal numbers above or below the average , we compared , as an example , the median numbers of animals per NTS for 9 research areas chosen from ICD-10 chapters II , Neoplasms , IX , Diseases of the circulatory system , and VI , Diseases of the nervous system ( see Fig 4 ) . As species could be a confounding factor for discrepancies between the proportions of NTSs and animal numbers , we only analysed those NTSs that mentioned the use of mice as laboratory animal species . The median values ranged from 405 animals per NTS for category C69–C72 to 912 animals per NTS for category G10–G14 . There was a significant difference in the distribution of animal numbers per NTS among the 9 ICD-10 blocks of 3-character categories ( Kruskal–Wallis test , p = 0 . 0026 ) . Subsequent analysis showed that for C81-C96 , Malignant neoplasms , stated or presumed to be primary , of lymphoid , haematopoietic and related tissue ( versus C15–C26 , C69–C72 , I60–I69 , and I70–I79 ) , G10–G14 , Systemic atrophies primarily affecting the central nervous system ( versus C15–C26 , C69–C72 , I30–I52 , I60–I69 , and I70–I79 ) , and G35–G37 , Demyelinating diseases of the central nervous system ( versus C69–C72 und I60–I69 ) , the median animal numbers specified in the NTSs were significantly higher ( pairwise Wilcoxon rank sum tests , p < 0 . 05 ) . Notably , despite the thematic differences , the medians for the remaining 6 categories were approximately 500 animals per NTS ( Fig 4 ) . Biomedical research involving nonhuman primates is one of the most controversial areas , and analysing NTSs can improve transparency . Hence , we analysed the statements described in the benefit section of 68 NTSs for projects authorised in 2015 for the use of nonhuman primates ( total number of animals = 4 , 024 ) . The majority of projects involving nonhuman primates could be allocated to the target group ‘patients’ ( 56 NTSs ) , while a small fraction ( 4 NTSs ) refers to the target group ‘pure basic research’ . Most projects were intended for use in late preclinical testing , as 47 out of 68 NTSs mention ‘toxicity’ , ‘safety’ , or ‘pharmacodynamics’/’pharmacokinetics’ in their titles . However , 8 NTSs ( accounting for 40% of animals ) could not be assigned an ICD-10 code or be attributed to one of the other target groups ( see Table 1 ) . This includes a single NTS authorised for the use of 1 , 440 cynomolgus monkeys that describes late-preclinical toxicity studies but does not state a specific disease in the benefit field . Nonhuman primates in NTSs assigned to the target group ‘patients’ comprised 3 , 429 cynomolgus monkeys , 233 rhesus monkeys , and 187 marmosets/tamarins , which were further analysed according to the associated ICD-10 code ( see Table 2 ) . The dominant ICD-10 chapters were , in descending order of the numbers of animals involved , chapter II , Neoplasms , and chapter VI , Diseases of the nervous system , which corresponds to the ranking of ICD-10 chapters attributed to all animals of NTSs submitted in 2015 . Notably , a high percentage of nonhuman primates were intended to be used for research in the area Diseases of the musculoskeletal system and connective tissue ( chapter XIII ) . Additionally , a large number of nonhuman primates were authorised for research and testing covered by multiple ICD-10 chapters . Our analysis also showed that cynomolgus monkeys are broadly used in various research areas , as they are represented in all ICD-10 chapters listed in Table 2 except for chapter X , Diseases of the respiratory system . The assignment of nonhuman primates to blocks of 3-character categories is shown in Table 2 .
In the overwhelming majority of cases , the envisaged beneficiaries indicated by researchers in the free-text fields of NTSs are patients . Approximately 80% of the NTSs analysed in this study were assigned to an ICD-10 code , and the percentage of NTSs in each ICD-10 category remained largely stable over 2 consecutive years . Thus , the largest proportion of animal experiments seems to be driven by a human disease-based objective , regardless of the indicated purpose of the authorised project , e . g . , ‘basic research’ or ‘translational/applied research’ . NTS allocation to the 22 ICD-10 chapters can give a prospective , in-depth picture of the objectives of animal experiments . Current legal regulations prescribe that the use of experimental animals is retrospectively reported in detail but differentiate only 12 purposes , all related to human diseases [14] . Our study revealed that NTSs were distributed over all 22 ICD-10 chapters , reflecting the broad spectrum of current research activities in Germany . The 6 most frequently attributed ICD-10 chapters were II , Neoplasms , IX , Diseases of the circulatory system , VI , Diseases of the nervous system , I , Certain infectious and parasitic diseases , IV , Endocrine , nutritional and metabolic diseases , and XIX , Injury , poisoning and certain other consequences of external causes . Remarkably , these 6 research areas are also target areas of the German federal government’s health programme . This programme supports research efforts to combat the 6 most common diseases occurring in the German population [15] . It is not surprising that there is great overlap between the most attributed ICD-10 chapters in our analysis and the research areas represented by the ‘German Centres for Health Research’ . Moreover , the 6 research areas addressed by most NTSs are in line with the most common diseases mentioned in the recent health report published by the Robert Koch-Institute [16] . The report lists the 10 main causes of death according to their ICD-10 codes , referring to 6 chapters , 5 of which were also among the 6 most frequently attributed ICD-10 chapters in our analysis . This high concordance supports the validity of NTS allocation to ICD-10 chapters in this pilot study , suggesting that the analysis can provide guidance for the development and support of directed 3R measures for specific research areas . The implementation of the 3R principle could be strengthened within the scientific landscape by establishing 3R ‘hubs’ in the German Centres for Health Research . The infrastructure and concentrated scientific competence of those centres enables the efficient development of specific alternative methods for the 6 research areas that are most highly represented in the NTSs . A major advantage of this strategy is that scientists working with animals can directly interact with those developing alternative methods . This translational scientific strategy could help to identify the best models to accurately mimic the human disease condition . Allocation of NTSs to ICD-10 codes at the ‘block of 3-character category’ level , as shown for the 3 chapters Neoplasms , Diseases of the circulatory system , and Diseases of the nervous system , allows a prospective , more fine-grained overview of the intended benefits of in vivo biomedical research than the retrospective overviews requested by European regulations [14] . For example , within the field of cancer research the annual statistical reports about animal usage defined by the European Commission only differentiate between the categories ‘oncology’ for basic research and ‘human cancer’ for translational and applied research [14] . With the ICD-10 , we were able to distinguish 15 types of neoplasms , according to the affected organ sites , for planned animal research projects . Within the research area Neoplasms , about 2/3 of the NTSs described studies of cancers of specified sites , i . e . , digestive organs , eye , brain and central nervous system , breast , and skin or of cancers of lymphoid , haematopoietic system and related tissues . Notably , with a difference of 1%–2% , the distribution of research areas within the field of Neoplasms was highly congruent between 2014 and 2015 . Comparing our results with the latest ‘Cancer in Germany’ report published by the Robert Koch-Institute in 2016 [17] , the distribution of planned in vivo projects related to different cancer types is highly congruent with the morbidity and death rates of the respective form of cancer . For example , malignant neoplasms of digestive organs ( C15–C26 ) are mostly allocated to NTSs in the field of neoplasms and are also linked to high morbidity and death rates [17] . Notably , some types of cancers , including skin , breast , prostate , and lung cancers , are not a focus of in vivo cancer research , as the high morbidity and death rates might suggest , and in view of these forms of cancer being highly relevant from a global point of view [18] . Other forms of cancer , involving the lymphoid; haematopoietic system and related tissues; and eye , brain , and central nervous system , rank high with respect to in vivo research but affect a relatively small proportion of the population [17] . A possible explanation for this discrepancy is that in certain areas of preclinical cancer research , sound in vitro or ex vivo models are available , although their validity is controversial [19 , 20] . Furthermore , our analysis showed that 1/3 of NTSs refer to unspecified neoplasms , i . e . , Malignant neoplasms of ill-defined , secondary and unspecified sites ( C76–C80 ) , and thus their proposed specific benefit is unclear . NTSs that mention ‘cancer research’ without further differentiation were allocated to this category . Here , the quality of NTSs and , consequently , that of the analysis thereof , could be increased if researchers provided a more precise description of the area of research , thereby supporting the identification of other key aspects for the development of alternative methods according to the 3R principle . This deeper analysis of NTSs indexed with ICD-10 codes suggests that a substantial amount of experimental animal research , including basic research , is driven by a human disease-based approach focusing on diseases with global relevance . Notwithstanding this , there is an urgent necessity for research in fields that affect only a small group of the population but have a detrimental effect on the quality of life of those concerned , and for research in other areas , such as pure basic research that aims to advance knowledge without direct applications to practical problems in the near future . Indexing NTSs with ICD-10 codes can also support transparency in research areas that are highly controversial , for instance , those experiments involving nonhuman primates [21] . Respecting public interest , Directive 2010/63/EU stipulates in Article 42 that all research projects using nonhuman primates have to be authorised by the competent authority , irrespective of whether they apply for a simplified procedure necessary to satisfy regulatory requirements [4] . Thereby , the public is informed of all planned projects using nonhuman primates by corresponding NTSs . The ICD-10 classification of NTSs provides fine-scale information about the prospective benefits of authorised nonhuman primate experiments beyond the current reporting of numbers of animals , as recently analysed for the United Kingdom [22] . More than half of the nonhuman primates for which applications were submitted in Germany in 2015 addressed specified human diseases . The envisaged benefits were associated with 10 ICD-10 chapters , and the largest fraction was related to chapter II , Neoplasms , followed by chapters VI , Diseases of the nervous system , XIII , Diseases of the musculoskeletal system and connective tissue , and I , Certain infectious and parasitic diseases . Compared to the allocation of all animals to ICD-10 chapters , as depicted in S4 Fig , the variety of research using nonhuman primates is narrower . In particular , a high number of nonhuman primates were included in research projects focused on Diseases of the musculoskeletal system and connective tissue ( chapter XIII ) , and this chapter ranked 11th in 2015 when looking at all animal species . Interestingly , for nonhuman primates , human musculoskeletal disorders were not represented in the German statistics on the reporting of experimental animals in 2014 or 2015 . This observation might indicate an increasing trend in research in this area . However , as authorised animal projects can be conducted within a period of 5 years , it is possible that this research activity will not appear in the current and next statistical reports . A small number of NTSs referring to a large number of nonhuman primates could not be classified , owing to vague language in the benefit and title fields . This is caused , in particular , by a single NTS for a large toxicity study using cynomolgus monkeys . Although the respective project obviously addresses the target group ‘patients’ , we were unable to allocate this specific NTS with an ICD-10 code . If the applicant of this particular NTS provided a more specific indication of the beneficiary , enabling the assignment of an ICD-10 code , the percentage of nonhuman primate research relevant to human diseases might have increased to approximately 95% . This example suggests that , especially in the field of nonhuman primate research , applicants often do not provide detailed information about their project in the NTS , either to safeguard their intellectual property or to protect their anonymity . Asking researchers to indicate the most relevant ICD-10 code when entering information about their project in the AnimalTestInfo database is a practicable solution to this problem . One of our next steps , therefore , is to integrate the ICD-10 classification system in AnimalTestInfo . Researchers then can more precisely and consistently describe the benefits of their project on a voluntary basis , which would substantially improve the quality of NTSs . Despite increasing transparency in animal research , our analysis using the ICD-10 system uniquely provides the scientific community with detailed and data-based information about potential research areas in which the promotion of 3R measures would be most efficient . Currently , research on alternatives to animal experiments and other 3R measures is often conducted randomly , without specifying concrete targets , i . e . , without naming those research areas that will benefit from the respective 3R strategy . Approaches that are not inspired by the reality of in vivo research , however , risk overlooking crucial needs of the prospective users , which are pivotal for later acceptance . We are now able to precisely identify research areas that consistently include a multiple number of in vivo projects , such as Malignant neoplasms of digestive organs ( C15–C26 ) , Cerebrovascular diseases ( I60–I69 ) , and Demyelinating diseases of the central nervous system ( G35–G37 ) . This information can be used by third-party funders as well as by researchers to develop , validate , and implement targeted 3R measures . We postulate that the development of 3R strategies for research areas with many NTSs would be most efficient , as a large number of animals would benefit , irrespective of whether replacement , reduction , or refinement is addressed . Another unique feature of indexing NTS with ICD-10 codes is the possibility to plot animal numbers retrieved from single projects against the associated ICD-10 code of a specific human disease . As an example , we calculated the average number of animals per NTS for 9 specific research areas within the 3 main research fields Neoplasms , Diseases of the circulatory system , and Diseases of the nervous system for mouse studies . The median number of animals was approximately 500 per NTS . Nonetheless , 2 research areas seemed to require more animals than average , i . e . , the category Lymphoid , haematopoietic cancers , requiring about 800 animals per NTS , and the category Systemic atrophies primarily affecting the central nervous system , requiring about 900 animals per NTS . There are many potential explanations for these large requested animal numbers . It is possible that some experimental settings need more animals than others , e . g . , due to high variability of the chosen end points or weak effects of interventions . Indeed , meta-analyses have shown high variation in the magnitude of standardised effects among different areas of research . For instance , Holman et al . [23] found standardised effect strengths of 0 . 84 for cancer and 1 . 42 for stroke , leading to group sizes of 24 and 9 animals , respectively , to verify statistically significant differences . Thus , a high number of animals per NTS could be the result of a sound sample size calculation to ensure adequate statistical power [24] . Low statistical power contributes to an inability to detect true effects and to the nonreproducibility of study results . Additionally , the positive predictive value diminishes with decreasing power . Therefore , preliminary power analyses are recommended to save animal lives in the long run . Another explanation for differences in the number of animals per NTS is that early preclinical studies ( i . e . , exploratory studies ) might require more animals than late preclinical studies , such as evaluations of effectiveness , safety , or toxicity ( confirmatory studies ) . The relevance of exploratory studies may depend on the specific area of research . Other plausible explanations for relative high animal numbers per NTS are the merging of several separate projects into a single large project or projects authorised for the breeding and maintenance of genetically altered animals . Thus , knowledge of these potential factors is indispensable to conduct a quantitative evaluation of authorised animal numbers and to support potential 3R measures . Due to their simplicity , the contents of NTSs , however , do not allow for more detailed analyses of why some research areas deviate with respect to animal numbers . Such analysis could only be carried out if the contents of the whole project application for animal experiments are available or by means of an animal study registry , which recent studies have called for [25 , 26] . Still , we believe that these figures can provide impetus for analyses of the underlying causes and may encourage the development of appropriate 3R measures , not only for replacement and reduction but also for refinement , as a large number of animals would benefit , irrespective of causal factors . In the coming years , we aim to use the information on NTS numbers and average animal numbers per NTS to provide researchers with an ‘NTS map’ of in vivo biomedical research , depicting trends in planned animal experiments . This will enable researchers to formulate hypotheses for increased or decreased animal usage observed in specific research areas identified by ICD-10 codes and to define potential 3R strategies . The success of replacement or reduction measures could subsequently be monitored by means of the map , as a peak should diminish over time . We realise that data for more than 2 years are needed to distinguish between a decrease in animal use caused by successful reduction or replacement measures from normal fluctuations , but we are convinced that this study provides a basis for a data-based navigation system to develop targeted 3R strategies and to measure the success of alternative methods . One limitation that needs to be addressed is related to NTSs that involve large numbers of animals . In our present study , a low percentage of NTSs involved a large number of animals , defined as 10 , 000 animals or more , i . e . , 30% and 50% of the total animals in 2014 and 2015 , respectively . As a large percentage of these NTSs could not be attributed to any target group , a lot of information concerning their benefits is missing , which becomes relevant when animal numbers are analysed ( see S1 Text ) . The present pilot study shows that NTSs contain crucial information on animal research that has not yet been considered and goes beyond the intent of the Directive 2010/63/EU to purely inform the public about animal experiments . Continuous classification of NTSs according to ICD-10 codes will enable us to characterise animal research with high precision and high resolution . Priorities and developments in research involving animals can be identified prospectively , and evidence-based arguments in favour of 3R research projects can be deduced . Our classification strategy has several benefits for different parties concerned with animal experiments: ( 1 ) For the public , a higher transparency regarding the benefits of current and upcoming in vivo research activities can be achieved . The information can contribute to informed opinions about animal experimentation , specifically in research areas involving controversial animal experiments . ( 2 ) Animal welfare can be improved , as detailed analyses of research areas with high numbers of in vivo projects as well as matching NTSs with respective authorised animal numbers could identify fields where 3R measures would be most efficient . Scientists and third-party funders looking for reasonable and innovative 3R research fields can utilise this information . ( 3 ) Researchers and funding bodies can benefit from the ample information provided in the NTSs , as future trends in in vivo biomedical research can be easily derived . Moreover , scientists applying for funding for ‘alternatives to animal experiments’ will be able to provide the precise number of animals that could be saved as a result of the planned project . Such numbers are often requested by funding institutions , such as the Wellcome Trust , National Centre for the Replacement , Refinement & Reduction of Animal in Research ( NC3R ) , the German Centre for the Protection of Laboratory Animals ( Bf3R ) , the German Federal Ministry of Education and Research ( BMBF ) , the Organisation of the German Research Foundation ( DFG ) , and Stiftung zur Forschung und Förderung von Ersatz- und Ergänzungsmethoden zur Einschränkung von Tierversuchen ( The SET Foundation ) . The ICD-10 classification of German NTSs will be further developed . The next steps involve the technical implementation of ICD-10 classes in AnimalTestInfo so that scientists can voluntarily allocate the most applicable ICD-10 code or target group to their project . In the future , NTSs shall be classified to ‘4-character subcategories’ , which will allow for deeper data analyses . In addition to the ‘benefits’ field , we plan to evaluate statements mentioned in the fields ‘harms’ and ‘3R measures’ . A box in which researchers can consistently indicate the expected degree of harm will be included . Here , we hope to gain information on implemented reduction and refinement measures within a specific research area . A detailed indication of the applied refinement measures would benefit other researchers working in the same field and could aid in improving the reproducibility of results . To enable the targeted development of new 3R strategies , scientists could use the opportunity to provide more detailed information about their project , including the underlying hypothesis and study design . Finally , detailed information on animal experimentation and the resulting 3R research activities can contribute to replacing or reducing animal testing and to the implementation of refinement measures minimising pain , suffering , distress , or lasting harm in animals used for research .
In Germany , NTSs are published in the web-based , searchable database AnimalTestInfo ( https://animaltestinfo . de/ ) , which was created and is maintained by the BfR [6] . All NTSs of authorised German scientific projects using animals are published , including projects classified as severe or authorised for the use of nonhuman primates . In Germany , projects that are subject to a simplified administrative procedure , e . g . , testing necessary to satisfy regulatory requirements , do not require an NTS and , therefore , are not represented in the database . It is essential to mention that NTSs can involve experiments lasting up to 5 years; therefore , the number of NTSs for a particular year does not reflect the number of animals used annually and retrospectively reported by each Member State of the EU . The data requested in NTSs are based on the working document on nontechnical project summaries by the national competent authorities for the implementation of Directive 2010/63/EU on the protection of animals used for scientific purposes [14] . The relational database management system Informix was used to create and maintain a database with 16 tables . Four of the tables contain the information extracted directly from the NTSs , such as title , number and species of animals to be used , and objectives and potential benefits of the project . Another 4 tables were used to store indexing data , including ICD-10 codes , and the other tables contained catalogues and redundant data used to speed up searches . The application was written in CFML and JavaScript for the Adobe ColdFusion web application development platform . In the principle workflow for submitting data to the database , researchers first log in to the database and create an NTS , which receives an individual ID . The entire procedure complies with the current Federal Data Protection Act . The researcher then sends the ID for its NTS together with the corresponding application form for the approval of an animal experiment to the competent authority . The competent authority ensures that the content of the NTS is accurate and corresponds with the project application . With the approval of the application , the NTS is activated in the database and made visible to the public . For this pilot study , duly completed NTSs transferred to us in 2014 ( n = 2 , 328 ) and 2015 ( n = 2 , 970 ) , i . e . , a total of 5 , 298 NTSs , were analysed . A few NTSs ( n = 54 ) were excluded from the analysis because the number of animals was not specified . For technical reasons , there is a 3-month time lag between the receipt and publication of an NTS in AnimalTestInfo . Hence , not all of the analysed NTSs were uploaded in the database in the years of receipt , i . e . , some were published at the beginning of the following year ( 2015 and 2016 , respectively ) . The 5 , 298 NTS foresee the use of 7 , 872 , 775 animals ( in 2014 , n = 2 , 816 , 639; in 2015 , n = 5 , 056 , 139 ) . The number of animals per NTS varied greatly , with a range from 1 ( cattle ) to 394 , 560 animals ( other fish ) . Most NTSs indicate the use of fewer than 10 , 000 animals . Approximately 2% of all duly submitted NTS indicate the use of 10 , 000 or more animals , i . e . , 29 NTSs in 2014 and 74 NTSs in 2015 . This small proportion of NTSs ( i . e . , 103 in total ) had a disproportionate influence on the results when NTSs were analysed according to animal numbers ( see S3 Fig ) . Therefore , the 103 NTSs indicating the use of 3 , 352 , 066 animals in total were excluded from the general analysis and were analysed separately ( see S1 , S2 and S3 Figs and S1 Text ) . The threshold of 10 , 000 animals was chosen for pragmatic reasons . The use of other thresholds was not evaluated but is planned for future research projects . NTSs were classified by 3 indexers . The 2 main indexers were documentation assistants experienced in the area of terminology ( use of thesauri , e . g . , MeSH ) . The third indexer , an experienced scientist , was consulted by the 2 documentation assistants only if an NTS could not be easily attributed to an ICD-10 code or addressed target groups other than ‘patients’ , ‘wild animals’ , or ‘farm and domestic animals’ ( an overview of the workflow is depicted in S5 Fig ) . Indexing was assisted by a standard operation procedure for classification ( available in German in the OpenAgrar repository; https://doi . org/10 . 17590/20171025-154025 ) and online tools [11 , 12] . Each NTS was classified by a single indexer . NTSs were opened individually and statements in the fields ‘title’ and ‘benefits’ were evaluated . First , NTSs were allocated to one of the following 5 target groups: ‘patients’ , ‘pure basic research’ , ‘consumers’ , ‘farm and domestic animals’ , ‘laboratory animals’ , and ‘wild animals’ . This supported classification-based information retrieval in cases in which ICD-10 codes were not applicable . The allocated target group refers to the population that is intended to benefit obviously from the results of the respective research project and was defined as follows: the target group ‘patients’ refers to NTSs in which a corresponding ICD-10 code could be allocated based on the statements given by applicants , i . e . , when a target disease was mentioned . The target group ‘pure basic research’ was assigned if the planned research is intended to increase general biological knowledge and is not primarily application oriented . The target group ‘consumers’ was attributed to animal research considering the harmfulness or safety of consumer products , chemicals , or pesticides , including research in the areas of occupational safety and health . The target group ‘farm and domestic animals’ was attributed to veterinary research or experimental farm animal husbandry . The target group ‘laboratory animals’ included experimental laboratory animal science , e . g . , the development of refinement measures . The target group ‘wild animals’ refers to , for example , research on conservation measures or testing for environmental toxicity . In cases in which NTS statements did not allow for an unequivocal assignment of a target group or in cases in which the planned animal usage was presented as an undesignated service for experimental in vivo studies ( e . g . , establishment of models or breeding ) , the label ‘no classification’ was used . NTSs assigned to the target group ‘patient’ were further assigned an ICD-10-GM-2016 code . NTSs obtained in 2014 were classified to the ICD-10 chapter level only , except for those assigned to chapter II , Neoplasms , which were assigned to blocks of 3-character categories . The construction of an assisting IT-tool was completed in spring 2016; all NTSs obtained in 2015 were classified into a block of 3-character category with the help of this tool ( but only chapters II , VI , and IX were further evaluated in this pilot study ) . NTSs were indexed according to the month of receipt and a list containing the allocated ICD-10 codes was exported for subsequent analyses implemented in MS Excel . The resulting MS Excel files can be retrieved from the OpenAgrar repository ( https://doi . org/10 . 17590/20171025-153520 ) . A retrospective evaluation of the indexing consistency between the 2 main indexers was conducted . The 2 documentation assistants independently classified 100 randomly chosen ( using an Informix random number generator ) NTSs approved in 2015 . The number and percentage of NTSs with matching ICD-10 codes were calculated . The analysis revealed a concordance of 95 . 5% for ICD-10 chapter and 80 . 6% for blocks of 3-character categories . Data of NTSs per target group ( Fig 1 ) , NTSs per ICD-10 chapter ( Fig 2 ) , and NTSs per block of 3-character category for chapters II , VI , and IX ( Fig 3 ) are presented as percentages of the total NTS numbers . Please note that only in Fig 1 are all duly completed NTSs submitted in 2014 and 2015 depicted ( n = 5 , 298 ) . For data presented in Figs 2–4 , NTSs referring to 10 , 000 animals or more ( n = 103 ) were excluded from analyses . The separate analyses of these data are shown in the Supporting Information ( S1–S3 Figs ) . All numerical values are mathematically rounded up or down to whole numbers . For the comparison of NTSs allocated to the 9 ICD-10 blocks of 3-character categories ( C15–C26 , C69–C72 , C81–C96 , G10–G14 , G30–G32 , G35–G37 , I30–I52 , I60–I69 , and I70–I79 ) shown in Fig 4 , animal numbers per NTS were calculated and data are shown as box plots with medians [25th and 75th percentiles] . To facilitate comparisons , only those projects that planned to use mice alone for their research were included in this analysis . Statistical analyses were conducted using R [27] . Data were analysed by Kruskal–Wallis tests , followed by Wilcoxon rank sum tests . Differences in medians with p < 0 . 05 were considered statistically significant . Figs 1 and 4 and S1–S3 Figs were generated using SigmaPlot version 13 ( Systat Software , Inc . , San Jose , USA ) ; Figs 2 and 3 and S4 Fig were generated using MS Excel ( Microsoft Corp . , Redmond , USA ) . | The AnimalTestInfo database was developed in Germany to make the nontechnical summaries ( NTSs ) of animal research studies available in a searchable and easily accessible web-based format . This database helps address requirements stipulated in a European Directive regarding the transparency and publication of animal experiments . In this meta-analysis , we analysed NTSs submitted in 2014 and 2015 to AnimalTestInfo and classified them according to the International Classification of Diseases and Related Health Problems ( ICD ) system . Each NTS was assigned an ICD-10 code based on the envisioned beneficiary of the research project . Based on these codes , we conducted various quantitative analyses . Our results provide a granular overview of the prospective uses of experimental animals and identify areas in need of alternative strategies to help replace , reduce , and refine animal research . We hope this data will inform governments and funding agencies in advancing the integrity and reporting of responsible animal research . | [
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Genome-wide yeast two-hybrid ( Y2H ) screens were conducted to elucidate the molecular functions of open reading frames ( ORFs ) encoded by murine γ-herpesvirus 68 ( MHV-68 ) . A library of 84 MHV-68 genes and gene fragments was generated in a Gateway entry plasmid and transferred to Y2H vectors . All possible pair-wise interactions between viral proteins were tested in the Y2H assay , resulting in the identification of 23 intra-viral protein-protein interactions ( PPIs ) . Seventy percent of the interactions between viral proteins were confirmed by co-immunoprecipitation experiments . To systematically investigate virus-cellular protein interactions , the MHV-68 Y2H constructs were screened against a cellular cDNA library , yielding 243 viral-cellular PPIs involving 197 distinct cellar proteins . Network analyses indicated that cellular proteins targeted by MHV-68 had more partners in the cellular PPI network and were located closer to each other than expected by chance . Taking advantage of this observation , we scored the cellular proteins based on their network distances from other MHV-68-interacting proteins and segregated them into high ( Y2H-HP ) and low priority/not-scored ( Y2H-LP/NS ) groups . Significantly more genes from Y2H-HP altered MHV-68 replication when their expression was inhibited with siRNAs ( 53% of genes from Y2H-HP , 21% of genes from Y2H-LP/NS , and 16% of genes randomly chosen from the human PPI network; p<0 . 05 ) . Enriched Gene Ontology ( GO ) terms in the Y2H-HP group included regulation of apoptosis , protein kinase cascade , post-translational protein modification , transcription from RNA polymerase II promoter , and IκB kinase/NFκB cascade . Functional validation assays indicated that PCBP1 , which interacted with MHV-68 ORF34 , may be involved in regulating late virus gene expression in a manner consistent with the effects of its viral interacting partner . Our study integrated Y2H screening with multiple functional validation approaches to create γ-herpes viral-viral and viral-cellular protein interaction networks .
Gamma-herpesviruses comprise a subfamily of Herpesviridae , a group of enveloped , double-stranded DNA viruses with large and complex genomes ranging from 120 to 230 kbp in length [1] . Herpesviruses have two distinct life cycle phases: latency and lytic replication . During latent infection , no active viral replication occurs and only a limited number of viral genes , including non-coding RNAs , membrane proteins , and various nuclear antigens , are expressed to maintain the viral genome and guard against host immune responses [2]–[4] . Upon reactivation of the latent virus , lytic replication ensues and results in the production of viral progeny , leading to the destruction of the host cell . In contrast to alpha and beta herpesviruses , gamma-herpesviruses have distinct cellular tropisms and establish life-long persistent infections in lymphocytes [5] . Two well-known human gamma-herpesviruses , Kaposi's sarcoma herpesvirus ( KSHV ) and Epstein Barr Virus ( EBV ) , are associated with the development of both lymphoid and non-lymphoid cell tumors , including Kaposi's sarcoma ( KS ) , Burkitt's lymphoma , nasopharyngeal carcinoma , and lymphoproliferative diseases in immunocompromised patients [5]–[8] . Though tumorigenesis induced by gamma-herpesviruses requires multiple genes expressed during latent infection , it has been suggested that lytic viral gene products expressed during sporadic reactivation in tumor lesions also promote cell growth [4] , [9] , [10] . A better understanding of the interplay between gamma-herpesviruses and the cells they replicate in will provide insight into the tumor-promoting properties of herpesviruses and may lead to the development of improved therapeutic strategies . MHV-68 , a natural rodent pathogen , serves as a useful model for the study of human gamma-herpesviruses due to the similarity of the gene sequences and genome organization . 80% of MHV-68 ORFs share significant homology with their human viral counterparts . Furthermore , the ability of MHV-68 to establish robust de novo productive infections in various human and mouse cell lines and to infect laboratory mice provides an experimental system to study the biological significance of virus-host cell interactions in vitro and in vivo [11]–[13] . The recent development of an MHV-68 virus that expresses luciferase provides a powerful tool to monitor virus replication in live mice [14] . Although it was known that MHV-68 infected multiple tissue types , live imaging of viral replication revealed new sites of infection and demonstrated that timing of lytic replication and clearance varied among different tissues and organs [14]–[16] . Combined with the ability to silence gene expression by RNA interference , and the availability of transgenic and knockout mice , a wide range of tools are available to interrogate MHV-68-host interactions both in vitro and in vivo . Over the past several years , we have undertaken multiple studies to systematically characterize the genes and proteins of MHV-68 , beginning with a comprehensive analysis of viral gene expression [17] and extending to proteomic analysis of the MHV-68 virion [18] , large-scale signature tagged mutagenesis to identify essential MHV-68 open reading frames [19] , and high-throughput random insertional mutagenesis for genome-scale functional profiling [20] . In this report we characterize the intra-viral and virus-cellular protein interaction networks of MHV-68 . Protein-protein interactions are critical for the functions of most proteins , and the systematic identification of viral protein interactions will provide insight into MHV-68 replication and pathogenesis . For example , viral proteins must interact with each other to create complexes needed for genome replication and virus assembly . However , even viruses with large genomes , such as the herpesviruses , require cellular proteins to supply activities not encoded in the viral genome . Viruses also interact with cellular proteins to manipulate cellular pathways in order to promote an environment favorable to the virus . Conversely , the cell expresses proteins that may bind directly to viral proteins to inhibit their functions to promote an antiviral state . Although several recent studies have extensively characterized the intra-viral protein interactions of five herpesviruses [21]–[25] much less is known about the gamma-herpesviral-cellular PPI network . To date , a single experimental study reported 173 interactions between EBV and human proteins [24] and one computational analysis predicted 20 herpesvirus-cellular protein interactions [21] . These studies are further limited by the lack of robust replication of human herpesvirus in vitro and in vivo , which makes functional validation of virus-cellular interactome difficult . MHV-68 complements this deficiency and has the capacity for effective genetic manipulation to study lytic replication in vitro [19] , [26] . Thus , a comprehensive inventory of MHV-68 protein interactions will provide a valuable resource to understand the interplay between the virus and host cell and will yield insights into the functions of individual proteins during viral replication . Therefore , to elucidate the molecular interactions of MHV-68 proteins we conducted genome-wide Y2H screens ( Fig . 1 ) . We report the identification of 23 intra-viral protein interactions and 243 virus-cellular protein interactions , the vast majority of which are novel . Evaluation of the virus-cellular protein interactions was aided by the development of a novel scoring method based on the network distances of MHV-68-interacting cellular proteins within a high-confidence binary cellular protein interaction network . Our study integrated computational analysis of MHV-68 interacting proteins with multiple complementary functional assays to demonstrate the biological relevance of the MHV-68 intra-viral and virus-cellular protein interactomes .
To systematically investigate the intra-viral PPI networks , genome-wide Y2H screens with MHV-68 genes were performed ( Fig . 1 ) . All predicted viral ORFs [12] were cloned into a Gateway entry vector , enabling efficient shuttling of genes into different expression plasmids . ORFs larger than 1 . 5 kb were divided into smaller fragments clones since large genes tend to yield fewer interactions in the Y2H assay [27] . Similarly , genes encoding proteins with predicted trans-membrane domains were truncated to express only the soluble portion . The entire library was then transferred into the Y2H DNA-binding domain ( pDEST32 ) and activation domain ( pDEST22 ) destination vectors and transformed into the yeast strains PJ69-4α and PJ69-4a , respectively [28] . All possible pair-wise combinations were tested independently for activation of the reporter genes HIS3 and ADE2 in quadruplicate using a 384-spot array format . Twenty-five ( 25 ) pairs of MHV-68 proteins were found to activate both reporter genes ( Fig . 2A , Table S1 ) , including two pairs in which the partners interacted when cloned as either bait or prey . The physical interactions between viral proteins were validated by co-immunoprecipitation ( Co-IP ) and co-localization assays ( Table S1; Fig . 2B and C ) . MHV-68 genes and gene fragments were transferred to mammalian expression vectors as fusions to the epitope tags FLAG ( pTAG ) and V5 ( pHB ) or the fluorescent proteins GFP and RFP . HEK293T cells were transfected with pairs of plasmids encoding putative interacting proteins and infected with MHV-68 24 h later . Cell lysates were co-immunoprecipitated with anti-FLAG , anti-V5 , or non-specific anti-mouse IgG antibodies and subjected to western blotting with anti-FLAG and anti-V5 antibodies . Of the 23 intra-viral interactions , 16 ( 70% ) pairs were confirmed in at least one direction of the antibody pull-down ( Table S1 , Fig . S1 ) . These interactions were further validated by co-localization of the interacting partners using either pairs of GFP- and RFP-tagged or FLAG- and V5-epitope tagged proteins expressed in NIH 3T3 cells ( Table S1 ) . Three additional interactions could not be confirmed due to the low expression in our system , but were previously reported between homologous proteins from other herpesvirus family members ( Table S1 ) [29]–[33] . In total , 19 out of 23 interactions were supported by secondary experiments for a combined confirmation rate of 83% . To illuminate the biological roles of the newly identified intra-viral PPIs , interactions were grouped according to the known and predicted functions of the viral proteins ( viral DNA replication , assembly/egress , capsid and envelope structural proteins , and unknown ) ( Fig . 2A ) . Tegument proteins were prominently featured among the interactions detected under our stringent screening conditions . In particular , ORF45 and ORF33 interacted strongly in both orientations in the Y2H assay , co-localized in the nucleus ( Fig . 2B ) , and co-purified in both directions in antibody pull-down experiments ( Fig . 2C ) . Both are tegument proteins expressed late during the infection cycle [34] , [35] and are essential for viral assembly and egress . Their homologues in KSHV interact with KSHV ORF64 , a hub protein that recruits other tegument proteins [22] . Our results suggest that the three proteins - ORF33 , ORF45 and ORF64 - may be a part of a larger multi-protein complex ( Fig . S1-B ) . To develop a comprehensive view of the current status of gamma-herpesvirus intra-viral protein interactions , we integrated the large-scale and literature-curated EBV and KSHV intra-viral protein interactions from [25] with the MHV-68 interactions identified here ( Fig . S2 ) . Seven non-self MHV-68 interactions were shared with each gamma-herpes virus , but only two interactions were found in all three ( Table S1 ) . Though many more interactions have been identified in both EBV and KSHV , the extent of the overlap between the two is surprisingly small , with only 13 interactions in common ( 5% of the 250 non-self interactions in EBV; 7% of the 175 non-self interactions in KSHV ) . The relatively low overlap between EBV and KSHV appears to be due to different proteins from KSHV and EBV yielding interactions in the various screens ( Fig . S2 ) . Assuming that interactions between conserved gamma-herpesviruses are conserved , this suggests that the screens for gamma-herpesvirus intra-viral interactions have missed many interactions . To identify cellular proteins that interacted with MHV-68 proteins , we performed Y2H library screens with 84 MHV-68 constructs . Since our primary goal in this study was to identify host factors that played a role in MHV-68 lytic replication , we chose a high quality cDNA library from a tissue type ( human liver ) known to support MHV-68 infection ( [36]and unpublished data ) . MHV-68 undergoes lytic replication in various human and mouse cell lines in vitro , including liver-derived cell lines ( [11]–[13] , and unpublished data ) . In addition , MHV-68 productively infects the liver in experimentally inoculated mice [14]–[16] . Finally , liver tissue contains multiple cell types and expresses a broad range of cellular genes . We acknowledge that by using a human cDNA library , interactions with murine-specific proteins will be missed . Our Y2H screens employed a modified Y2H vector in which gene fragments are cloned between the GAL4 activation domain and URA3 . ( Fig . 1 ) [37] , [36] . Growth of yeast on medium lacking uracil selects for inserts that are cloned in frame and expressed , and selects against clones whose inserts are in the wrong reading frame , contain stop codons , or are poorly expressed . Thus , the library has fewer clones than traditional libraries , which increases the likelihood of comprehensively sampling the AD clones in Y2H screens . In addition , frame-shift mutations that occur during Y2H screening – an important source of false-positives [38] – are selected against . The 84 MHV-68 genes and gene fragments described above were cloned into the Gal4 DNA binding domain plasmid pXDGATcy86 and screened at least twice against the human liver Y2H library ( 215 screens total ) . Seventy-four MHV-68 genes yielded positive colonies in at least one screen . The activation domain inserts from 1879 colonies ( up to 48 per screen ) were PCR-amplified and sequenced , yielding 1544 pairs of interacting proteins representing 508 different interactions . All unique cellular gene fragments ( excluding known false-positives ) were then re-cloned into the activation domain plasmid in fresh yeast cells and retested in the Y2H assay with the bait from the original Y2H assay . Two hundred-forty three pairs independently activated expression of both the HIS3 and ADE2 reporter genes in the retest and were considered true Y2H interactions ( Fig . 3A , Fig . S3 , Table S2 ) . Another 25 pairs activated expression of only one Y2H reporter . To characterize the cellular factors identified in the Y2H screen , we grouped them according to the known or predicted functions of their viral partners ( DNA replication , life-cycle regulation , virion assembly/egress , and capsid or envelope structural proteins ) and analyzed the functional annotation of all cellular proteins and their immediate neighbors in the cellular PPI network . Interestingly , the cellular factors identified in the screen were enriched in Gene Ontology ( GO ) terms related to the function of their viral partners ( Fig . S4A ) . For example , the targets of the viral DNA replication proteins participated in DNA replication , recombination , and repair . Similarly , viral regulatory proteins tended to interact with cellular proteins involved in the regulation of ubiquitin-ligase/protein kinase activities , protein amino acid phosphorylation , and the antigen receptor and integrin-mediated signaling pathways , whereas viral assembly/egress proteins tended to interact with cellular proteins involved in cell adhesion , lipid homeostasis , and regulation of cytoskeleton organization/biogenesis ( Fig . S4A ) . GO terms related to regulation of apoptosis and I-kB kinase/NF-kB cascade were enriched among the partners of viral proteins in multiple functional categories ( DNA replication , regulation , and structural ) while the regulation of post-translational modification term was enriched in all the functional groups . This latter observation may reflect the importance of these modifications at multiple stages of the viral replication cycle . In order to analyze the network properties of the viral-cellular protein interactions , cellular proteins identified in the Y2H screens were mapped onto the nodes of a high-confidence cellular PPI network . This network consisted of binary interactions reported in the DIP [39] , IntAct [40] and MINT [41] databases that were supported by at least one small-scale or multiple high-throughput experiments ( see Methods for details ) . Of the 197 distinct cellular proteins identified in our screen , 101 were present in this reference network . The cellular proteins identified in the screen were connected to an average of 5 . 7 neighbors , a number significantly larger than expected by chance ( 4 . 1; p-value 1 . 8×10−2 ) . In addition , the average network distance between two proteins that interacted with MHV-68 proteins , calculated as the length of the shortest path connecting the corresponding network vertices , was smaller than expected for two proteins picked randomly from the reference network ( 4 . 6 versus 5 . 3; p-value 4 . 0×10−4 ) . Similar trends were also observed with cellular proteins that interacted with EBV [24] ( Table 1 ) . Graphing the distribution of network distances between proteins targeted by MHV-68 revealed a significant shift toward smaller values as compared to the distribution obtained for an equally-sized set of proteins randomly selected from the reference network ( p-value 4 . 9×10−3 ) ( Fig . 4A ) . The distribution of the experimental data displayed a sharp drop-off at the large inter-protein distances and a higher frequency of proteins three or fewer edges away from each other . These observations indicate that the cellular factors that interacted with MHV-68 proteins are located nearer to each other than expected by chance within the reference PPI network . In order to rule out the possibility that the observed distribution arose as a result of biases in the dataset , we performed two additional analyses . One potential source of bias is the composition of the Y2H AD library . Since the library was not normalized , highly expressed genes are likely overrepresented and may give rise to more interactions , including potential false-positive interactions . We therefore analyzed how sensitive the distribution of network distances was to the removal of cellular proteins that interacted with more than one viral protein . As we observed no significant changes ( Fig . S5A ) we infer that , even if present , compositional bias of the cDNA library does not affect the inter-target distance distribution . A second potential source of bias is the presence of highly connected proteins ( i . e . , those with high degree ) , which are enriched among the partners of MHV-68 proteins . Such proteins , by virtue of the large number of cellular proteins they interact with , have a greater likelihood of being close to another MHV-68-binding protein by chance . To test this possibility , we compared the average distribution of network distances between a set of randomly selected proteins with that obtained for an equivalently sized set of proteins having the same degree distribution of the cellular proteins that interacted with MHV-68 . Although the distribution of distances in the degree-modified set of random proteins was shifted slightly toward shorter path lengths ( Fig . S5B ) , the shift was less than that observed in the experimental data . Also , the difference between the experimentally observed distance distribution and the one obtained for the modified reference set remained statistically significant ( p-value 4 . 8×10−2 ) . We therefore concluded that the distinct shape of the distance distribution histogram was an inherent property of the set of cellular proteins specifically targeted by the MHV-68 virus , and was not influenced by the potential composition bias of the cDNA library used in our screen or higher than average connectivity of the targeted cellular proteins . Similar changes in the distance distribution profiles were observed in previously reported sets of proteins identified in viral-cellular protein interaction screens ( Fig . S6 ) , suggesting that this may be a general feature of cellular proteins targeted by viruses . Since the shift toward shorter distances between cellular proteins that interacted with MHV-68 was unlikely to have occurred by chance , we hypothesized that interactions with closely linked cellular proteins was important for MHV-68 replication . We further reasoned that cellular proteins that bound to MHV-68 proteins and that were located in regions of the cellular protein interaction network with higher densities of MHV-68 targeted proteins were more likely to play an important role in the MHV-68 life cycle . To test this hypothesis , we developed a scoring system to identify such proteins . For each cellular protein that interacted with an MHV-68 protein , we counted the number of other cellular proteins that bound to an MHV-68 protein and that were within four protein-protein interactions in the cellular protein interaction network ( see example in Fig . 4B ) . The choice of the limiting distance was based on computational analyses indicating that functional correlations within PPI networks tend to disappear at network distances above four [42] , [43] . Each of the 101 cellular proteins that interacted with an MHV-68 protein and that were present in the reference cellular protein interaction network were scored as described above , with scores ranging from 0 to 81 . To estimate the statistical significance of this score , p-values corresponding to the probability of having that many neighboring viral targets were calculated ( Fig . 4C , Table S3 ) . We then divided the proteins into three groups based on their network distance scores: high-priority ( Y2H-HP , which included 60 top ranked genes ) , low priority ( Y2H-LP , which included the 51 proteins with the lowest scores ) , and not scored ( Y2H-NS , which included the 96 proteins not present in the reference cellular protein interaction network ) . As a preliminary test to evaluate the biological relevance of prioritization scores , we inhibited the expression of the 60 highest scoring cellular genes by RNA interference and analyzed the effect on MHV-68 replication . A two-step luciferase reporter virus system was employed to monitor viral replication after siRNA treatment in a sensitive and medium throughput manner [14] , [44] . First , siRNA-treated 293T cells were infected with M3-Luc MHV-68 , a reporter virus that contains an M3-promoter driven firefly luciferase reporter . Second , supernatant from the infected siRNA-treated cells was used to infect fresh 293T cells and luciferase levels were measured 20 h later . Because luciferase expression from the M3-Luc virus has a linear relationship across a broad range of the infectious particles ( Fig . S7A ) , the level of luciferase activity provides an indirect measurement of viral titer . All siRNA experiments were performed in 96-well plates , with each siRNA being transfected into HEK293T cells in triplicate wells ( Fig . 5A ) . As negative controls we used an siRNA with a sequence that did not match any known gene , and siGL3 , which targets luciferase ( siGL3 will inhibit luciferase RNA expression in the first step , but not the second , and has no effect on MHV-68 replication ) . As a positive control we used siRTA , which inhibited immediate the MHV-68 early viral gene Replication and Transcription Activator ( RTA ) and significantly reduced viral replication ( Fig . 6A ) . siRNA-transfected cells were infected with a low titer of the M3-Luc reporter virus ( MOI = 0 . 02 ) that was empirically determined to enable both positive and negative effects on virus replication to be detected . The impact of the siRNAs on cell viability was assessed in parallel by measuring ATP levels at 50 h post-mock infection , which mimics the condition of the cells at the time of peak virus replication after siRNA treatment ( Fig . 5A ) . siRNAs that reduced cellular ATP levels more than 40% were considered to be toxic and were excluded from further analysis . This experiment was repeated twice with significant correlation in the magnitude of the fold change in viral replication ( R = 0 . 89 ) . More than 50% of the siRNAs that targeted Y2H-HP cellular genes either enhanced or inhibited viral replication ( Fig . 5B ) . To more rigorously demonstrate the value of this prioritization scheme , we randomly selected 20 genes each from Y2H-HP , Y2H-LP , and the set of human proteins from the reference human PPI network that did not interact with MHV-68 proteins ( C-R ) . Each gene was inhibited with two independent siRNAs using the approach outlined above . Genes for which both siRNAs caused a consistent phenotype , either an enhancement or inhibition of luciferase levels by at least one log2 relative to the plate median , were considered to have a significant effect on viral replication ( Fig . 5C , S7B , and S8 ) . Overall , we found 53% of the genes in the Y2H-HP group significantly affected MHV-68 replication , whereas only 21% of Y2H-LP and 16% of C-R did so ( P<0 . 05 ) ( Fig . 5C , S7B , and S8 ) . Thus , consistent with our preliminary study ( Fig . 5B ) , cellular proteins with high scores were much more likely to affect MHV-68 replication than cellular proteins with low scores or that were randomly chosen from the reference network . These results suggest that the high scoring cellular proteins ( i . e . , those that are located near other cellular proteins that also interacted with MHV-68 proteins ) are among the most critical cellular targets for the virus , and may reveal regions the virus-cellular protein interaction network that are more likely to affect virus replication . To determine if particular pathways were over-represented among the Y2H-HP proteins , we repeated the analysis of GO terms for this subset . As shown in Fig . 5D , five major functional clusters were significantly enriched ( post-translational protein modification , and regulation of apoptosis , protein kinase cascade , transcription from RNA polymerase II promoter , and I-κB kinase/NF-κB cascade ) . In contrast , the functional annotations of proteins in the low priority group were mostly involved in regulating cellular organelle organization and biogenesis , lipid homeostasis and DNA biogenesis . Moreover , although some terms , such as regulation of apoptosis , were common to both groups , they were enriched to a greater extent in the high priority group . Given that the Y2H-HP proteins were more likely to affect MHV-68 replication , the fact that these pathways are more highly enriched in the Y2H-HP group suggests that they may be critical for MHV-68 replication . It is possible that low priority proteins may have a more dramatic effect on MHV-68 replication during the infection in vivo . For more in depth follow up experiments we focused on cellular proteins that interacted with a group of viral ORFs that were previously shown to be essential for virus replication in a screen of MHV-68 signature-tagged mutants [19] and that displayed similar phenotypes in subsequent mechanistic studies [45]–[48] . Five viral proteins ( ORF18 , ORF24 , ORF30 , ORF31 and ORF34 ) were involved in regulating late gene expression but were dispensable for early gene expression and DNA replication . In this study we identified TAX1BP1 ( Tax1-binding protein ) and PCBP1 ( Poly ( rC ) -binding protein 1 , also referred to as αCPs and hnRNP E ) as potential cellular binding partners of ORF31 and ORF34 , respectively . Both proteins were included in the Y2H-HP group and had a significant effect on MHV-68 replication when their expression was inhibited . However , TAX1BP1 and PCBP1 had opposite effects on MHV-68 . Whereas inhibiting TAX1BP1 expression reduced M3-Luc-MHV-68 replication , silencing PCBP1 caused an increase ( Fig . 6A ) . We obtained similar results using two additional shRNA constructs for each gene that targeted different sequences on PCBP1 and TAX1BP1 , confirming our initial observations . Conversely , when PCBP1 and TAX1BP1 were over-expressed , TAX1BP1 enhanced viral replication ∼3 . 5-fold , whereas PCBP1 inhibited MHV-68 viral replication approximately 5-fold ( Fig . 6B ) . The interaction of ORF34 and PCBP1 was analyzed further as a model to investigate cellular protein effects on viral replication . We obtained additional support for the physical interaction between ORF34 with PCBP1 by demonstrating binding in GST-pull down assays and co-localization in immuno-fluorescence assays ( IFA ) in cells ( Fig . 7A–C ) . Since PCBP1 has been shown to affect gene expression at multiple levels via its cis-binding with either DNA or RNA [49]–[51] , we hypothesized the interaction of ORF34 and PCBP1 might regulate viral gene expression . A series of viral promoter-linked reporter constructs was used to determine which stage of viral replication was affected by PCBP1 . Over-expressing PCBP1 reduced the induction of the luciferase expression from two viral late gene promoters ( ORF26 and M9 promoters ) upon infection ( Fig . 7E ) . In contrast , the inhibitory effects of PCBP1 were not observed on RTA auto-activation of its own promoter or on RTA-mediated activation of an MHV-68 early gene promoter ( ORF57 promoter ) ( Fig . 7D ) . This result was consistent with the phenotype of the ORF34 knockout mutant virus , which affected expression of late viral promoter , but did not affect expression at early viral promoters or reduce viral DNA replication [48] . Together , these results suggested that the interaction of ORF34 and PCBP1 regulates viral replication by affecting the transcriptional activities of late promoters . Since the effect of ORF34 on late gene expression is similar to that of ORF18 , ORF24 , ORF30 and ORF31 , we repeated the GO term analysis of the cellular proteins that interacted with these five viral proteins . Three major functional groups were significantly enriched , including regulation of transcription activities , ubiquitination , and post-translational modification . Together , these results indicate that not only do the viral proteins have similar roles in the viral life cycle , but they also bind to cellular proteins with related functions . Furthermore , it suggests an unexpected role of ubiquitination in the regulation of viral late gene expression . Future studies will focus on the contributions of these pathways to MHV-68 late gene expression .
In this study we identified 22 binary interactions between MHV-68 proteins , including 17 interactions not previously identified in any other gamma-herpesvirus ( Table S1 ) . Though fewer than reported in other studies [21]–[24] , the interactions appear to be of high quality as indicated by greater than 70% confirmation rate of the interactions in co-immunoprecipitation experiments , which compares favorably to confirmation rates of 42 to 53% in previous reports [24] , [23] . The reduced number of interactions is likely due to the combination of a stringent Y2H strain ( PJ69-4 ) and a pair of Y2H vectors ( pDEST22 and pDEST32 ) that tend to yield fewer interactions [52] . Despite the stringent selection conditions in our Y2H screens , we identified a number of novel intra-viral protein interactions , several of which involve MHV-68 tegument proteins . Herpesvirus tegument proteins form a complex matrix located between the nuclear capsid and viral envelope and participate in viral assembly and egress [53] , [54] , [55] . At the center of this sub-network are ORF45 and ORF33 , which strongly and reproducibly interact with each other ( Fig . S1B ) . The homologues of ORF45 and ORF33 were also shown to interact with KSHV ORF64 , a hub protein that recruits other tegument proteins [22] . Our data suggest that ORF45 may act as a scaffold protein that interacts with tegument proteins ORF33 , ORF52 , and ORF67 , and with the envelope protein ORF68 , a homologue of EBV major envelope protein BFLF1 ( Fig . S1B ) . We have previously demonstrated that ORF45 is nearly completely absent from the partially tegumented capsids formed by viruses in which the genes for either ORF52 or ORF33 were disrupted [35] , [56] Together , these results suggest that the recruitment of ORF45 to the capsid depends on interactions with both ORF52 and ORF33 . The recruitment of ORF45 may further assist the virion assembly by interacting with tegument protein ORF67 and envelope protein ORF68 . Further studies are needed to determine if interactions with ORF67 and ORF68 are also required to incorporate ORF45 into capsid or , alternatively , if ORF45 is needed to recruit ORF67 and ORF68 . To identify cellular proteins that interacted with MHV-68 proteins , we screened a well-characterized human liver cDNA library ( [36] and unpublished data ) and confirmed the initial positives under stringent selection conditions . MHV-68 undergoes productive infection in mouse liver in vivo [14]–[16] , and , unlike human gamma-herpesvirus , replicates robustly in various human and mouse cell lines in vitro ( [11]–[13] , and unpublished data ) . Although the liver cDNA library may not include all cellular proteins that interact with MHV-68 , liver tissue contains multiple cell types and expresses a broad range of cellular genes . In addition , many more protein-protein interactions have been reported for human proteins than for mouse proteins , which enabled the viral-cellular protein interactions to be placed in the context of the larger cellular protein interaction network . However , one consequence of using a human cDNA library is that most interactions identified in this study involved MHV-68 proteins that are conserved in human herpesviruses . More than half of the interactions that were positive in retest experiments involved 22 MHV-68 proteins that have homologues in all three herpesvirus families and more than 90% involved 32 MHV-68 proteins conserved in gamma-herpesviruses . The six non-conserved MHV68 proteins in our dataset yielded only 20 interactions ( 8% of the total ) . For comparison , 27% of the EBV-cellular protein interactions involved EBV-specific proteins [24] . Interactions with murine-specific proteins and proteins important for latency will require additional studies to be identified . In total , 243 interactions involving 197 cellular proteins were identified and confirmed in our Y2H screens and retest experiments . Among the most enriched features of the cellular proteins revealed by GO enrichment analyses were terms relating to regulation of apoptosis and regulation of NFκB pathway ( Fig . S6 ) . This observation is consistent with the notion that viruses encode numerous proteins that counteract cellular immune system and prevent the host cell from prematurely dying [57]–[60] . For example , homologues of the cellular anti-apoptotic protein Bcl-2 , including MHV-68 M11 [61] , KSHV ORF16 of [62] and EBV BHRF1 [63] , are found in all gamma-herpesviruses that inhibit p53-induced apoptosis during infection . Interestingly , one of the prioritized proteins , the coiled-coil myosin-like BCL2-interacting protein ( BECN1 ) that interacts with M11 , was independently identified by others to participate in inhibition of apoptosis and autophagy [64] , [65] . Another group of proteins , typified by vFLIP of KSHV and LMP1 of EBV , activate NFκB pathway for cell survival by inhibiting early lytic viral gene expression and maintaining viral latency [66]–[73] . However , as shown in herpes simplex virus , the regulation of NFκB pathways might have distinct roles during different stages of viral infection cycles , promoting replication in the lytic phase and regulating host immune responses during latency [74] . In addition to being over-represented in the entire set of interactions , proteins from these pathways ranked highly on the priority list and significantly affected virus replication in siRNA based functional assays , supporting the crucial role of these pathways in MHV-68 replication . To evaluate the functional roles of the MHV-68-interacting cellular proteins during lytic infection , we used RNA interference and a luciferase-expressing virus . We focused on lytic replication because it is required for the production of viral progeny , is more experimentally accessible than latent infection , and contributes to viral pathogenesis . Tumorigenesis induced by gamma-herpesviruses requires multiple genes expressed during latent as well as lytic replication [10] , [75] , [76] . Lytic replication is also directly involved in the pathogenesis of diseases such as oral hairy leukoplakia ( OHL ) caused by EBV and multicentric Castleman disease ( MCD ) caused by KSHV . Much remains to be understood about the process of lytic replication and the roles of cellular cofactors . Assay conditions were optimized to reveal both positive and negative effects on MHV-68 replication . From a set of 40 human genes that were targeted with two distinct siRNAs , 13 had a significant effect on virus replication . Of these , six genes caused an increase in MHV-68 replication when their expression was inhibited , whereas seven caused a decrease . It is likely the genes that caused in a decrease in MHV-68 replication when their expression was inhibited encode proteins that are required for virus replication and are being exploited by the virus . In contrast , genes that increased replication when their expression was reduced may either play a negative regulatory role in the virus life cycle or may be part of the cellular antiviral defense . Among the cellular proteins that caused an increase in MHV-68 lytic replication when their expression was inhibited was PCBP1 , which interacted with ORF34 . Binding of ORF34 and PCBP1 was confirmed by affinity pull-down experiments and co-localization in mammalian cells , suggesting the interaction occurs in vivo . ORF34 was previously implicated in the regulation of late viral gene expression [48] . Consistent with this phenotype , over-expression of PCBP1 inhibited expression from late viral promoters , but had no effect on early viral promoters . Together , the effects of over- and reduced PCBP1 expression on viral replication and promoter activity suggest that PCBP1 plays a negative role in the MHV-68 life cycle . PCBP1 is a multi-functional protein that has been reported to regulate gene expression at the level of transcription , mRNA splicing , mRNA stability , and translation [77] . Multiple viruses , including other gamma-herpesviruses , exploit PCBP1 to regulate of translation , generally in an IRES-dependent manner [78]–[83] . Because no known IRES sequences were present in the promoter constructs , it is unlikely that the effect of PCBP1 on MHV-68 late promoters was at the level of translation . Similarly , since both the early and late viral promoter constructs shared a common 3′ UTR and none encoded splice sites , it is unlikely that PCBP1 altered mRNA stability ( which typically occurs through binding to RNA sequences in the 3′ UTR ) or splicing of the reporter gene . Rather , we propose that PCBP1 negatively regulated the transcription of late MHV-68 gene . However , it remains to be determined whether the inhibitory role of PCBP1 is by directly binding to the viral DNA or by indirectly modulating cellular gene expression . Regardless of the exact mechanism , these results illustrate the potential for the MHV-68-cellular protein interactome to reveal novel and interesting aspects of virus-cellular protein interactions . Network analyses of the MHV-68-cellular protein interactome indicated that the cellular proteins that bound to MHV-68 proteins tended to be closer to each other in the reference cellular protein interaction network than expected by chance . We exploited this observation to develop a method to prioritize the cellular proteins from the MHV-68-cellular protein interactome for further characterization . Cellular proteins that interacted with an MHV-68 protein and that were located near other cellular proteins that also bound to MHV-68 proteins were given a positive score , with the magnitude corresponding to the number of MHV-68-interacting proteins located nearby . In essence , this approach identified network neighborhoods that contained multiple cellular proteins targeted by MHV-68 . It is important to note that this scoring method is not equivalent to the number of cellular binding proteins ( degree ) of the MHV-68-interacting proteins . Although the cellular proteins that interacted with MHV-68 proteins participated in more intra-cellular interactions than the average protein in the cellular protein interaction network , the distribution of network distances between MHV-68 interacting proteins was only partially dependent on degree . Similarly , the scoring method is not equivalent to the list of genes bearing enriched GO terms . Although the highly scoring proteins are enriched in several GO terms , particularly those related to cell survival and kinase function , not all proteins with these GO terms received high scores . Thus , the scoring method provides information not obtained from standard network or term enrichment analyses . Proteins from the MHV-68-cellular protein interactome that scored highly in this scheme were much more likely to affect MHV-68 lytic replication than proteins with a low score or proteins randomly chosen from the cellular protein interaction network that did not interact with MHV-68 proteins ( 53% vs . 21% or 16% , respectively ) . A preliminary screen of the 60 highest scoring proteins using a single siRNA per gene yielded comparable results , with slightly more than half the siRNAs causing a significant change in MHV-68 replication . However , the magnitude of the effect on virus replication did not correlate with the score , though this could be due to differences in the effectiveness of the siRNAs in reducing expression of the cellular proteins ( Fig . S6 ) . A second caveat is that the scoring system will miss some important proteins in the MHV-68-cellular protein interactome , as indicated by the fact that several low or non-scoring also affected MHV-68 replication . The prioritization approach is also unable to identify cellular proteins that do not bind to MHV-68 but that affect MHV-68 replication indirectly , unless those proteins are located in network neighborhoods target by multiple MHV-68 interactions; genome-wide siRNA screens will be needed to systematically identify cellular cofactors that indirectly impact MHV-68 . Since functionally related proteins cluster together in protein interaction networks [84] , [85] an implication of our results is that MHV-68 targets particular cellular processes or pathways through multiple interactions with viral proteins . Consistent with this observation , specific GO terms were enriched in this and other virus-cellular protein interaction networks , which can only occur if multiple functionally related proteins are present . The fact that viruses devote valuable resources to interacting with multiple proteins involved in the same or related functional process suggests that these pathways or process are particularly important for successful replication . However , rather than conferring redundancy to the virus , network neighborhoods that have multiple links to viral proteins appear to represent regions of vulnerability since inhibiting expression of these cellular proteins was more likely to affect virus replication . A similar pattern appears to exist among cellular proteins that bind to HIV-1 proteins [86] . In particular , MacPherson and collaborators recently reported that known HIV-1 host factors are functionally and physically linked clusters within the host cells [86] . Human proteins that interact with HIV-1 can be grouped in a limited number of functionally and physically linked clusters within the host cells [86] . Thus , targeting multiple functionally related proteins may be a general theme in virus-cellular protein interaction networks . In contrast to the screen for virus-virus protein interactions , our screen for cellular proteins that interacted with MHV-68 proteins identified more interactions than a similar study with EBV proteins ( 243 versus 173 ) [24] . Although no interactions between homologous viral proteins and the same host factor were found , six cellular proteins were reported in both data sets , which represents a small but statistically significant overlap ( p-value = 4 . 0×10−3 ) [24] . The high false-negative rates of Y2H screens in addition to the use of different versions of the Y2H assay , different cDNA libraries , and different criteria for selecting and confirming true-positive interactions likely contributed to the low overlap . However , this should not be taken to imply that MHV-68 and EBV target completely different sets of cellular proteins , as we expect that direct comparison in pair wise Y2H or co-purification experiments would reveal substantial overlap . Furthermore , our analyses suggest that MHV-68 and EBV target cellular proteins located in the same region of the cellular protein interaction network . The average distance between cellular proteins that interacted with MHV-68 and EBV proteins was significantly closer than the average distance between an equivalently sized set of randomly chosen proteins ( MHV-68 to EBV vs . random = 4 . 67 vs . 5 . 31 , p-value: 3 . 0×10−5 ) . The same comparisons of the average distance between cellular proteins that interacted MHV-68 ( or EBV ) and influenza virus ( Flu ) revealed no significant differences compared to randomly chosen cellular proteins ( MHV-68 to Flu: 4 . 98; EBV to Flu; 5 . 05 ) , suggesting that MHV-68 and EBV target cellular proteins located in the same region of the cellular protein interaction network and that the network proximity of the virus-targeted proteins was specific to herpeviruses [24] , [87] . Consistent with this hypothesis , several GO terms , including regulation of apoptosis , I-kappaB kinase/NFκB cascade , and ubiquitin-protein ligase activity , were significantly enriched in both datasets ( Fig . S4B ) . Thus , although the MHV-68 and EBV data sets are undoubtedly incomplete , that MHV-68 and EBV appear to be interacting with similar pathways , but utilizing different proteins to do so . An intriguing implication of this hypothesis is that integrating the gamma-herpesvirus-cellular protein interaction networks may compensate the low coverage of each screen and may reveal important targets missed when the analyses are performed with individual data sets . Indeed , merging the datasets increased the significance of the priority scores of cellular factors that interacted with either MHV-68 or EBV . This effect was much stronger for the smaller of the sets ( EBV; Table S4 ) suggesting the use of composite sets of cellular factors when evaluating newly identified sets of host factors of related viral species may be a valuable approach . A similar strategy has been proposed for evaluating intra-viral protein interaction networks [23] . Individual herpesviruses have co-evolved with their hosts , adopting diverse strategies to evade the host immune system and to hijack existing cellular machinery [5] , [8] , [88] , [89] . Our study adds to the growing list of gamma-herpesvirus protein-protein interactions and provides additional insight into the complexity of herpesvirus-host cell interactions . The interactions identified here revealed cellular genes that significantly impacted MHV-68 replication and suggest numerous hypotheses about MHV-68 pathogenesis that can be explored in future studies . In addition , we devised a novel strategy to prioritize cellular binding partners of viral proteins for in depth analyses . This nontraditional approach enabled rapid identification of several cellular proteins that negatively or positively affected MHV-68 replication and provides a valuable method for extracting biologically relevant interactions from virus-host protein interaction networks . Finally , this dataset constitutes a valuable resource for comparative studies of the replication strategies of gamma-herpesviruses . As herpesviruses encode a core of conserved genes , such comparative analyses have the potential to reveal cellular proteins and pathways utilized by multiple herpesviruses that may serve as new targets for therapeutic intervention .
MHV-68 open reading frames ( ORFs ) were PCR amplified from the MHV-68 viral DNA and cloned into pENTR vectors by using Gateway pENTR/D/TOPO cloning kit ( Invitrogen ) . Total of 84 MHV-68 ORF pENTR clones , including 64 full length and 14 fragmented viral ORF clones for ORF larger than 1 . 5 kb , and 6 ORF clones without trans-membrane domain , were generated . Inserts in entry clones were sequence verified and transferred to destination vectors for expression in yeast and mammalian system using Gateway LR Clonase enzyme mix ( Invitrogen ) suitable for N-terminus tagging . Destination clones , pTAG and pHB vectors , were constructed by adding FLAG/Calmodulin Binding Peptide ( CBP ) and V5/HIS fusion tag respectively for co-immunoprecipitation ( Co-IP ) . pYFP and pRFP vectors were constructed by adding the genes encoding yellow fluorescent protein and Discosoma sp . red fluorescent protein ( DsRed ) respectively for immunofluorescence assay ( IFA ) . Recombination of inserts from pENTR clones results in an in frame fusion at the 3′ end of the fusion epitope tag or fluorescent tag . DNA-binding domain strains were mated to PJ69-4a cells transformed with the parental activation domain plasmid pDEST32 . Diploid yeast were selected on synthetic drop-out medium ( SD ) lacking tryptophan and leucine supplemented with 0 . 003% adenine ( SD–LEU–TRP+ADE ) and plated on SD medium lacking leucine , tryptophan , and histidine ( SD–LEU–TRP–HIS ) and containing 0 , 1 , 3 , 5 , 10 , 20 , 50 , 75 , 100 , 125 , and 150 mM 3-amino-1 , 2 , 4-triazole ( 3-AT , a competitive inhibitor of His3 ) . Y2H screens were performed using the lowest concentration of 3-AT that suppressed yeast growth in the absence of an interacting activation domain fusion ( 1 or 3 mM for most DNA-binding domain constructs ) . PJ69-4a cells expressing MHV-68 activation domain fusions were arrayed in 96-spot format on SD–LEU–TRP+ADE medium and converted to 384-spot format using a Biomek FX robot ( Beckman Coulter , Brea , CA ) equipped with 96 pin replicating head; each strain was represented four times in the 384-spot format . Y2H assays were performed by replica pinning the activation domain clones to YPDA ( yeast extract-peptone-dextrose plus 0 . 003% adenine ) medium and then pinning a single DNA-binding-domain clone on each spot . Diploid yeast containing the DNA-binding domain and activation domain plasmids were selected on SD–LEU–TRP+ADE and replica-pinned onto two independent Y2H selection media: ( i ) SD–LEU–TRP-HIS supplemented with the optimal 3-AT concentration as determined above; and ( ii ) SD–LEU–TRP−ADE . Yeast growth was assessed after 7 to 14 days at 30 C . Pairs of activation and DNA-binding domain fusions that stimulated yeast growth on both Y2H selection media in at least three spots were judged to be true positives . Y2H screens were performed by mating as described by [92] with the following modifications: Mid log phase R2HMet cells ( 1×107 cfu ) containing a pXDGATcy86 clone were mixed with 5×106 cfu of the human liver activation domain library in YPD ( yeast extract-peptone-dextrose medium ) , pH 3 . 5 , and incubated 1 h with rotation , at which point the media was replaced with 3 ml of YPDA , pH 4 . 5 . The cells were collected by centrifugation and incubated overnight at room temperature to allow mating . Mating efficiency was estimated by plating an aliquot of each screen on SD–LEU–TRP+ADE , which selected for diploid yeast containing both Y2H plasmids; typical yields were 3–5×105 diploids per screen . The remaining yeast were collected by centrifugation , washed once with dH2O , and plated on SD–LEU–TRP-HIS supplemented with the optimal 3-AT concentration as determined above . Plates were incubated at 30°C until colonies appeared ( 4 to 10 days ) . All DNA-binding domain clones were screened at least twice . Yeast colonies that grew on SD–LEU–TRP-HIS+3-AT ( up to 48 per screen ) were picked and grown overnight in liquid YPDA medium . The human gene insert in pOAD . 103 was then PCR-amplified with primers 5′-CGACGACGAGGATACGCCACCGAAC-3′ , and 5′-GAGCTTCGCAGCAACCGGACTAGGA-3′ and sequenced from the 5′ end with primer 5′-ATACGCCACCGAACCCTAAGAAA-3′ . The gene identity was assigned by querying the human RefSeq database ( downloaded 3/4/08 ) using Cross Match . PCR products of activation domain inserts from unique interactions identified in the Y2H library screens were re-cloned into pOAD . 103 by in vivo homologous recombination in the yeast strain BK100 [93] . Two independent activation domain inserts were re-cloned for interactions that were identified more than once in the library screens . Plasmids were verified by PCR and sequencing . Yeast containing the remade activation domain clones were spotted on solid SD–LEU–TRP+ADE medium in 96-spot format with a BioMek FX robot . Each spot was then transferred to solid SD–LEU–URA+ADE medium in quadruplicate to create a 384-spot array . Strains expressing the DNA-binding domain fusions were grown to mid log phase in SD–TRP+ADE medium , collected by centrifugation , resuspended in fresh media and pinned onto solid YPDA medium in 384-spot format corresponding to the activation domain clone array described above . The activation domain array was then replica pinned onto the same plates . After 2 days at 30°C , yeast were replica pinned onto solid SD–LEU–TRP+ADE medium to select for diploid yeast . Plates were incubated 3 days at 30°C and the yeast replica pinned onto three plates: ( i ) SD–LEU–URA–TRP-HIS containing the minimum concentration of 3-AT to suppress background growth; ( ii ) SD–LEU–URA–TRP–HIS containing 3-AT at a concentration one step above minimum level required to suppress background growth; and ( iii ) SD–LEU–URA–TRP–ADE . Plates were incubated 7 days at 30 C and imaged at days 3 and 7 . Interactions were scored as positive if at least 3 of the 4 spots for each interaction displayed growth above background growth of in-plate negative controls on both media lacking HIS and ADE . Human embryonic kidney ( HEK ) -293T ( 293T ) cells and NIH/3T3 ( 3T3 ) cell lines were both maintained at 37 °C in 5% CO2 atmosphere in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum ( 293T ) or bovine calf serum ( 3T3 ) and 1% penicillin and streptomycin . Anti-FLAG-M2 was purchased from Sigma . Anti-V5 , secondary fluorescent antibodies ( Alexa Fluor 594 goat anti-mouse IgG , Alexa Fluor 488 goat anti-mouse IgG ) were purchased from Invitrogen . Protein samples were resolved by SDS-PAGE on 12 . 5% or 15% polyacrylamide gels and transferred to polyvinylidene fluoride membrane . Blots were blocked overnight in 5% nonfat dried milk in phosphate-buffered paline ( PBS ) with 0 . 2% Tween 20 ( PBST ) and probed with anti-V5 ( 1∶5000 ) or anti-FLAG-M2 ( 1∶5000 ) followed by horseradish peroxidase ( HRP ) conjugated rabbit anti-mouse IgG secondary antibody ( 1∶5000 ) or with HRP conjugated primary antibody , anti-Flag-HRP ( 1∶5000 ) and anti-V5-HRP ( 1∶5000 ) . Blots were imaged using the ECL detection kit ( GE Healthcare ) according to manufacturer's instructions . Corresponding FLAG ( pTAG ) , V5 ( pHB ) tagged , GFP , YPF , or RFP cDNA expression clones for protein-protein interactions of interest were co-transfected into 20 million 3T3 cells by using BioT reagent according to manufacturer's instructions in 96 wells plate . At 24–28 hours post-transfection , cells were washed with 100 µl of cold Phosphate-buffered Saline ( PBS ) and fixed with 50 µl cold methanol on ice for 15 minutes . Fixative was aspirated and cells were washed with 150 µl cold PBS four times and blocked with 100 µl blocking buffer ( 1× PBS , 10% Fetal Bovine Serum , 1% Bovine Serum Albumin and 0 . 1% Triton-X 100 ) for 30 minutes at room temperature . Fifty µl of diluted primary antibody was added to each well and incubated for one hour at room temperature . Anti-FLAG-M2 antibodies and anti-V5 antibodies were used for detection of pTAG and pHB expression clones . Cells were washed with cold PBS four times and incubated with 50 µl of appropriate fluorescent-conjugated secondary antibody for another hour . DAPI ( Invitrogen , Carlsbad , CA ) were used for nuclease stain for 15 minutes at room temperature . Predesigned siRNAs were purchased from Qiagen . The siRNA sequences were designed by Qiagen using BioPredsi neural-network based on a large number of siRNA datasets , which has been shown to have about 70% knockdown efficiency on targeted transcripts [94]–[97] . SiGL3 and Negative Control siRNA were used as controls ( Qiagen ) . Cellular cDNA expression clones were PCR amplified from revered-transcribed cDNA from 293T and cloned into Gateway system as previous described . Two pmol of siRNA or 200 ng of cDNA expression clones ( pTAG or pHB ) were reverse-transfected into 293T cells with Lipofectamine 2000 ( Invitrogen ) according to manufacturer's instructions in 96 well format and infected with M3-Luc-MHV-68 at 48 h ( siRNA ) or 24 h ( cDNA ) post-transfection . The M3-Luc MHV-68 reporter virus was constructed by inserting the firefly luciferase gene downstream of the lytic gene M3 promoter ( constructed by Dr . Seungmin Hwang ) . Fifty µl of supernatant containing infectious viral particles from infected cells was carefully transferred to fresh 293T cells 50 h post-infection . Cell extract for luciferase activity assay was prepared by adding 60 µl of Bright-glo buffer ( Promega ) to each well 20 h post-transfer . Transfections were performed in triplicate and duplicate readings of luciferase activity were obtained ( Molecular Devices ) . Cytotoxicity assays were performed on siRNA-transfected cells that were mock-infected at 48 h post-siRNA transfection . ATP levels were measured at 50 h post-mock infection with ATPLite Luminescence ATP Detection Assay System ( PerkinElmer ) according to the manufacturer's protocol for a 96-well microplate . To be considered for this analysis , we required that the siRNA treatment resulted in at least 60% of ATP level compare to the negative controls and that the measurements had less than 30% of the standard deviation ( SD ) within the triplicate of each siRNA treatments . The average signals of the triplicate non-specific siRNA treatments were similar to the average to the median of each plate . To avoid plate-to-plate variation , the averages of luciferase activities of the triplicate measurements were normalized to the median of all the readings on the same plate which is close to the average of the negative control . Treatments that increased the luciferase levels to ≥the median plus two times the standard deviation within triplicates of negative controls , which corresponds to plus one Log2 fold , were considered to have enhanced MHV-68 replication . Similarly , treatments that reduced luciferase levels to ≤the median minus two times the standard deviation within triplicates of negative controls , which corresponds to minus one Log2 fold , were considered to have inhibited virus replication . Viral promoter reporter plasmids were constructed in which the firefly luciferase gene was linked to either early ( M3 and ORF57 ) or late ( M9 and ORF26 ) viral gene promoters . HEK293T cells were transfected with early viral promoter-firefly luciferase reporter plasmids with cDNA expression construct ( pHB/PCBP1 ) or vector alone ( pHB/attR ) with/without pCMV/RTA co-transfection . Late viral promoter-firefly luciferase reporter plasmids were co-transfected with cDNA expression construct ( pHB/PCBP1 ) or vector alone ( pHB/attR ) followed by MHV-68 ( MOI:0 . 5 ) infection at 24 h post-transfection in 293T cells . A CMV-driven Renilla luciferase plasmid was co-transfected with all of the transfection combinations to serve as a control for transfection efficiency . Cells were lysed with 100 µl Passive Lysis Buffer ( Promega ) at 24 h post-transfection for early promoters assays , and at 24 h post-infection for late viral promoter assays . Lysates were assayed for firefly and Renilla luciferase activity using Dual-Luciferase Reporter assay kit ( Promega ) flowing the manufacture protocol . The reference protein-protein interaction network was constructed by merging binary interactions reported in DIP [39] , IntAct [98] and MINT [99] databases; common curation rules adopted by these databases within IMEx Consortium [100] ensure consistent annotation of the experiments and similar level of quality control . Only direct and physical interactions supported by at least one small-scale experiment , defined operationally as these reported in a paper describing less than 100 independent experiments were included . Binary , physical interactions inferred from the multi-protein complexes according to the spoke model ( i . e . bait-prey pairs ) were included only for complexes composed of less than 30 proteins . Alternative forms of the proteins coded by the same gene were merged into one node within the network . The relatively stringent criteria used here resulted in a reference human protein-protein interaction network composed of 4280 proteins connected by 8939 interactions . Whereas of only moderate size when compared to interaction networks that include the results of large-scale interaction screens and computational predictions , the reference network constructed here represents a subset of the currently known human interactome that is supported by a wide array of independent experiments reported in more than 3 , 200 publications . It thus attempts to minimize experimental and computational bias possible when constructing interaction sets based predominantly on the computationally processed results of a small number of high throughput experiments and computational protein interaction predictions . Distances between two proteins within the reference interaction network were calculated as the length of the shortest path connecting the corresponding network vertices with each edge of the network assigned a weight of 1 . Disconnected pairs of proteins were excluded from the distance analysis . The distribution of the pair-wise network distances , calculated as the length of the shortest path between the cellular proteins identified in the Y2H screen , was constructed by taking into consideration every protein pair connected within the reference network described above . It was compared to the distribution of distances between equivalent numbers of proteins randomly selected from the reference network . Statistical significance of the differences in the distribution shape was evaluated by Monte-Carlo method . To this end distribution of the chi-square statistic:where ρ ( dN ) is the frequency of observing dN distance , was calculated for 104–105 random sets of cellular factors and subsequently to used to estimate p-value as p ( C2>C2obs ) . In order to prioritize cellular factors initially identified in the Y2H screen , a simple score was calculated by counting , for each cellular factor , the number of other cellular factors located not farther than four interactions away within the reference human protein interaction network ( see Fig . 4A ) . The significance of the priority scores was estimated by Monte-Carlo method as described above . All the interactomes were generated by an open sources plateform for network analysis and visualization software , Cytoscape ( http://cytoscape . org/index . php ) . GO terms analysis generated using the BinGO [101] plugin within Cytoscape . The functional annotations of cellular proteins were analyzed together with their immediate neighbors in the cellular protein inteaction reference network under the GO background of Homo Sapiens . GO terms with specific functional annotation with corrected p- value less than 1×10−3 were selected . The set of interactions reported in this paper has been submitted directly to the DIP database and assigned IMEx Consortium ( http://www . imexconsortium . org ) IM-15822 accession number . | Persistent infections by the herpesviruses Epstein Barr virus ( EBV ) and Kaposi's sarcoma herpesvirus ( KSHV ) are associated with tumor formation . To better understand how these and other related viruses interact with their host cells to promote virus replication and cause disease , we studied murine gamma-herpesvirus 68 ( MHV-68 ) . MHV-68 belongs to the same group of herpesviruses as EBV and KSHV , but has the advantage of being able to replicate efficiently in cell culture . Our study used genome-wide screens to identify 23 protein-protein interactions between the 80 MHV-68 proteins . Several of these interactions are likely to be important for assembling new viruses . We also discovered 243 interactions between MHV-68 and cellular proteins . To help prioritize cellular proteins for follow up studies , we developed a new computational tool to analyze our data . Proteins with high priority scores were more likely to affect viral replication than low priority proteins . Among the cellular proteins that had the greatest effect on MHV-68 replication was PCBP1 , which negatively regulated MHV-68 late gene expression . This study identified many novel cellular proteins involved in MHV-68 replication and established a method to identify important proteins from high-throughput virus-cellular protein-protein interaction data sets . | [
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] | 2011 | An Integrated Approach to Elucidate the Intra-Viral and Viral-Cellular Protein Interaction Networks of a Gamma-Herpesvirus |
The early systemic production of interferon ( IFN ) -αβ is an essential component of the antiviral host defense mechanisms , but is also thought to contribute to the toxic side effects accompanying gene therapy with adenoviral vectors . Here we investigated the IFN-αβ response to human adenoviruses ( Ads ) in mice . By comparing the responses of normal , myeloid ( m ) DC- and plasmacytoid ( p ) DC-depleted mice and by measuring IFN-αβ mRNA expression in different organs and cells types , we show that in vivo , Ads elicit strong and rapid IFN-αβ production , almost exclusively in splenic mDCs . Using knockout mice , various strains of Ads ( wild type , mutant and UV-inactivated ) and MAP kinase inhibitors , we demonstrate that the Ad-induced IFN-αβ response does not require Toll-like receptors ( TLR ) , known cytosolic sensors of RNA ( RIG-I/MDA-5 ) and DNA ( DAI ) recognition and interferon regulatory factor ( IRF ) -3 , but is dependent on viral endosomal escape , signaling via the MAP kinase SAPK/JNK and IRF-7 . Furthermore , we show that Ads induce IFN-αβ and IL-6 in vivo by distinct pathways and confirm that IFN-αβ positively regulates the IL-6 response . Finally , by measuring TNF-α responses to LPS in Ad-infected wild type and IFN-αβR−/− mice , we show that IFN-αβ is the key mediator of Ad-induced hypersensitivity to LPS . These findings indicate that , like endosomal TLR signaling in pDCs , TLR-independent virus recognition in splenic mDCs can also produce a robust early IFN-αβ response , which is responsible for the bulk of IFN-αβ production induced by adenovirus in vivo . The signaling requirements are different from known TLR-dependent or cytosolic IFN-αβ induction mechanisms and suggest a novel cytosolic viral induction pathway . The hypersensitivity to components of the microbial flora and invading pathogens may in part explain the toxic side effects of adenoviral gene therapy and contribute to the pathogenesis of adenoviral disease .
Adenoviruses ( Ads ) cause mild disease in humans , but are hazardous pathogens in immuno-compromised individuals [1] . Human Ads are dsDNA viruses grouped into six species . Species A , C , D , E , and F and species B Ads use different infectious entry pathways [2] . Human Ads enter mouse cells and express their early genes; however , the virus genome is not replicated and no viral progeny is made during the infection of mouse cells in vitro or in vivo [3] , [4] . Furthermore , early viral gene expression can be abolished by UV-inactivation and well-defined mutants with defects of viral early genes or viral entry are available [3] , [5] . Thus , the effects elicited by different components of the virus-host interaction preceding viral replication can be accurately evaluated . Ads transduce many different cell types and can be produced in vitro in sufficiently high amounts for in vivo administration . While these properties make them attractive for gene therapy applications , they can also trigger a severe systemic toxic reaction [6] , [7] . Upregulation of inflammatory mediators , including cytokines and chemokines such as IL-1 , IL-6 , IL-8 , IL-12 , macrophage inhibitory protein-1/2 , tumor necrosis factor-α ( TNF ) and recently also type I IFN has been observed in experimental and clinical infections with wt as well as with recombinant Ads [6] , [7] , [8] , [9] , [10] . Type I IFNs represent one of the host's most important antiviral defense mechanisms . The type I IFN family comprises different IFN-α subtypes , a single IFN-β and other less well characterized proteins [11] . All IFN-α species and IFN-β interact with the same IFN-αβ cellular receptor , the activation of which mediates a wide range of direct and indirect innate antiviral or antimicrobial effects and modulates the antiviral adaptive immune response [12] , [13] , [14] . At present , two main mechanisms of type I IFN induction by viruses resulting from the extracytoplasmic or cytoplasmic virus recognition , respectively , are known [12] , [13] , [14] , [15] . The extracytoplasmic induction is initiated by triggering the surface-expressed transmembrane protein toll-like receptor ( TLR ) 4 with certain non-nucleic viral constituents [16] , [17] , [18] or upon recognition of viral nucleic acids in the endosomes of specialized cells ( dendritic cells and macrophages ) via different members of the TLR family . These include TLR3 , TLR7/TLR8 and TLR9 , sensing dsRNA , ssRNA and CpG DNA , respectively . For IFN-αβ induction TLR3 and TLR4 signal through the adaptor molecule TIR domain-containing adaptor protein inducing interferon β ( TRIF ) . This results in the activation of interferon regulatory factor ( IRF ) -3 . TLR7 , 8 and 9 signal through the adaptor molecule Myeloid differentiation factor 88 ( MyD88 ) . An important result of the MyD88-mediated pathway is the activation of IRF-7 ( but not of IRF-3 ) , which together with the transcription factors NF-kB and AP-1 initiates the induction of both the IFN-α and IFN-β genes [13] , [15] . This induction pathway is responsible for the strong , early IFN-αβ response to several replicating and inactivated viruses in pDCs , which express preferentially TLR7 and TLR9 [19] , [20] , [21] . The “classical” cytosolic pathway is the major IFN-αβ producing mechanism in cells other than pDCs [22] , [23] . Signal transduction leading to type I IFN gene induction is initiated by the recognition of intracellular virus-associated molecular patterns . dsRNA and 5′-triphosphate RNA produced during viral replication are sensed by the RNA helicases RIG-I and MDA-5 [23] , [24] , [25] , [26] , [27] , [28] . This pathway has been extensively studied , mainly in virus-infected fibroblasts . Triggering of the aforementioned RNA helicases leads to the activation of the transcription factors NF-kB , IRF-3 and IRF-7 that are important for the induction of IFN-αβ and proinflammatory cytokines , including IL-6 . In the cytosolic pathway , type I IFN gene induction is a sequential event and both IRF-3 and IRF-7 were shown to be important in the early phase when mostly IFN-β is produced . The late phase of the IFN-αβ response is regulated by positive feedback via the increased levels of IRF-7 elicited by IFN-β production during the early phase [29] , [30] , [31] . In addition to fibroblasts , the potential of mDCs and macrophages to produce significant amounts of type I IFNs in response to viral replication has been demonstrated in vitro [32] , [33]; however , in vivo the specific contribution of these cells to systemic levels of IFN-αβ is not well documented . Recently , detection of bacterial DNA in cells infected with L . monocytogenes and recognition of transfected B-DNA has been shown to trigger IFN-β production . This type of response strictly requires IRF-3 [15] , [34] , [35] , [36] , [37] , [38] . Such a sensing system has been suggested to represent a further mechanism of cytosolic DNA virus recognition [14] , [15] and the Z-DNA binding protein 1 ( Zbp1 , also referred as DNA-dependent activator of IFN regulatory factors , DAI ) was shown to be a candidate DNA sensor in this pathway [39] . Notably however , in a follow-up study the same group found a critical role for DAI in L-929 cells but not in mouse embryonic fibroblasts ( MEFs ) [40] . Furthermore , recent experiments with Zbp1/DAI knockout mice did not show the essential role of Zbp1/DAI in the induction of innate and adaptive responses to B-DNA in vivo and in macrophages , dendritic cells and MEFs in vitro [41] . The induction of type I IFN in Ad-infected mice has been recently studied [10] , [42] and associated with both the extracytoplasmic and intracytoplasmic pathways . It was claimed that a part of the IFN-αβ response is initiated by TLR9 and MyD88 signaling in pDCs and another part by cytosolic DNA recognition in non-pDCs . However , the identification of IFN-αβ producing cell types directly in infected mice was not carried out . In the present study we investigated the IFN-αβ responses of Ad-infected mice and showed that the bulk of the in vivo induced IFN-αβ is produced by splenic mDCs . Furthermore , we found that TLRs , including TLR9 play no major role . The Ad-elicited IFN-αβ response required viral endosomal escape , suggesting a cytosolic induction pathway . Surprisingly however , the induction was independent of IRF-3 and dependent on stress-activated protein kinase/c-Jun NH2-terminal kinase ( SAPK/JNK ) activity , which is in contrast to the known induction mechanism initiated through cytosolic DNA recognition . Instead , the induction required IRF-7 , and a positive feedback regulation via the type I IFN receptor . Although this does not exclude a role for cytosolic nucleic acid sensors , our data do not support the involvement of MDA-5 , RIG-I and Zbp1/DAI in the induction of the IFN-αβ response to Ad . Furthermore , our results reveal distinct mechanisms in the induction of IFN-αβ and IL-6 by Ad . Finally , we show that Ad-induced IFN-αβ is a key mediator of hypersensitivity to bacterial lipopolysaccharides in infected mice . Enhanced susceptibility to LPS and to other microbial inducers of inflammation may contribute to toxic reactions observed during adenoviral gene therapy and to the clinical symptoms of adenoviral diseases .
In order to characterize the induction of type I IFNs by Ad in vivo , we first examined how two types of human Ad , Ad3 ( species B ) and Ad R700 ( species C ) [2] known to use distinct infectious entry routes , elicit an IFN-αβ response in vivo . The results summarized in Fig . 1A and supplementary Fig . S1A show that all mice infected with either of the two viruses exhibited similar IFN-αβ responses . IFN-αβ was first detectable in plasma at 4 h , peaking at 8 h and declining to low levels 18 h after infection . We then investigated , whether the expression of viral genes is required and/or regulate the induction of IFN-αβ by Ads . To this end , we injected mice with an UV-inactivated Ad3 ( Fig . S1B ) or Ad R700 , incapable of viral gene expression , or with a recombinant Ad5 ( species C ) that contains a deletion of the E1 and E3 Ad early regions and expresses GFP ( Ad5-GFP ) ( Fig . S1C ) . As shown in Fig . 1A , UV-inactivated Ads also induced a strong IFN-αβ response which , however , peaked at 6 h after injection , i . e . 2 h earlier than the response to intact Ads . A similar early-peaking IFN-αβ response was obtained in mice injected with UV inactivated Ad R700 ( Fig . S2A ) and with the recombinant Ad5-GFP ( Fig . S2B ) . Titration of the viral preparations in mice revealed that a positive correlation between the viral dose and the height of the IFN-αβ response existed only when relatively small doses of intact Ads were used . Higher doses of the Ads either did not elicit a further increase of the IFN-αβ response ( Ad3 , Fig . 1B ) or led to a decrease of the response ( Ad R700 , Fig . S2C ) . In contrast , injection of the corresponding UV-inactivated Ads always led to a gradual increase of the IFN-αβ response and , at higher viral concentrations , even exceeded the response obtained with intact viruses . Thus , the expression of Ad genes is not required for the induction of IFN-αβ in mice . The data also indicate that the expression of early adenoviral genes negatively regulates type I IFN production . Notably , the Ad E1A gene has been shown previously to suppress Newcastle Disease Virus and IRF-3 induced IFN-α4 promoter induction in transient expression assays in fibroblasts [43] . Interestingly , however , viral gene expression did not inhibit the production of IL-6 in either Ad3- or Ad R700-infected mice ( Fig . 1C and Fig . S2D ) . Further experiments revealed that , in contrast to the UV-inactivated Ad , heat-inactivated Ad did not elicit IFN-αβ in mice ( data not shown ) . Since heat inactivation prevents the entry of Ad into cells ( [44] and Fig . S2E ) , we conclude that signal transduction leading to IFN-αβ production is activated during Ad entry . Previous studies have shown that , depending on the inducing virus , IFN-αβ is produced ubiquitously or in a cell type specific manner [13] , [14] . Here we stimulated different primary mouse cells , including bone marrow derived mDCs ( BMDC ) , bone marrow derived macrophages ( BMM ) , bone marrow derived pDCs and mouse embryonic fibroblasts ( MEFs ) , with Ad2 ( species C ) or Ad3 ( species B ) . Six hours later , the IFN-αβ content of culture supernatants and the expression of the viral E1A gene in the cells were determined . BMDC and BMM , but not MEFs , produced IFN-αβ ( Fig . 1D ) , although all cell types were successfully infected as shown by RT-PCR ( Fig . 1E and not shown ) . Very similar results were obtained when , instead of Ad2 , Ad3 or the recombinant Ad5-GFP were used for infection of the three cell types ( not shown ) . In addition , pDCs also produced IFN-αβ in response to Ad infection in vitro ( Fig . S3A ) ; however , only at high multiplicities of infection . Finally , like MEFs , L-929 cells infected with Ad2 produced no IFN-αβ either ( Fig . S3B ) . In agreement with previous studies [36] , [37] , MEFs and L-929 cells produced IFN-αβ following transfection with purified DNA ( Fig . 1D and Fig . S10C ) . In addition to mouse DCs and macrophages we also found that human monocyte-derived DCs produced IFN-αβ upon infection with the adenoviral vector Ad5-GFP ( Fig . S3C ) . The present data confirm that Ads can trigger IFN-αβ production in various immune cells in vitro [10] , [45] , [46] . However , it furthermore indicates that production does not proceed ubiquitously in all types of infected cells . In order to identify the organ site of viral uptake and IFN-αβ production in vivo , we analyzed the expression of the early viral gene E1A and of type I IFN mRNAs , respectively , in the spleen , liver , lung and kidney of Ad3-infected mice . We found expression of the early viral E1A gene in spleen and liver ( Fig . S1B ) , but not in lung and kidney ( not shown ) , which agrees with earlier findings on in vivo Ad tropism [47] , [48] . Expression of IFN-α and IFN-β mRNA was below the level of detection in the organs of non-infected controls . We also found that between 4 and 18 h after infection IFN-α and IFN-β mRNAs were expressed at high levels in the spleen ( Fig . 1F ) , but surprisingly not in the liver , despite the presence of Ad in both organs . As expected , IFN-αβ was not expressed in the virus-free lung or kidney of infected mice ( Fig . 1F ) . In order to identify the cell type ( s ) producing IFN-αβ in vivo , we isolated splenocytes from Ad infected animals 8 h after virus treatment and separated them into CD11c+ ( DC-containing ) and CD11c− ( non-DC ) populations . Both CD11c+ and CD11c− populations contained viral DNA ( not shown ) . This finding is in accordance with the report of Morelli et al describing that both splenic DC and non-DC contain the virus in Ad-infected mice [49] . However , quantitative RT-PCR determination of IFN-α and IFN-β mRNA in both populations revealed the presence of IFN-αβ mRNAs predominantly in the DC-containing CD11c+ fraction ( Fig . 2A , B ) , but not in the macrophage containing CD11c− fraction . In contrast to IFN-α and IFN-β , the mRNA levels of IL-6 , another cytokine known to be induced by Ad , were comparably upregulated in both the CD11c+ and CD11c− population ( Fig . 2C ) . Since the latter non-DC population comprises more than 95% of all mouse splenocytes [50] , we conclude that most of the IL-6 made in the Ad-infected spleen is of non-DC origin . According to published data , the CD11c+ cell population contains different types of DCs , as well as other cells such as lymphocytes , macrophages , granulocytes and NK cells [51] , [52] , [53] . We therefore further separated the purified CD11c+ cells into mDCs ( CD11c+CD11b+Gr1− ) , pDCs ( CD11c+CD11b−GR1+B220+ ) and a CD11c+CD11b-F4/80+ subpopulation ( Fig . S4A–D ) and measured the expression of IFN-α , IFN-β and β-actin with real-time RT-PCR . After normalization to β-actin expression , we found that on a per cell basis mDCs expressed significantly more IFN-β than pDCs ( Fig . 2D ) , but both mDCs and pDCs expressed similar amounts of IFN-α ( Fig . 2E ) . In contrast , CD11c+ , CD11b− cells carrying the macrophage marker F4/80+ did not express detectable amounts of IFN-α or IFN-β . Since mDCs comprise approximately 60% of all analyzed CD11c+ splenocytes and their numbers are approximately 10-times higher than those of pDCs ( Fig . S5A , B and [50] ) , these results suggested that the vast majority of IFN-αβ in Ad-infected mice was produced by splenic mDCs . To verify this assumption , we analyzed the Ad-elicited cytokine responses in mice depleted of CD11chigh MHC II+ myeloid DCs . To ablate these cells , we injected diphtheria toxin into the CD11c-diphtheria toxin receptor CD11cDTR/GFP transgenic mice [54] 24 h prior to infection with Ad . In agreement with previous reports [55] , [56] , DT pre-treatment of DTR/GFP transgenic mice resulted in the ablation of CD11chigh MHC II+ CD11b+ splenic mDCs , whereas the CD11cint Siglec H+ CD11b− plasmacytoid DCs remained unaffected ( Fig . S5A , B ) . When DT pre-treated CD11cDTR/GFP transgenic mice were challenged with Ad3 and examined for IFN-αβ in plasma 4 and 8 h after infection , only marginal IFN responses were found at both time-points , in contrast to the strong responses of similarly infected transgenic control mice that had not received DT ( Fig . 3A ) . The same pretreatment with DT had no effect on the Ad elicited IFN-αβ response of wild-type mice ( Fig . 3A ) . Interestingly , the determination of IL-6 levels in plasma 8 h after infection revealed that the ablation of mDCs in CD11cDTR/GFP transgenic mice affected only moderately the induction of IL-6 ( Fig . 3B ) , confirming that non-DCs contribute significantly to the Ad-elicited IL-6 response in vivo . The fact that different cell types are responsible for IFN-αβ and IL-6 response to Ad may explain at least in part why viral gene expression did not inhibit the production of IL-6 in Ad3- or Ad R700-infected mice ( see Fig . 1C and Fig . S2D ) . In order to functionally evaluate the possible participation of pDCs to the systemic production of IFN-αβ to Ad we also checked this response in mice depleted of pDCs . As shown in Fig . S6A and B , the injection of anti PDCA-1 antibody led to the substantial decrease of the number of splenic pDCs . Nevertheless , the production of IFN-αβ was not changed in response to Ad infection ( Fig . 3C ) . The data collectively show that the vast majority of IFN-αβ but not of IL-6 in Ad infected-mice is produced by splenic mDCs . Recent studies have shown the involvement of different TLRs including TLR 2 , 3 , 4 , 7 and 9 in the innate recognition of different viruses [16] , [17] , [18] , [57] , [58] , [59] , [60] , [61] . Signaling via TLR9 was shown to be responsible for the strong type I IFN response of pDCs to Ad in vitro [10] , [45] , [46] . Here we investigated the possible involvement of TLR9 in the induction of type I IFNs by measuring IFN-αβ in Ad infected wt and TLR9−/− mice . Fig . 4A shows that TLR9−/− mice produced normal levels of IFN-αβ . upon infection with Ad3 . Comparable results were obtained with the recombinant Ad5-GFP ( Fig . S7A ) . Moreover , Unc93B mice , deficient in signalling by intracellular TLRs showed normal IFN-αβ responses to Ad5-GFP ( Fig . S7A ) . We further checked the possible involvement of the TLR system using mice deficient for TLR2 , TLR4 or for the TLR adaptor proteins TRIF and MyD88 . Fig . 4A shows that the Ad3 induced IFN-αβ levels in all strains of mice were as high as in the respective wt controls . Similarly , comparable IFN-αβ responses were also found in TLR- , MyD88- or TRIF- deficient mice and in the corresponding wt animals after infection with UV-inactivated Ad3 , AdR700 and Ad5-GFP ( not shown ) . Furthermore , comparable IFN-αβ responses were found in cultures of Ad-infected BMDCs from wt , MyD88- and TRIF-deficient mice ( Fig . 4B ) . The various TLR deficient mice showed impaired responses to the corresponding ligands in control experiments ( Fig . S7B ) . Collectively , our data show that the TLR system plays no major role in the systemic IFN-αβ responses to Ads in mice . The type of virus , the IFN-αβ-producing target cell , and the activation mechanism determines whether positive feedback signaling is involved in the induction of the IFN-αβ response or not [13] , [20] . Here , we studied the possible involvement of IFN feedback signaling in the IFN-αβ and IL-6 response to Ad3 by using wt and IFN-αβR-deficient mice . The absence of the IFN-αβ receptor resulted in dramatically decreased levels of IFN-α and IFN-β protein in the plasma as well as of IFN-α and IFN-β mRNAs in the spleen ( Fig . 5A , B ) 4 and 8 h after Ad infection . The difference between the protein or mRNA levels of IFN-α and IFN-β in wt versus mutant animals was approximately 100 and 20-fold , respectively . Furthermore , in contrast to wt mice , the characteristic rise in IFN-αβ levels between 4 and 8 h after infection was absent in IFN-αβR−/− mice . Thus , in Ad-infected mice , production of both IFN-α and IFN-β is strictly dependent on positive IFN-αβ feedback . The determination of IL-6 protein and mRNA levels in the same plasma and splenic tissue samples revealed that the induction of IL-6 is also positively regulated by IFN-αβ signaling ( Fig . 5C ) , which is in agreement with a previous study [10] . We also tested whether IFN-αβR-dependent signaling is involved in the IFN-αβ response of Ad-infected BMDCs in vitro . As with the in vivo results , we found that cells from IFN-αβR−/− mice infected with Ad3 produced significantly less type I IFN than similarly infected cells from wt mice ( Fig . S8 ) . The loss of IFN-αβ signaling also resulted in a strong inhibition of the Ad induced IL-6 production in BMDCs ( Fig . 5D ) and BMMs ( Fig . 5E ) . Furthermore , as shown using Ad-infected BMMs , it resulted also in a strong reduction of inducibility of IL-6 mRNA expression ( Fig . 5F ) . Because transcriptional changes are often determined by epigenetic factors [62] we checked the levels of hyperacetylated histone H4 ( acH4 ) , a permissive factor for transcription at the IL-6 promoter in control and Ad-infected BMMs from wt and IFNαβR−/− mice . Chromatin immunoprecipitation ( ChIP ) assays showed a significant enrichment of acH4 at the IL-6 promoter in infected wt , but not IFNαβR−/− BMMs ( Fig . 5G ) . In a control experiment , as expected , an enrichment of acH4 was observed at the promoter of the constitutively active Topoisomerase 3β but not of the l5 ( not expressing , active only in early B-cells ) gene in both cells types . From these results we conclude that IFN-αβ exerts a positive regulatory effect on the Ad-induced IL-6 transcription and that its loss is at least partly responsible for the strong reduction of the IL-6 response in Ad-infected IFN-αβR knockout mice . Using real-time RT-PCR we then analyzed the spectrum of IFN-αβ genes in wt mice as well as the impact of IFNαβR deficiency on their induction . Included were IFN-α2 , 4 , 5 , 6 , 9 , 11 , 12 , 13 , 14 and IFN-β . All of them were induced in the spleen by Ad3 in vivo and in BMDC in vitro . IFN-αβ subtypes were not detectable in the spleen of unstimulated mice or BMDCs ( not shown ) . Fig . 6 shows the patterns of IFN-αβ genes induced in vivo and in vitro in the presence or in the absence of IFN-αβ feedback signaling . In wt mice and cells IFN-α5 and IFN-β were the most strongly expressed genes and IFN-α13 was the least activated IFN-α subtype . IFN-α11 was not induced at all . IFNαβR deficiency resulted in an inhibition of IFN-αβ gene expression , the strength of which was subtype dependent . In some cases the inhibition was weak ( IFN-α2 , 4 , 5 and IFN-β ) , in others strong or complete ( IFN-α12 , 13 , and 14 ) , showing that the expression of different subtypes of IFN-αβ are differentially affected by IFN-αβ feedback signaling . Collectively , these data show that the adenovirus triggered production of IFN-αβ and IL-6 in BMDCs in vitro and in mice in vivo is strongly dependent on intact IFN-αβ signaling . The transcription factors IRF-3 and IRF-7 have distinct and important roles in IFN-αβ production induced by viruses or other pathogens and their involvement can be characteristic for the induction mechanisms involved [13] . Specifically , IRF-3 has been shown to be critically involved in cytoplasmic DNA sensing and in the Ad-induced IFN-αβ production in BMMs in vitro [63] . We show here that IRF-3 is critical for the induction of IFN-αβ by isolated adenoviral DNA , but not by infection with whole virions in BMDCs ( Fig . 7A ) . In order to analyze the individual contribution of IRF-3 and IRF-7 to the Ad-induced IFN-αβ response in vivo , we infected mice deficient for these transcription factors with Ad3 . We also compared these responses to those triggered by poly I∶C , a known activator of the cytosolic IFN-αβ producing pathway in vivo . As shown in Fig . 7B , the lack of IRF-3 did not significantly influence the plasma levels of IFNαβ 4 or 8 h after infection in response to Ad . In contrast , Ad-infected IRF-7-deficient mice did not exhibit detectable amounts of IFN-αβ in the plasma . Very similar data were obtained with Ad5GFP ( Fig . S9 ) . Thus , IRF-7 , but not IRF-3 , is essential for the induction of the IFN-αβ response during Ad infection . Compared to wt mice , poly I∶C injected IRF-7 deficient mice produced significantly less , but still well detectable amounts of IFN-αβ . Next , we investigated whether the cytoplasmic RNA sensors MDA-5 or RIG-I , or the putative DNA sensor DAI/Zbp1 may play a major role in the induction of the IFN-αβ response to Ad . The possible involvement of MDA-5 was tested using BMDCs and BMMs from MDA-5 deficient mice . Normal IFN-αβ responses to Ad were obtained in MDA-5-deficient BMDCs cells ( Fig . 8A ) and also in BMMs ( not shown ) , whereas the responses to the known MDA-5 ligand Poly I∶C were abrogated ( Fig . 8A ) . The possible involvement of RIG-I was checked using a dominant negative form of RIG-I ( RIG-IC ) [64] . BMDCs from IRF-3−/− mice were transfected with a GFP-expressing plasmid ( transfection control ) with or without RIG-IC and subsequently stimulated with Ad or control leader RNA . We used IRF-3−/− cells to avoid any induction of IFN-αβ by the plasmid itself , which is , contrary to that by Ad , strictly IRF-3 dependent . The induction of IFN-β mRNA was analyzed in sorted GFP-positive cells . As shown in Fig . 8B , left , the transfection of RIG-IC prevented induction of IFN-β mRNA by the leader RNA , but not by Ad . The role of Zbp1/DAI was analyzed using siRNA-mediated knockdown of DAI/Zbp1 in BMDCs . For this purpose , cells from IRF-3−/− mice were co-transfected with DAI/Zbp1 targeting siRNAs and a GFP expressing plasmid and subsequently stimulated with Ad . As shown in Fig . 8B , right , the transfection of BMDCs resulted in strongly reduced DAI/Zbp1 expression but not in a reduced IFN-β mRNA induction by Ad in GFP-positive cells . Control experiments with L-929 cells showed that transfection of the gene-specific siRNA downregulated the expression of DAI/Zbp1 on both the mRNA and protein levels and efficiently inhibited the IFN-β response to B-DNA ( Fig . S10A–C ) . Collectively , our results indicate that known nucleic acid sensors such as MDA-5 , RIG-I and Zbp1/DAI are not involved in the Ad-induced type I IFN production . MAPKs have been previously shown to be activated by Ad in vitro , in different non-immune cell types and to be important for the induction of chemokines in response to Ad [65] , [66] , [67] , [68] . Here we investigated whether members of the MAPK family play a role in the Ad-induced IFN-αβ and IL-6 response . We infected BMDCs with Ad3 in the presence or absence of MAPK inhibitors and determined the levels of IFN-αβ and IL-6 produced . Fig . 9A and Fig . S11 show that the pharmacological blockade of the SAPK/JNK MAPK almost completely inhibited the Ad3-induced production of both IFN-αβ and IL-6 . In contrast , the inhibition of the p38 MAPK pathway partially inhibited the production of IL-6 , but had no effect on the production of IFN-αβ . Finally , the blockade of ERK1/2 had no effect on the production of either IL-6 or IFN-αβ . Very similar data on the effects of MAPK inhibitors were obtained using Ad2 and mutant Ad5-GFP to stimulate BMDC ( data not shown ) . Next , we analyzed the levels of activated SAPK/JNK MAPK proteins in BMDCs and found their robust phosphorylation 2 h after either Ad3 or Ad5-GFP infection ( Fig . 9B ) . We also tested the importance of SAPK/JNK signaling on Ad-induced IFN-αβ production in vivo . Fig . 9C shows that the blockade of the SAPK/JNK signaling pathway in mice completely inhibited the production of IFN-αβ at 4 and partially at 8 h after Ad infection . Taken together , these data strongly indicate that the Ad-activated SAPK/JNK MAPK pathway plays an important role in the virus-induced production of type I IFNs and IL-6 . During the course of our adenovirus preparations , we regularly found “empty capsids” which we separated and purified in addition to the mature virions . We tested the IFN-αβ stimulating activity of these preparations in vivo and observed that they were not active ( Fig . S12A ) . Since empty capsids lack viral DNA and exhibit an altered protein composition [69] , the absence of an inducing viral constituent ( s ) from these capsids could explain their inability to provoke an IFN-αβ response . Another possible explanation could be that endosomal escape is required for IFN-αβ induction , since empty capsids cannot escape from the endosome [69] . To test the latter possibility , we infected mice with 3 . 6×1010 viral particles of wt Ad2 and Ad2Ts1 , a viral mutant deficient in endosomal escape [5] . As shown in Fig . 10A , in contrast to the wt virus , Ad2Ts1 did not induce detectable levels of type I IFN at 4 and 8 h after infection . Furthermore , the IL-6 response was also severely reduced in these animals ( Fig . S12B ) . In a control experiment , mice infected with Ad2Ts1 grown at permissive temperature ( 32°C ) and thus capable of endosomal escape , exhibited normal IFN-αβ responses ( Fig . 10B ) . The inability of Ad2Ts1 to escape from endosomes of mDCs was confirmed by electron microscopy ( Fig . 10E–H ) and in vivo by the lack of Ad early E1A gene expression in the spleen of mice infected with Ad2Ts1 ( Fig . 10I ) . Thus , escape from the endosome is critical for the induction of IFN-αβ and IL-6 by adenoviruses . It should be noted that a further increase in the Ad2Ts1 dose ( 2 . 16×1011 particles ) resulted in detectable , albeit very low levels of plasma IFN-αβ that were released with different kinetics ( Fig . 10C ) . In this case , IFN-αβ was detectable as early as 2 h after infection and , in contrast to the results we obtained with wt viruses ( Figures 1A , 10A and S1A ) , the levels of IFN-αβ did not increase significantly at the later time-points . This already suggests a mechanism for type I IFN induction by Ad2Ts1 that is fundamentally different from the IFN-αβ induction seen with wt Ad2 . Since Ads are DNA viruses , they can possibly be detected by TLR9 . In fact , the innate immune recognition of Ad in pDCs is TLR9 dependent . We therefore repeated the experiment with a high dose of Ad2Ts1 using TLR9−/− mice . As shown in Fig . 10C , TLR9−/− mice did not produce IFN-αβ in response to the mutant Ad2Ts1 , quite in contrast to the results obtained with the wt virus ( see Fig . 4A ) Likewise , there was no detectable IFN-αβ release in Ad2Ts1-infected mice deficient in MyD88 , an essential component of TLR9 signaling ( not shown ) . Furthermore , the corresponding amount of empty particles ( DNA-free ) of Ad2Ts1 elicited no IFN-αβ response in wt mice ( Fig . 10C ) . These data illustrate the critical role of TLR9 in the induction of IFN-αβ by means of high doses of Ad2Ts1 . To exclude the possibility that contaminating DNA on the surface of the virions was responsible for the TLR9-dependent IFN-αβ induction , we treated the mutant virions with bensonase , which destroys all kinds of free nucleic acids . Bensonase-treated Ad2Ts1 still induced an IFN-αβ response in mice ( not shown ) supporting the view that endogenous viral and not contaminating DNA is responsible for IFN-αβ induction by the Ad trapped in the endosomes . We also tested the role of endosomal escape of Ad in the in vitro induction in BMDCs of IFN-αβ and IL-6 . Fig . 10D shows a dose dependent production of IFN-αβ by Ad2Ts1 grown at permissive temperature and Figures S12C and D the production of IFN-αβ and IL-6 by wt Ad2 , but no production of either of the cytokines by Ad2Ts1 grown at the restrictive temperature . Interestingly , liposomal transfection of whole Ad2 Ts1 virions ( but not of empty virus particles ) in BMDCs resulted in the significant production of IFN-αβ that however , was critically dependent on IRF-3 ( Fig . 7A ) . Similarly , Ad2 Ts1 did not induce IFN-αβ production in human monocyte derived DCs ( Fig . S12E ) . The requirement of low pH for Ad3 infection was also tested using bafilomycin A1 , a drug known to inhibit the acidification of endosomes . Experiments shown in Fig . S13A , B revealed that cells treated with this drug produced significantly reduced levels of IFN-αβ and IL-6 respectively , in response to Ad3 . This is consistent with the notion that Ad3 and Ad7 infection of cultured cells requires low endosomal pH [70] , [71] . Similar results were obtained using treatment with ammonium chloride , another acidification inhibitor known to block Ad escape from endosomes ( not shown ) . Collectively , the data in vitro and in vivo provide evidence that the late phase of the Ad infectious entry , in which the virus escapes from the endosome , triggers an innate response characterized by the production of type I IFNs and IL-6 . Induction of type I IFNs was shown to be critical for some innate immune responses to Ads [10] . We therefore investigated whether a characteristic consequence of infection with Ad , the induction of LPS hypersensitivity [72] , might be mediated by type I IFNs . For this purpose , we infected wt and IFNαβR−/− mice with Ads , challenged them 16 h later with LPS and measured the TNF-α response . Non-infected LPS-treated mice served as a control . Unlike the infected wt mice , the Ad3-infected IFNαβR−/− mice did not exhibit enhanced responses to LPS ( Fig . 11A ) . Interestingly , LPS hypersensitivity developed also in mice injected with very small amounts of Ad . Such amounts were capable of the elicitation of IFN-β mRNA in the spleen of infected animals , but incapable of inducing detectable circulating IFN-αβ ( Fig . S14A and B ) . Similar results were obtained in Ad5-GFP-infected mice ( not shown ) . Furthermore , in IRF-7−/− mice that exhibit a severe impairment of the IFN-αβ response to Ad a severe impairment of LPS sensitization by Ad5-GFP was also observed . ( Fig . 11B ) . In contrast , sensitization to LPS developed normally in Ad5-GFP-infected TLR9−/− mice ( Fig . 11B ) , which is in agreement with our finding that TLR9 is not critical for the induction of IFN-αβ by Ad . Furthermore , only Ad5-GFP-infected wt , but not IFNαβR−/− mice exhibited enhanced susceptibility to LPS shock ( Fig . 11C ) . Notably , also Ad vector-infected or IFN-α treated human monocyte-derived mDCs exhibited enhanced sensitivity to LPS and overproduced TNF-α upon LPS challenge ( Fig . S14C ) . On the whole , our results indicate that IFN-αβ is an essential mediator of the LPS hypersensitivity induced by adenovirus infection . An increased expression of the LPS receptor complex on target cells , may contribute to the enhanced reactivity to LPS [73] , [74] . We therefore investigated whether macrophages of Ad infected mice overexpress the receptor components mCD14 and TLR4/MD-2 . We found that splenic macrophages from Ad infected mice overexpress mCD14 in an IFN-αβ dependent manner ( Fig . 11D ) but the expression of TLR4/MD-2 was only minimally affected ( Fig . S14D ) . Furthermore , we found increased acetylation levels of histone H4 at the TNF-α promoter in Ad-infected wt , but not in IFNαβR-deficient macrophages ( Fig . 11E ) . This suggests that Ad-induced IFN-αβ increases LPS-induced TNF-α production , at least in part by epigenetic changes at the TNF-α promoter .
As shown previously and in this study , a wide spectrum of cells including pDCs , mDCs and macrophages [10] , [45] , [46] , [63] produce IFN-αβ in response to Ad in vitro . The present finding that in vivo , in Ad-infected mice , splenic mDCs are the major source of IFN-αβ is surprising , since in mice infected with different viruses ( MCMV , HSV , VSV , MHV , influenza , vaccinia and Sendai viruses ) [27] , [57] , [58] , [59] , [75] , [76] , [77] , [78] pDCs activated by the TLRs constitute the major contributors to the systemic levels of type I IFNs . In the present study the expression levels of IFN-αβ mRNAs in organs and cells from Ad-infected mice suggested a dominant role for splenic mDCs in the IFN-αβ response . Furthermore , the IFN response to Ad was practically absent in mice depleted of CD11chigh MHC II+ myeloid DCs . Also , there was a striking similarity between the IFN-αβ subtypes induced by Ads in the spleen of infected mice and those induced in mDC cultures in vitro . It should be emphasized that in mice infected with VSV , in which splenic pDCs are the main IFN producers , the spectrum of in vivo induced IFN-α subtypes was markedly different [76] . Finally , the finding that the response of Ad-infected mice was completely independent of TLR signaling and strongly dependent on IFN-αβ feedback provide further arguments against a major role for pDCs . In a number of viral infection models , IFN-α and -β production by pDCs was mediated by TLR7/9 [13] , [19] , [23] and was at least partially independent of a positive IFN-αβ feedback [76] , [77] , [79] , [80] . In variance to our data , pDCs and various types of non-pDCs [10] , [42] were suggested to be responsible for the type I IFN responses to adenoviral vectors in mice . In [10] the loss of TLR9 signaling resulted in a reduction of the IFN-α response in mice infected with the recombinant species C Ad-lacZ [10] . This finding suggested a significant contribution of TLR9 , and therefore of pDCs , to the Ad-induced IFN-αβ response in vivo . The ratio of pDCs to mDCs ( approximately 1∶1 ) in the spleen of animals used in the above study was quite different from that we and others [50] have found ( approximately 1∶10 ) . This may explain the conflicting results on the role of TLR9 in vivo , between this previous study and ours . In another study [42] the induction of IFN-αβ was studied in mice with an artificially enlarged pool of DCs ( due to prior pretreatment with sFLT-3L ) , 24 h after recombinant Ad administration . In our study naïve mice were used and 24 h after Ad infection the levels of IFN-αβ were already below the detection limit . Of note also , the bone marrow stromal antigen 2 ( BST2 ) ( that is recognized by the PDCA-1 antibody used for isolation of pDCs in [42] ) , was shown to be up-regulated on numerous cell types following stimulation that triggers an IFN response [81] . Thus , the use of the PDCA-1 antibody for the isolation of pDCs seems to be more reliable in the case of uninfected mice [81] . Recently , an absolute IRF-3 dependency of the in vitro IFN-αβ response of bone marrow derived macrophages to Ad has been reported [63] . In our study IRF-3 deficiency had no significant effect on the levels of IFN-αβ induced in Ad-infected mice . In addition , in Ad-infected macrophages , the relatively low level of IFN-α4 mRNA and its negative regulation by the autocrine feedback was different from our in vivo findings ( strong induction and positive regulation by feedback ) . This is in agreement with the concept that in vivo macrophages make no significant contribution to the IFN-αβ response to Ad . A likely explanation is that the induction of IFN-αβ in macrophages requires the high numbers of virions used in vitro , and that such multiplicities were never reached in vivo . Interestingly however , in contrast to IFN-αβ induction , the induction of IL-6 proceeded in the spleen of infected mice in both DC and non-DC populations . In accordance , the depletion of IFN-αβ-producing mDCs in mice prior to infection lowered , but did not entirely prevent the IL-6 response . It is possible that the pathways leading to the induction of IFN-αβ and IL-6 by Ad are different , at least in different cell types . Alternatively , in vivo , part of the IL-6 formed is induced indirectly via secondary mechanisms . In this context it is interesting that blockade of the p38 MAPK in mDCs in vitro had no effect on Ad-induced IFN-αβ production , but partially inhibited the production of IL-6 . Our data showing that IFNαβR−/− mice exhibit strongly impaired IL-6 responses to Ad is in agreement with a previous report [10] and shows that IFN-αβ is a positive regulator of IL-6 production . Moreover , our data indicate that this effect could be explained at least in part by the positive regulatory effect of IFN-αβ on Ad-induced IL-6 transcription and is achieved by the alteration of the chromatin structure at the IL-6 promoter . The experiments carried out in this study in MyD88- , TRIF- , Unc93B and various TLR-deficient mice excluded a participation of TLRs in the IFN-αβ responses to Ads , including the recombinant Ad5-GFP . The only exception was the TLR9- and MyD88-dependent IFN-αβ response elicited by Ad2Ts1 , a mutant virus deficient in endosomal escape [5] . However , compared to all other Ads used in this study , Ad2Ts1 induced very low levels of IFN-αβ , with faster kinetics and only when used in very high amounts . This is consistent with the finding that the mechanisms of type I IFN induction by Ad2Ts1 and the other adenoviruses are not the same . We suggest that the fast escape of Ads from the endosome circumvents activation of endosomal TLRs . The requirement for a longer endosomal retention time in TLR9-dependent IFN-αβ production has been recently demonstrated [82] . On the whole , our experiments indicate that Ad endosomal escape is required for the induction of IFN-αβ in vivo and suggest a cytosolic pathway . The same requirement was ascertained in the present study for the IL-6 response . Sensors of nucleic acids are powerful initiators of the TLR-independent cytosolic IFN-αβ induction [12] , [14] , [15] . We excluded in this study a major involvement of the cytoplasmic RNA sensors RIG-I and MDA-5 in the induction of IFN-αβ by Ads . Likewise , our study does not support a major participation of the cytosolic DNA sensor DAI/Zbp1 in this induction either . However , our study did not formally exclude the existence of all potentially redundant recognition pathways . So far reported , the pathway ( s ) of IFN-αβ induction activated by cytosolic DNA is ( are ) strictly dependent on IRF-3 and minimally on IRF-7 [34] , [35] , [36] , [37] , [39] . IRF-3 was also reported to be essential for the adenoviral DNA-dependent induction of IFN-αβ in BMMs in vitro [63] . In agreement , in this study we show that transfection of BMDCs with naked adenoviral DNA or with whole virions of the endosomal escape deficient Ad2Ts1 results in a strictly IRF-3 dependent IFN-αβ response . Evidently however , the IFN-αβ response of Ad-infected BMDCs and mice is independent of IRF-3 . These findings do not exclude a requirement for dsDNA recognition in the induction of IFN-αβ , but suggest a different induction mechanism and show the importance of cellular compartmentalization during normal Ad entry . A possible important factor involved in cytosolic Ad sensing might be the adenoviral cysteine protease L3/23 , whose activation requires the presence of Ad DNA [83] . This assumption is supported by our finding that a small fraction of the protease-lacking Ad2Ts1 still reaches the cytosol ( Fig . 10F , H ) , but is devoid of any IFN-αβ-inducing activity . This enzyme , apart from its involvement in maturation of viral proteins and endosomal escape , is essential for the stepwise disassembly of Ads in the cytosol and the release of viral DNA at the nuclear pore [5] . The absolute requirement of IRF-7 for type I IFN induction in Ad-infected mice shows that this transcription factor participates not only in the positive IFN-αβ feedback , but also in the initial IFN-αβ production and indeed plays a master role in the regulation of type I interferon response [29] . A further finding of the present study is the essential role for the MAPK SAPK/JNK in the IFN-αβ induction by Ads in vitro and in vivo , although the DNA-mediated cytosolic induction of IFN-αβ has been reported to be independent of MAPKs activation [14] , [15] , [36] . A cytosolic dsDNA-signaling pathway , mediated by the RNA helicase RIG-I and MAVS , and leading to the induction of IFN-β has been recently demonstrated in human hepatoma cells [84] . This induction pathway is absent in murine systems [22] , [37] , [84] , [85] and is therefore unlikely to participate in the IFN-αβ response of Ad-infected mice . On the whole , our findings do not support the participation of any known nucleic acid-mediated mechanisms in the elicitation of IFN-αβ responses to human Ads . In this context it is interesting that , as shown here , mouse embryonal fibroblasts do not produce IFN-αβ upon Ad infection , although they posses efficient cytosolic induction pathways for dsRNA or dsDNA [13] , [36] , [37] , the latter shown also in this study . Rather , our data support the possibility that the IFN-αβ response to Ads occurs via a novel , not yet characterized cytosolic DNA or protein recognition pathway . Further studies are required to identify the viral components and the host receptors involved . We also emphasize that the known extra- and intra-cytosolic induction pathways may contribute to the IFN-αβ response in a host in which Ads replicate and free viral dsRNA and dsDNA are generated . As mentioned above , the production of IFN-αβ in Ad-infected mice is strongly dependent on IFN-αβ feedback signaling . Inhibition of positive IFN-αβ feedback is a likely explanation for the negative regulation of the IFN-αβ response , observed , in mice infected with intact Ads ( expressing early genes ) in this study . Likely candidates for negative regulators of the IFN-αβ production are the E1A proteins . Previously , they were shown to inhibit Stat1 signal transduction [86] , which plays a role in positive feedback signaling by means of the IFN-αβ receptor . Because the adenoviral vectors used in this study correspond to some of those used in gene therapy trials , the present findings in mice may have important implications for Ad gene therapy applications . The adverse effects observed in therapeutic trials , such as systemic inflammatory response and toxicity [6] , [7] , [9] can be at least partly explained by the enhanced susceptibility of the Ad-infected host to microbial components , such as LPS [72] and lipopeptides ( unpublished results ) from incoming secondary pathogens or from the patient's own flora . Our in vitro finding of a strongly enhanced TNF-α response to LPS in Ad5-GFP-infected human DCs suggests that enhanced susceptibility to LPS may develop also in patients treated with adenoviral vectors . As shown here , this hypersensitivity is mediated by viral-induced IFN-αβ , which is in accordance with the role of IFN-αβ as a key mediator of sensitization to LPS [87] , [88] . The increased mCD14 expression on LPS target cells and epigenetic changes on promoters of relevant genes ( both shown here ) , may at least in part explain the role of IFN-αβ in the development of Ad induced LPS hypersensitivity . Type I interferon induction was recently found in recombinant Ad-treated human cells , especially in pDCs [46] , [89] and in monocyte-derived DCs in the present study . Moreover , this response was observed also in patients administered with recombinant adenoviral vectors [42] , [90] . Since as shown here , IFN-α pre-treatment is capable of increasing susceptibility to LPS of human DCs in vitro , we assume that Ad-induced IFN-αβ can induce LPS hypersensitivity in humans in vivo . Undesirable complications mediated by IFN-αβ can occur not only during gene therapy , but also in immunocompromised patients where Ads are major pathogens [1] . Our finding that blocking SAPK/JNK signaling inhibits the IFN-αβ response to Ads , is of potential interest for prevention or treatment of the direct and indirect adverse effects of IFN-αβ in Ad gene therapy .
Wt C57BL/6 , C57BL/10 and 129Sv mice , as well as all knockout mice were bred under SPF conditions at the MPI . Breeding pairs of IRF-3−/− and IRF-7−/− mice were kindly provided by T . Taniguchi and K . Honda , ( Department of Immunology , Graduate School of Medicine , University of Tokyo , Tokyo ) , of the TRIF and Unc 93B−/− mice by B . Beutler ( Scripps Research Institute , La Jolla ) and of MyD88−/− mice by M . Kopf ( TH Zürich ) and R . Landmann ( Department Forschung , Kantonspital Basel ) . MDA-5−/− femurs were provided by M . Colonna ( Washington University School of Medicine , St . Louis ) . IRF-3- [30] , IRF-7- [29] , Myd88- [91] , TRIF- [92] , TLR-9- [93] , Unc 93B [94] MDA-5 [25] deficient mice and CD11c-diphtheria toxin receptor ( DTR ) /GFP transgenic mice [54] were on a C57BL/6 background , TLR2/4-deficient mice [95] on C57BL/10 background and IFNαβR-deficient mice [96] on 129Sv and for the lethality experiments on C57BL/6 background . When the strain of the mouse was not indicated C57BL/6 mice were used . Mice of both sexes , 8-12 weeks of age were used for the experiments . All of the experimental procedures were in accordance with institutional , state and federal guidelines on animal welfare . Human Ads of species B ( Ad serotype 3 ) and C ( Ad R700 , an Ad serotype 5 derivative , Ad serotype 2 , Ad2Ts1 and Ad5-GFP an early gene expression defective Ad were grown , purified and stored as previously described [5] , [72] . The ratio of infectious/total viral particles was determined on susceptible cells and was typically 1∶20–50 . If not otherwise stated , Ad2Ts1 used for the experiments was grown at non-permissive temperature 38 . 5°C ( resulting in the absence of incorporation of the adenoviral protease L3/23 into viral capsids ) . Empty particles were identified by their light density in CsCl density gradients and the absence of viral DNA and were purified simultaneously with mature virions . UV inactivation of Ad was done as described previously [5] , [72] using the minimal essential dose preventing viral gene expression and replication in susceptible cells . Heat inactivation was done at 56 °C for 60 min [44] . All virus preparations were LPS-free ( less than 1 pg LPS/1011 viral particles ) as determined by the Limulus amebocyte lysate test ( Pyroquant Diagnostic GMBH , Mörfelden , Germany ) . MEFs , BMMs and GM-CSF induced BMDCs were generated as described [95] , [97] . The purity of BMMs was higher than 98% , of BMDCs approximately 80% . BM derived pDCs were generated in the presence of Flt3L and CD11c+ CD11b− B220+ CD62L+ pDCs were MoFlo sorted ( purity higher than 95% ) as described [55] . MEFs were grown in α-MEM ( Invitrogen ) . Immature , monocyte derived human DCs were obtained by incubating adherent monocytes with GM-CSF as described [98] Human mDCs were infected with the indicated amounts of Ads in growth medium containing 2% of donor serum . Mouse L-929 cells were grown in DMEM with 10% FCS . MAPK inhibitors UO126 ( MEK1/2 , Cell Signaling ) , SB203580 ( p38 , Sigma ) and SP600125 ( SAPK/JNK Calbiochem ) were used at 15 µM in vitro and SP600125 at 20 mg/kg in vivo . Bafilomycin-A1 ( Sigma ) was used at 100 nM . The plasmids pmaxGFP ( Amaxa ) and RIG-IC [64] , [99] , [100] were purified with the Endo-Free Plasmid kit ( Qiagen ) and PEG purification . RV leader RNA was generated by in vitro transcription ( MEGA shortscript Kit; Ambion ) from a synthetic DNA template: 5′ACATTTTTGCTTTGCAATTGACAATGTCTGTTTTTTCTTTGATCTGGTTGTTAAGCGTTATAGTGAGTCGTATTACGCG-3′ annealed with 5′-AATTCGCGTAATACGACTCACTATA-3′ . RNA was purified using mini Quick Spin RNA Columns ( Roche ) . siRNA mediated knockdown of DAI/Zbp1 was done using DAI/Zbp1 targeting siRNAs ( On-Target Plus Smartpool reagent , Dharmacon ) . For the induction of type I IFNs MEFs and BMDCs were transfected with nucleic acids complexed with Lipofectamine 2000 ( Invitrogen ) in 96-well plates according to the instructions of the manufacturer . L-929 cells were cotransfected with siRNAs and pmaxGFP using Lipofectamine 2000 according to the suggestions of the supplier . Subsequently GFP positive cells were purified by FACS sorting for further analysis . BMDCs were co-transfected with siRNAs or RIG-IC together with pmaxGFP using the nucleoporator apparatus and the mouse dendritic cell nucleofector kit ( Amaxa ) according to the suggestions of the supplier and gene expression was analyzed in FACS-sorted GFP positive cells . Splenocytes of 3–5 spleens were separated into CD11c+ and CD11c− cells by magnetic adsorption cell sorting ( MACS; Miltenyi Biotec ) . Both fractions were further purified by FACS sorting using anti CD11c-biotin and Streptavidin-PE-Cy5 ( BD PharMingen ) . For the analysis and isolation of splenocytes and CD11c+ subsets , anti-CD11c-biotin , anti-mouse CD11b-Alexa Fluor 647 , Gr-1 FITC , B220 PE , F4/80 PE , anti-mouse CD14- Alexa Fluor 647 and anti-mouse TLR4/MD2-Alexa Fluor 647 antibodies and Streptavidin-PE-Cy5 ( BD PharMingen ) and anti-Siglec H-biotin from Hycult Biotechnology were used . 4–20×104 cells in the different fractions were sorted using MoFlo ( Dako Cytomation , Glostrup , ) to a typical purity of 95%–97% . To deplete mDCs , CD11c-DTR/GFP mice were injected i . p . with DT ( 4 ng/g body weight; Sigma-Aldrich ) as described . pDCs were depleted from naïve mice with 500 µg i . p . injected rat anti–mPDCA-1 mAb ( Miltenyi Biotec ) 24 h prior to Ad infection . Murine IFN-αβ activity was measured using an L-929 cell line ( provided by B . Beutler and Z . Jiang , Scripps Research institute , La Jolla ) as described [18] Human IFN-αβ bioactivity was measured using the HL116 cells from G . Uzé as described [101] . The contribution of IFN-α or IFN-β to the total IFN-αβ activity was determined by pre-incubating plasma for 1 h with excess amounts of neutralizing anti–IFN-β antibody ( Yamasa Corporation , Japan ) or control antibody . Murine TNF-α was measured by a bioassay as described [95] . IL-6 was detected with an ELISA from Pharmingen BD . Human TNF-α was detected with an ELISA from R&D Biosystems . JNK/SAPK MAPKs were detected on immunoblots with antibodies detecting all or phosphorylated isophorms of the proteins ( Cell Signaling ) . Zbp1/DAI and β-actin were detected on immunoblots with antibodies from Santa Cruz Biotechnology . Total RNA was isolated from organs and cells with guanidinium-thiocyanate-phenol-chloroform extraction or with TRI reagent ( Sigma ) . To exclude DNA contamination , RNA samples were treated with RNase free DNase I ( Fermentas ) . cDNA was prepared using Expand reverse transcriptase ( Roche ) and oligo-dT . Conventional RT-PCRs were performed with primers as follows . β-actin: GTC CAC ACC CGC CAC CAG TTC G and GGA ATA CAG CCC GGG GAG CAT CGT C , IFN-β: CCT TTG CAC CCT CCA GTA ATA G and GAC GGA GAA GAT GCA GAA GAG T , IFN-α: ATG GCT AGR CTC TGT GCT TTC CT and AGG GCT CTC CAG AYT TCT GCT CTG , Ad2 E1A: GTT ATT ACC GAA ATG GCC GCC AGT CT and CTT CGG GGG CCG TCA CGT CTA AAT CAT AC . Real-time PCR quantification of RNA expression was done using the LightCycler II system ( Roche ) and the Quantitect SYBR Green PCR Kit ( Qiagen ) according to the instructions of the manufacturers . Arbitrary units of relative expression were generated by dividing the value obtained for IFN-α , -β or IL-6 by the value of β-actin and multiplying the result by 1000 . The detection limits for the IFN-α and IFN-β PCRs were 150 and 30 copies/reaction , respectively . The following primers were used for real-time RT-PCR: β-actin: TGG AAT CCT GTG GCA TCC ATG AAA and TAA AAC GCA GCT CAG TAA CAG TCC G , IFN-β: CCT TTG CAC CCT CCA GTA ATA G and GAC GGA GAA GAT GCA GAAGAG T , IFN-α: TCT GAT GCA GGT GGG and AGG GCT CTC CAG ACT TCT GCT CTG , IL-6: GTG ACA ACC ACG GCC TTC CCT AC and TGC AAG TGC ATC ATC GTT GTT CAT , Zbp1/DAI: GAC GAC AGC CAA AGA AGT GA and GAG CTA TGT CTT GGC CTT CC . The IFN-α primers are consensus sequences from previous publications [102] , [103] , detecting all IFN-α mRNAs . To determine the levels of different IFN-αβ subtypes the HT7900 quantitative PCR system ( Applied Biosystems ) was used . cDNAs were measured in duplicates or triplicates using the following gene-specific assays ( TaqMan Gene Expression Assays , Applied Biosystems ) : IFN-alpha2 ( Mm00833961_s1 ) , IFN-alpha4 ( Mm00833969_s1 ) , IFN-alpha5 ( Mm00833976_s1 ) , IFN-alpha6 ( Mm02524285_g1 ) , IFN-alpha9 ( Mm00833983_s1 ) , IFN-alpha11 ( Mm01257312_s1 ) , IFN-alpha12 ( Mm00616656_s1 ) , IFN-alpha13 ( Mm00781548_s1 ) , IFN-alpha14 ( Mm01703465_s1 ) , IFN-beta ( Mm00439546_s1 ) . The gene for mouse hypoxanthine guanine phosphoribosyl transferase-1 ( HPRT-1 , Mm00446968_m1 ) was used to calibrate the mRNA levels . Quantitative analysis was performed using the SDS 2 . 1 software ( Applied Biosystems ) . mRNA levels were calculated by the following formula: relative expression = 2∧ ( − ( Ct ( Target ) -Ct ( Endogenous control ) ) *f , with f = 10 000 as an arbitrary factor . Chromatin Immunoprecipitation ( ChIP ) assays were performed as described [104] . Briefly , cells were cross-linked with 1% formaldehyde and sonicated nuclear extracts were mock-immunoprecipitated or immunoprecipitated with anti-tetraacetylated histone H4 ( Upstate ) . Recovered DNA aliquots from these samples and from input extracts were amplified with real-time PCR using the LightCycler II system ( Roche ) and Quantitect SYBR Green PCR Kit ( Qiagen ) . Enrichments at specific chromatin loci are shown as the amount of immunoprecipitated DNA in the percent of total input chromatin . The following primers were used: IL-6 promoter: TGG GGA TGT CTG TAG CTC ATT and CAT AGC GGT TTC TGG AAT TGA , TNF promoter: GGG CAG CCC CAG AGG GAA TGA ACT C and TAT GGC AGA GGC TCC GTG GAA AAC TCA CT , Topoisomerase 3β promoter: AGT CCG AGA ACA GCC TGG GT and AGT TGT GCT GCC CAC AGA GG , λ5 promoter: TCC CCA TTG CCA GAT AGA GAC ACA and TGG GCC CAA CAG ATT AAC ACA GAG . BMDCs were cold synchronized with saturating amounts of Ad2 and Ad2-ts1 ( 60 µg/ml , 0 . 25 ml per 4×104 cells on a 12 mm glass coverslip ) for 1 h , washed and incubated at 37°C for the indicated times . The samples were fixed with 2 . 5% glutaraldehyde in 0 . 1 M ice-cold Na-Cacodylate buffer ( pH 7 . 2 ) containing 0 . 5 mg/ml ruthenium red for 1 h , washed with 0 . 1 M Na-Cacodylate buffer ( pH 7 . 2 ) , post-fixed with 2% OsO4 in the same buffer containing 0 . 5 mg/ml ruthenium red for 1 h at room temperature , and embedded in Epon as described [105] . Virus particles at the plasma membrane , endosomes and the cytosol were determined , and results expressed as means of analyzed cells ( n ) with standard errors of the mean . Data was analyzed using Prism GraphPad 4 . 0 software . Data in all figures are presented as mean , error bars show SEM . Statistical analysis was performed with the unpaired t-test ( *: P<0 . 05; **: P<0 . 01; ***: P<0 . 005 ) . | Adenoviruses ( Ads ) are important pathogens and promising vectors for gene therapy applications . In the course of adenoviral infections innate immune responses are activated , which can be beneficial for the antiviral host defense but also detrimental if activated in a deregulated manner . Type I IFNs are crucial for the innate immune control of various viral infections in the mammalian host . So far , the early , systemic release of IFN-αβ during viral infections has been attributed to specialized immune cells , the plasmacytoid dendritic cells . Here , in a mouse infection model , we show that wild type Ads , as well as adenoviral vectors , elicit rapid IFN-αβ production almost exclusively in another cell population , the splenic myeloid dendritic cells . This IFN-αβ storm depends on viral escape from endosomes to the cytosol and the requirements of the response are suggestive of a novel viral induction pathway . Furthermore , we show that virus induced IFN-αβ is the key mediator of Ad-induced hypersensitivity to the cytokine-inducing and toxic activity of lipopolysaccharide , a common constituent of Gram-negative bacteria . Since these bacteria comprise several commensals and pathogens , enhanced susceptibility to lipopolysaccharide may contribute to toxic reactions observed during adenoviral gene therapy and to the clinical symptoms of adenoviral diseases . | [
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] | 2008 | Key Role of Splenic Myeloid DCs in the IFN-αβ Response to Adenoviruses In Vivo |
Over the last fifteen years there have been five pandemics of norovirus ( NoV ) associated gastroenteritis , and the period of stasis between each pandemic has been progressively shortening . NoV is classified into five genogroups , which can be further classified into 25 or more different human NoV genotypes; however , only one , genogroup II genotype 4 ( GII . 4 ) , is associated with pandemics . Hence , GII . 4 viruses have both a higher frequency in the host population and greater epidemiological fitness . The aim of this study was to investigate if the accuracy and rate of replication are contributing to the increased epidemiological fitness of the GII . 4 strains . The replication and mutation rates were determined using in vitro RNA dependent RNA polymerase ( RdRp ) assays , and rates of evolution were determined by bioinformatics . GII . 4 strains were compared to the second most reported genotype , recombinant GII . b/GII . 3 , the rarely detected GII . 3 and GII . 7 and as a control , hepatitis C virus ( HCV ) . The predominant GII . 4 strains had a higher mutation rate and rate of evolution compared to the less frequently detected GII . b , GII . 3 and GII . 7 strains . Furthermore , the GII . 4 lineage had on average a 1 . 7-fold higher rate of evolution within the capsid sequence and a greater number of non-synonymous changes compared to other NoVs , supporting the theory that it is undergoing antigenic drift at a faster rate . Interestingly , the non-synonymous mutations for all three NoV genotypes were localised to common structural residues in the capsid , indicating that these sites are likely to be under immune selection . This study supports the hypothesis that the ability of the virus to generate genetic diversity is vital for viral fitness .
Norovirus ( NoV ) , a member of the Caliciviridae family , is now considered the most common cause of viral gastroenteritis outbreaks in adults worldwide [1] . In the US , NoV has been identified as the cause of over 73% of outbreaks of gastroenteritis [1] . Furthermore , outbreak NoV strains spread rapidly causing great economic burden on society due to medical and social expenses . Consequently , a vaccine or treatment for NoV would be useful in reducing its transmission and alleviating disease symptoms . Our current knowledge of NoV replication and evolution has made it difficult to predict the efficacy of a treatment or longevity of a vaccine , as evidence is emerging that NoV , like many other RNA viruses , exists as a dynamic , rapidly evolving and genetically diverse population [2] , [3] , [4] . The high level of genetic diversity in RNA viruses is recognised as the basis for their ubiquity and adaptability [5] . Therefore , in order to develop a successful treatment or control program it is first necessary to understand the mechanisms behind NoV replication and evolution . NoV is a small round virion of 27–38 nm in diameter and possesses a single-stranded , positive-sense , polyadenylated , RNA genome of 7400–7700 nucleotides [6] . The human NoV genome is divided into three open reading frames ( ORFs ) . ORF1 encodes for the non-structural proteins , including an NTPase , 3C-like protease and RNA-dependent RNA polymerase ( RdRp ) [7] . The two structural proteins VP1 , the major capsid protein , and VP2 , the minor capsid protein are encoded by ORF2 and ORF3 , respectively [8] , [9] . NoV is a highly diverse genus with up to 61% VP1 amino acid diversity between its five genogroups ( GI to GV ) [10] . Up to 44% amino acid diversity over VP1 is also observed within the genogroups and has resulted in the further subgrouping of GI , GII and GIII into 8 , 17 and 2 genotypes , respectively [10] . VP1 exhibits the highest degree of sequence variability in the genome [11] , [12] . It consists of three domains , namely the shell ( S ) domain connected by a flexible hinge ( P1 domain ) to a protruding domain ( P2 ) [13] . The highly conserved S domain forms the backbone of the capsid structure [13] , while the moderately conserved P1 domain encodes the flexible hinge that connects the S and P2 domains . The protruding P2 domain possesses motifs that are involved in binding to the host cell , and hence , the P2 domain is responsible for the antigenicity of the virus [14] , [15] . The most clinically significant of the five genogroups is GII , as it is the most prevalent human NoV genogroup detected and more frequently associated with epidemics compared with other genogroups . Of particular interest is GII genotype 4 , ( GII . 4 ) , because this lineage accounts for 62% of all NoV outbreaks globally [14] , [15] and has also caused all five major NoV pandemics in the last decade ( 1995/1996 , U5-95_US strain; 2002 , Farmington Hills; 2004 , Hunter; 2006 , 2006a virus; and 2007 , 2006b virus ) [16] , [17] , [18] , [19] . The basis for the increased epidemiological fitness [20] of the GII . 4 strains , as determined by its high incidence and ability to cause pandemics , is currently unknown . Investigations with influenza indicate a link between increased viral evolution and increased viral incidence [21] , [22] . However , because of the non-culturable nature of human NoV , variations in rates of evolution have not been calculated for different NoVs and consequently this has not been investigated as a factor in determining viral incidence and epidemiological fitness . Replication efficiency and genetic diversity are both important parameters in viral fitness [23] . The aim of this study was to determine if these two parameters are contributing to the increased epidemiological fitness of the GII . 4 strains . Replication efficiency and genetic diversity are primarily determined by the viral RdRp , as it controls the rate new sequence is introduced into the genome . Therefore using in vitro RdRp assays together with bioinformatics , the replication efficiency , mutation rate and rate of evolution of GII . 4 viruses was compared with other NoV GII genotypes . The results of this study suggest that , like influenza A , the increased incidence of the pandemic GII . 4 lineage may be a result of the combined influence of a high mutation , replication and evolution rate which , together culminate in an increased epidemiological fitness for the GII . 4 strains .
Stool samples containing NoV were obtained from the Department of Microbiology , Prince of Wales Hospital , Sydney , Australia , with the exception of the stool specimen that contained NoV/Mc17/01/Th ( GenBank accession numbers AY237413 ) . This stool specimen was obtained from McCormic Hospital , Chiang Mai , Thailand [16] . The six genetically diverse NoV strains used in this study included: three GII . 4 pandemic strains; NoV/Sydney 348/97/AU ( of the NoV/US95_96 GII . 4 pandemic lineage ) [16] , NoV/NZ327/06/NZ ( NoV/2006a GII . 4 lineage ) [17] and NoV/NSW696T/06/AU ( NoV/2006b GII . 4 lineage ) [17] . Two recombinant strains; NoV/Sydney C14/02/AU ( GII . b ORF1 and GII . 3 ORF2/3 [commonly referred to as GII . b/GII . 3] ) [16] and NoV/Sydney4264/01/AU ( GII . 4 ORF1 and GII . 10 ORF2/3 , [GII . 4/GII . 10] ) [16] , and a GII . 7 NoV , NoV/Mc17/01/Th associated with rare sporadic cases of gastroenteritis [24] . In this study , the RdRp enzymes are referred to by their genotype , except in the case of the GII . 4 strains , which are referred to by their pandemic name , eg . GII . 4 2006b-RdRp ( see Table 1 ) . RdRps from recombinant strains are indicated by an ‘r’ in front of the nomenclature . Viral RNA was extracted from 140 µl of 20% faecal suspension using the QIAmp Viral RNA kit according to manufacturers' instructions ( Qiagen , Victoria , Australia ) . RNA was resuspended in 50 µl of Baxter Steri-pour H2O and stored at −80°C . cDNA synthesis was performed as described previously [16] . The full length capsid gene , P2 domain and RdRp regions were amplified with specific primers ( Table 2 ) using reverse transcriptase - polymerase chain reaction ( RT-PCR ) methods described in [17] . The amplified RdRp genes were cloned into pGEM-T Easy vector ( Promega , Wisconsin , United States ) . Plasmids and PCR products were purified by PEG precipitation and washed with 70% ethanol . Products were sequenced directly on an ABI 3730 DNA Analyzer ( Applied Biosystems , Foster City , CA , US ) using dye-terminator chemistry . pGEM-T Easy vectors containing 1736 bp from the 3′ end of ORF1 were purified using the Quantum prep® plasmid miniprep kit ( BioRad , California , United States ) and used as template DNA for the construction of expression vectors . Strain specific primers incorporating restriction enzyme sites , were designed to amplify the precise RdRp region of each strain ( Table 2 ) . PCR was performed as described previously [17] . PCR products were digested with their corresponding restriction enzymes and cloned into the expression vector pTrcHis2A ( Invitrogen , Mount Waverley , Australia ) . Constructs containing the hepatitis C virus ( HCV ) genotype 3a RdRp ( pVRL69 ) and HCV genotype 1b RdRp ( pVRL75 ) , were used as controls and have been described previously [25] . Site directed mutagenesis of residue 291 in the GII . 4 US95_96-RdRp and the GII . 4 2006a-RdRp was carried out with the Stratagene Quickchange II mutagenesis kit , according to manufacturer's instructions ( Stratagene , La Jolla , United States ) . The primers used to introduce the mutation into the plasmid are listed in Table 2 . The NoV RdRps and control HCV RdRps were expressed in Escherichia coli , as described previously [25] , except expression of the NoV RdRps was performed for 4 hr at 30°C . Purity was checked by SDS-PAGE and the identity of the RdRp was confirmed by western blot with an anti-six histidine antibody and peptide sequencing performed by the Bioanalytical Mass Spectrometry Facility ( University of New South Wales , Australia ) . Recombinant RdRp was quantified with a Nanodrop ND-1000 Spectrophotometer ( Nanodrop , Wilmington , United States ) . Kinetic RdRp assays were performed in a final volume of 15 µl and contained 20 mM Tris-HCl ( pH 7 . 4 ) , 2 . 5 mM MnCl2 , 5 mM DTT , 1 mM EDTA , 500 ng of homopolymeric C RNA template , 2 U RNasin ( Promega ) , 4 mM sodium glutamate and increasing concentrations of [3H]-GTP ( Amersham Biosciences , Little Chalfont , UK ) ranging from 2 µM to 60 µM . Reactions were initiated with the addition of 50 nM of RdRp and incubated for 9 mins at 25°C . The reactions were terminated by adding EDTA to a final concentration of 60 mM , 10 µg herring sperm DNA and 170 µl of 20% ( w/v ) trichloroacetic acid . The incorporated radionucleotides were precipitated on ice for 30 min and then filtered through a 96 well GF/C unifilter microplate ( Falcon , Franklin Lakes , United States ) by a Filtermate harvester ( Packard BioSciences , Melbourne , Australia ) . Using the harvester , the filters were washed thoroughly with water and left to dry . The filter wells were each filled with 25 µl of Microscint scintillation fluid ( Packard Biosciences ) and radioactivity measured using a Packard liquid scintillation counter ( TopCount NXT; Packard Biosciences ) . Background measurements for each assay consisted of reactions without RdRp and were subtracted from the count per minute ( CPM ) values obtained for the individual enzyme assays . Results were plotted and statistical analysis performed with the Mann Whitney Test ( one-tailed , 95% confidence interval ) in GraphPad Prism version 4 . 02 ( GraphPad Software , San Diego , CA ) . An in vitro fidelity assay was developed to measure mutation rates and was adapted from Ward et al . [26] . The RdRp assay was performed using conditions described above with a homopolymeric C RNA template , except 82 . 1 pmoles of [3H]UTP ( 2 µCi ) or [3H]ATP ( 4 µCi ) ( Amersham Biosciences ) were added ( as the non-complementary nucleotides ) with an equimolar amount of GTP ( 82 . 1 pmoles ) ( Promega ) added as the complementary nucleotide . The total amount of ribonucleotide incorporated was calculated in a parallel experiment with the addition of 1 µCi ( 164 . 2 pmoles ) [3H]GTP ( Amersham Biosciences ) as the correct nucleotide . The assay was incubated for 50 min at 25°C . Error frequency of the RdRp was determined by calculating the total number ( pmoles ) of non-complementary ribonucleotides incorporated and dividing by the total number ( pmoles ) of [3H]GTP ribonucleotides incorporated . In order to determine the rate of evolution of the rGII . 3 , GII . 3 , GII . 4 and GII . 7 capsids , the nucleotide sequences of ORF2 were analysed . RNA capsid sequences used for the analysis included eight from this study and 76 sequences from GenBank , with the oldest strains available dating back to 1987 . The strains used and their GenBank accession numbers are listed in Text S1 . The rate of evolution ( substitutions/nucleotide site/year ) for GII . 3 , GII . b/GII . 3 GII . 4 and GII . 7 NoVs was determined by calculating the number of nucleotide substitutions in ORF2 compared to an ancestral strain and this was plotted against time [27] . The rate of evolution was determined by linear regression with the program GraphPad PRISM® version 4 and was equivalent to the gradient of the line . Pairwise alignments of RNA sequences and evolutionary distances between sequences were carried out using the Maximum Composite Likelihood model in Mega 4 . 0 [28] . Bootstrapped trees ( 1000 data sets ) were constructed using the Neighbour-joining method , also with the program Mega 4 . 0 . In order to determine the amount of selection each genotype is under , the average Ka/Ks ratio was calculated for each genotype's capsid gene ( GII . 4 , GII . b/GII . 3 and GII . 7 ) . The Ka/Ks ratio is a measure of nonsynonymous amino acid changes compared to synonymous ( silent ) changes . Ka/Ks>1 indicates that positive selection is occurring . Ka/Ks = 1 is interpreted as neutral evolution and Ka/Ks<1 is indicative of negative or purifying selection . The program Sliding Windows Alignment Analysis Program ( SWAAP ) version 1 . 0 . 2 [29] was utilised . The Nei-Gojobori model was used to calculate Ka and Ks values [30] . The window size was set at 15 bp ( 5 aa ) and the step size was 3 bp ( 1 aa ) . Predicted secondary structure analysis of the RdRps and capsid protein VP1 were performed by generating a Protein Data Bank ( PDB ) file from the amino acid sequence in FastA format using software on the CPHmodels 2 . 0 Server [31] . Three dimensional structures were then generated from the PDB files with PyMol [32] . The GenBank accession numbers for the RdRp and capsid genes described in this paper are listed in Text S1 .
Over the last decade five NoV pandemics have occurred approximately every two years and all pandemics have been associated with a single NoV genotype , GII . 4 [16] , [17] , [19] , [34] . The reason for the predominance of the GII . 4 strains has been the subject of much speculation but is currently unknown primarily due to a limited understanding of NoV population dynamics and evolution [4] , [15] , [35] . Studies with other RNA viruses indicate that viral fitness is dependent on many factors , such as , viral mutation , replication efficiency , population size and host factors ( reviewed in [2] ) . To date progress has been made in understanding the role host factors have on NoV prevalence with several studies indicating that variations in viral docking to the blood group antigens may affect infectivity of individuals within a population ( reviewed in [36] ) . In particular , GII . 4 viruses bind to all blood group antigens , whereas , GII . 1 and GII . 3 viruses bind fewer blood group antigens and this could account for higher prevalence of GII . 4 viruses [36] . This paradigm however remains controversial , especially for GII NoV , as not all studies show an association between blood group antigens and clinical infection [37] , [38] , [39] . Apart from the host/viral interaction , no other factors have been affiliated with NoV fitness . Recent studies performed with poliovirus have shown that an increase in fidelity leads to less genetic diversity and subsequently a reduction in viral fitness and pathogenesis because of a reduced adaptive capacity of the virus [40] , [41] . It has been hypothesised that viruses are fitter if they are able to produce a more robust ( diverse ) population ( reviewed in [42] , [43] , [44] ) . In the current study we examined whether there was a link between epidemiological fitness , as defined by their incidence , and the rate and accuracy of viral replication . In the present study error rates were assessed directly by examining the mutation rate of the viral RdRp and by analysing the rate of evolution for selected GII lineages . Our results are consistent with mutation rates for the poliovirus RdRp [26] and retrovirus reverse transcriptases [45] , which range between 10−3 to 10−5 ( Table 1 ) . The more prevalent GII . 4 strains had a 5 to 36-fold higher mutation rate compared to the less frequently detected GII . b/GII . 3 and GII . 7 strains , as determined by in vitro enzyme assays . Consistent with this , the rate of evolution of the capsid was on average 1 . 7-fold higher in GII . 4 viruses compared to GII . 3 , GII . b/GII . 3 and GII . 7 viruses . The GII . 4 capsids also had a larger Ka/Ks ratio than the GII . b/GII . 3 and GII . 7 strains suggesting that the increased incidence/epidemiological fitness of the GII . 4 strains maybe through greater antigenic drift , a consequence of the higher mutation rate of the GII . 4 RdRp . The mutation rates for the control HCV RdRps ( average of 1 . 6×10−3 substitutions per nucleotide site , Table 1 ) were 2-fold higher compared to the GII . 4 RdRps . Evaluation of previously published rates of evolution for the HCV hypervariable region 1 ( HVR1 ) within the envelope 2 glycoprotein ( E2 ) were also higher ( 6–fold ) than the NoV GII . 4 rates of evolution calculated in this study [46] ( Table 1 ) . HVR1 was chosen for comparison because , like the NoV capsid gene , it is the most variable region in the genome and under the greatest immune selection . Mutation rate and rate of evolution cannot be directly compared as they are indirectly related due to the increased complexity of evolution in vivo [20] . However , in this study we did find a common trend between the two different measurements of diversity with HCV displaying the highest diversity rate for both measurements compared to NoV . Interestingly , the majority of non-synonymous mutations in the P2 domain for all three NoV genotypes were localised to six common structural sites . These six hypervariable regions within the P2 domain were consistent with hypervariable sites for GII . 4 capsids already identified in other studies [4] , [19] . We demonstrated that GII . 7 and GII . 3 viruses shared two and four common hypervariable sites , respectively , with GII . 4 viruses ( Fig . 5 ) . Substitutions at one of these sites ( residue 395 ) have been shown to alter GII . 4 strains antigenic profiles [4] . Localization of the hypervariable sites to common regions on the surface of the P2 domain suggests that these regions are likely to be under immune pressure possibly from a neutralizing antibody response [39] . The lower number of amino acid changes at these sites for viruses with a GII . 3 capsid may explain why GII . b/GII . 3 is predominantly associated with gastroenteritis cases in children [47] . This suggests that GII . b/GII . 3 viruses are not as efficient at escaping herd immunity compared to GII . 4 strains and therefore only hosts immunologically naïve to GII . 3 infection are susceptible . Similarly , we propose that the low prevalence of the GII . 7 strain is also a consequence of a low mutation rate in the RdRp resulting in limited antigenic drift and an inability to escape herd immunity . Apart from mutation rate , replication rate is considered to be another major determinant in viral fitness [48] . Replication rates are important because an increased replication rate would produce a larger heterogenous population than a slower replicating virus in the same unit of time , given the same mutation rate . Interestingly , the RdRps from the recent 2006 GII . 4 pandemic strains had a higher nucleotide incorporation rate than the recombinant GII . 4 RdRp and the US95/96-like pandemic GII . 4 RdRp , which could be associated with a point mutation in the RdRp ( Thr291Lys ) . Residue 291 is located in the finger domain , which is comprised of five β sheets that run parallel and strongly interact with each other . The innermost of these five β sheets contains motif F which interacts directly with incoming nucleotides [49] . Therefore , it is plausible that substitutions at residue 291 affects the orientation of motif F due to the strong interaction between the five β sheets and subsequently alters the binding affinity to the incoming nucleotide . Fixation of the Thr291Lys point mutation in the GII . 4 lineage after 2001 has been paralleled with a reduction in the period of stasis between the emergence of new antigenic variants [4] . Alterations in residue 291 after 2001 could have led to an increase in the rate of evolution of GII . 4 strains by increasing the replication rate , however this did not seem to have an effect on mutation rate ( Table 1 ) . High replication rates did not always correlate with epidemiological fitness as the NoV strain , GII . 7 , had the highest incorporation rate but is considered to be the least fit due to it having the lowest incidence . Therefore , this study suggests mutation rate in combination with a high replication rate are key determinates in epidemiological fitness . Influenza research also indicates a relationship between rate of evolution and epidemiological fitness ( reviewed in [21] ) . New antigenic influenza A variants arise every one to two years and cause more annual epidemics than influenza B , as well as the more devastating pandemics [21] . Once a population has accumulated mass herd immunity to a virus the virus is forced to alter its antigenic determinants , a possibility for viruses with poor fidelity and fast replication rates , or face extinction [50] , whereas , viruses such as influenza B , which have higher fidelity and slower antigenic change , are more often associated with sporadic cases [21] . In this study a parallel can be seen in the epidemiology between NoV and influenza , in particular between GII . b/GII . 3 viruses and influenza B and GII . 4 viruses and influenza A . In summary , this study supports the hypothesis that epidemiological fitness is a consequence of the ability of the virus to generate genetic diversity , as the NoV pandemic GII . 4 strains were associated with an increased replication and mutation rate . Therefore , it would seem that GII . 4 viruses , as opposed to GII . b/GII . 3 and GII . 7 viruses , have reached a balance in their replication rate and mutation rate that is better suited to viral adaptation . In contrast , it would seem that the GII . 7 lineage , despite having a high replication rate , has a low mutation rate that limits its adaptation and therefore its incidence . It is important to improve our understanding of the mechanisms underlying NoV epidemiological fitness as future pandemics are expected . | Since 1995 , norovirus has caused five pandemics of acute gastroenteritis . These pandemics spread across the globe within a few months , causing great economic burden on society due to medical and social expenses . Norovirus , like influenza virus , has over 40 genotypes circulating within the population at the same time . However , it is only a single genotype , known as genogroup II genotype 4 ( GII . 4 ) , that causes mass outbreaks and pandemics . Very little research has been conducted to determine why GII . 4 viruses can cause pandemics . Consequently , we compared the evolution properties of several pandemic GII . 4 strains to non-pandemic strains and found that the GII . 4 viruses were undergoing evolution at a much higher rate than the non-pandemic norovirus strains . This phenomenon is similar to influenza virus , where an increase in antigenic drift has been associated with increased outbreaks . This discovery has important implications in understanding norovirus incidence and also the development of a vaccine and treatment for norovirus . | [
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] | 2010 | Rapid Evolution of Pandemic Noroviruses of the GII.4 Lineage |
As of February 25 , 2019 , 875 cases of Ebola virus disease ( EVD ) were reported in North Kivu and Ituri Provinces , Democratic Republic of Congo . Since the beginning of October 2018 , the outbreak has largely shifted into regions in which active armed conflict has occurred , and in which EVD cases and their contacts have been difficult for health workers to reach . We used available data on the current outbreak , with case-count time series from prior outbreaks , to project the short-term and long-term course of the outbreak . For short- and long-term projections , we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission rates estimated from past EVD outbreaks , with outbreak trajectories conditioned on agreement with the course of the current outbreak , and with multiple levels of vaccination coverage . We used two regression models to estimate similar projection periods . Short- and long-term projections were estimated using negative binomial autoregression and Theil-Sen regression , respectively . We also used Gott’s rule to estimate a baseline minimum-information projection . We then constructed an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes . From August 20 , 2018 to February 25 , 2019 , short-term model projections were validated against known case counts . During validation of short-term projections , from one week to four weeks , we found models consistently scored higher on shorter-term forecasts . Based on case counts as of February 25 , the stochastic model projected a median case count of 933 cases by February 18 ( 95% prediction interval: 872–1054 ) and 955 cases by March 4 ( 95% prediction interval: 874–1105 ) , while the auto-regression model projects median case counts of 889 ( 95% prediction interval: 876–933 ) and 898 ( 95% prediction interval: 877–983 ) cases for those dates , respectively . Projected median final counts range from 953 to 1 , 749 . Although the outbreak is already larger than all past Ebola outbreaks other than the 2013–2016 outbreak of over 26 , 000 cases , our models do not project that it is likely to grow to that scale . The stochastic model estimates that vaccination coverage in this outbreak is lower than reported in its trial setting in Sierra Leone . Our projections are concentrated in a range up to about 300 cases beyond those already reported . While a catastrophic outbreak is not projected , it is not ruled out , and prevention and vigilance are warranted . Prospective validation of our models in real time allowed us to generate more accurate short-term forecasts , and this process may prove useful for future real-time short-term forecasting . We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination , and this model estimate may offer a surrogate indicator for the outbreak response challenges .
On August 1 , 2018 , the World Health Organization ( WHO ) announced a new outbreak of Ebola virus disease ( EVD ) in North Kivu Province , Democratic Republic of Congo ( DRC ) [1] . Epidemiological investigations traced EVD cases back to the week of April 30 and identified the initial epicenter to be Mabalako . This region of DRC ( Fig 1 ) has over five million inhabitants , some of whom suffer from armed conflict , humanitarian crisis , and displacement to the bordering countries of Uganda and Rwanda [2] . Since the outbreak began , its magnitude has surpassed all other EVD outbreaks in DRC , becoming the second largest in history to date . As of February 25 , 2019 , 875 EVD cases had been reported ( 65 probable and 810 confirmed ) , and the Ministry of Health of DRC , the World Health Organization , and other organizations were responding to the Ebola outbreak [3] . This is the first EVD outbreak to occur in an armed conflict zone , and this new context has created challenges to the outbreak response [4] . Uptake of traditional control measures such as contact tracing and safe burials has varied with the community , even with the rapid deployment and widespread availability of experimental vaccines and therapeutics [3] . Our understanding of the impact of scientific advances on EVD transmission has been evolving; there has been an underlying , untested assumption that vaccination may reduce epidemic transmission [5] . Mathematical modeling of simulated EVD epidemics suggested that vaccination coverage as low as 40% in the general population and 95% in healthcare workers ( HCWs ) may avert another epidemic similar to size of the West African one [6] . Studies have yet to estimate the levels of vaccination coverage associated with epidemic decline . An even greater potential problem in difficult-to-access outbreak areas is ascertainment of vaccination coverage among contacts and contacts of contacts . Mathematical models are needed that can produce relatively accurate forecasts in the setting of such unknown but important outbreak response metrics . During an Ebola outbreak , real-time forecasting has the potential to support decision-making and allocation of resources , but highly accurate forecasts have proven difficult for Ebola [8 , 9] as well as other diseases [10–13] . Highly accurate forecasts of small , noisy outbreaks may be a fundamentally elusive ideal [14] . Previous work has found that probabilistic forecasts can have relatively high accuracy within a few weeks , though they become less useful as time horizons grow longer [15] . Thus , short-term forecasts may provide useful information for response organizations . In this paper , we applied a suite of independent methods to estimate short- and long-term projections of future EVD case counts in northeastern DRC in real time . Given the unique context of the current EVD outbreak , we validated short-term projections before generating projections on February 25 . One of our models also estimated vaccination coverage . It is our hope that these projections offer insight into the outbreak trajectory and vaccination coverage , particularly in the setting of armed conflict .
Data on the current outbreak were collected from the WHO website in real time as updated information was published [3] . A cumulative case count of probable and confirmed cases was extracted to be consistent with the best knowledge at the time . Snapshots of the table of case counts were kept as of multiple dates ( S1 Fig ) , to be used in retrospective scoring of model projections against subsequently known counts . Although the epidemic was officially reported on August 1 as a cluster of cases occurring in June and July , seven sporadic early cases from April and May were subsequently linked to the current outbreak and were added to later case totals [3] . This additional knowledge was added retrospectively to the time series of cumulative case counts only for predictions made for days on or after September 15 , when these cases were officially linked to the current outbreak . We modeled Ebola virus ( EBOV ) transmission using a stochastic branching process model , parameterized by transmission rates estimated from the dynamics of prior EVD outbreaks and conditioned on agreement with reported case counts from the 2018 EVD outbreak to date . We incorporated high and low estimates of vaccination coverage into this model . We used this model to generate a set of probabilistic projections of the size of simulated outbreaks in the current setting . This model is similar to one described in previous work [16] , with the addition of a smoothing step allowing for a continuum of transmission rates interpolated between those estimated from prior outbreaks . On the assumption that past outbreaks provide a basis for projection of the current outbreak , we used estimates of transmission rates from past EVD outbreaks to parameterize simulations of the current outbreak . To estimate the reproduction number R in past outbreaks as a function of the number of days from the beginning of the outbreak , we included reported cases by date from fourteen prior outbreaks ( S1 Table ) , [17–30] . To reflect the Ebola response system in DRC during what is now its tenth outbreak , the first historical outbreak reported in each country was excluded ( e . g . the 1976 outbreak in Yambuko , DRC ) , as there is a difference in the Ebola response system as well as community sensitization to EVD following a country’s first outbreak . We used the Wallinga-Teunis technique to estimate R for each case and therefore for each reporting date in these outbreaks [31] . The serial interval distribution used for this estimation was a gamma distribution with a mean of 14 . 5 days and a standard deviation of 5 days , with intervals rounded to the nearest whole number of days , consistent with the understanding that the serial interval of EVD cases ranges from 3 to 36 days with mean 14 to 15 days . Transmission rates estimated by day in these outbreaks tend to decline from initially high to eventually low values , though they may display substantial fluctuations . This “quenching” of transmission may be driven by formal interventions such as quarantine , or by informal changes in individuals’ behavior in response to the disease or by depletion of uninfected contacts of infective individuals , or by other causes . We incorporated this pattern into our model by estimating an initial reproduction number Rinitial and quenching rate τ for each outbreak by fitting an exponentially quenched curve to the outbreak’s estimates of R by day d ( S2 Fig ) , and used these pairs of parameters , one from each past outbreak , to construct a joint distribution of initial reproduction numbers and quenching rates for outbreak simulation . We simulated EBOV transmission using a stochastic branching process model , in which the number of secondary cases caused by any given primary case is drawn from a negative binomial distribution , whose mean is the reproduction number R as a function of day of the outbreak , and variance is controlled by a dispersion parameter k [32 , 33] . All transmission events were assumed to be independent . The interval between date of detection of each primary case and that of each of its secondary cases is assumed gamma distributed with mean 14 . 5 days and standard deviation 5 days , rounded to the nearest whole number of days , as above . We used the ( Rinitial , τ ) pairs estimated from prior outbreaks to provide R values for simulation . Rinitial values were sampled uniformly from the range of values estimated from past outbreaks . We applied a linear regression to the values of Rinitial and log ( τ ) estimated for prior outbreaks , and used the resulting regression line to assign a mean τ to each R , used with the residual variance of log ( τ ) as a distribution from which to sample τ values for simulation given Rinitial . Note that the range of fast and slow quenching scenarios modeled in this way is not limited to the exact combinations estimated from past outbreaks , but extends over a continuous distribution that includes those values . The parameters Rinitial and τ sampled in this way , together with three values of the dispersion parameter k , 0 . 3 , 0 . 5 , and 0 . 7 , consistent with transmission heterogeneity observed in past Ebola outbreaks , were used to generate simulated outbreaks . This model generated randomly varying simulated outbreaks with a range of case counts per day . The outbreak was assumed to begin with a single case . The simulation was run multiple times , each instance producing a proposed epidemic trajectory , generated by the above branching process with the given parameters Rinitial , τ , and k . These proposed trajectories were then filtered , by discarding all proposed trajectories except those whose cumulative case counts matched known counts of the current 2018 EVD outbreak on known dates . In earlier , smaller data sets we filtered against all reported case counts , while in later , more complete data sets we used a thinned series of case counts for filtering , for computational tractability , by selecting five case counts evenly spaced in the data set plus the final case count ( S1 Fig ) . The filtration required an exact match of the first target value , and at subsequent target dates accepted epidemics within a number of cases more or less than each recorded value . On the earlier data sets in which the beginning dates of the epidemic were unknown , the first target value was allowed to match on any day , and subsequent target dates were assigned relative to that day . In order to produce model outbreak trajectories consistent with the case counts reported more recently in the outbreak , it was necessary to make the filtering step of the model more tolerant to variation in counts in order to accommodate the rapidly rising count . This was because higher transmission rates in late September and early October were necessary to generate case counts of that size than were consistent with the earlier counts . Thus , this model embodies a set of assumptions that transmission rates are overall gradually declining from the start of the outbreak to its end , though possibly with a high level of variability in transmission rate between cases and between simulations . When the tolerance of the filter on case counts is small , quenching of transmission through time must closely track case counts , while when tolerance is high , fluctuations in the rate of generation of new cases can reflect a pattern of ongoing quenching of transmission more loosely and on the long term , while being more insensitive to short-term up and down fluctuations in transmission rates reflected in the true case counts . We varied the tolerance as the data set became more complete , to maintain a roughly fixed rate of generation of filtered trajectories . As larger tolerances became necessary , in data sets from after October , we introduced one further distinction: while it is possible for cumulative case counts to decrease as inaccurately classified cases are removed from the counts , due to the precision of the labeling of cases probable and confirmed we expect this to happen rarely , so we limit the tolerance of matching to only at most 15 cases below the reported count regardless of the tolerance of counts above the reported count . This limit on underestimates was applied only to analysis of data sets from later than October 13 , to preserve unaltered the projection methods we reported in a preprint of this paper [34] . Filtering tolerances were as follows: when using the August 20 data set , we allowed a tolerance of 4 cases more or less than each target count; for August 27 and September 5 , 6 cases; for September 15 , 10 cases; for October 7 , 12 cases; for October 13 , 17 cases; for November 1 , 41 cases; for November 20 , 55 cases; for January 6 , 2019 , 75 cases; and for February 25 , 150 cases . This one-step particle filtering technique produced an ensemble of model outbreaks , filtered on agreement with the recorded trajectory of the outbreak to date . This filtered ensemble was then used to generate projections of the outbreak in the short term ( one week , two weeks , four weeks , and eight weeks ) and long term ( final size ) [35] . To model vaccination coverage with respect to total transmission ( unreported and reported ) , we multiplied the estimate of vaccine effectiveness by low and high estimates of reported cases . In a ring vaccination study at the end of the West Africa outbreak , the overall estimated recombinant vesicular stomatitis virus ( rVSV ) -vectored vaccine efficacy was 100% and vaccine effectiveness was 64 . 6% in protecting all contacts and contacts of contacts from EVD in the randomized clusters , including unvaccinated cluster members [36] . We used an estimate of 64 . 6% vaccine effectiveness in our stochastic model . The ring vaccination study found the vaccine to be effective against cases with onset dates 10 days or more from the date of vaccine administration , so we modeled the vaccination program as a proportionate reduction in the number of new cases with onsets 10 days or more after the program start date . We used past estimates of the proportion of unreported cases to estimate the proportion of exposed individuals not covered by the vaccination process . Based on a Sierra Leonean study from the 2013–2016 outbreak [37] , we estimated the proportion of reported cases ( out of all known cases ) in DRC to range from a low of 68% to a high of 96% , as extremes of the best known range of estimates , to be evaluated on their fit to data . Given these low and high estimates of reported cases and the estimate of vaccine effectiveness , a low estimate of vaccination program coverage was 44% ( 68% × 64 . 6% ) and a high estimate of vaccination program coverage was 62% ( 96% × 64 . 6% ) . Vaccination was included in the simulation using a start date of August 14 , 2018 , estimated from dates available from situation reports [3] . To generate forecasts , from each data set 320 to 330 simulated outbreaks were collected that passed the step of filtering on approximate agreement with DRC case counts . The simulated outbreaks that were retained after filtering were continued until they generated no further cases . Rare outlying simulated outbreaks that exceeded 300 , 000 cases were capped at the first value reached above that number , to limit unnecessary computation . We used this ensemble to derive a distribution of final outbreak sizes , and of cumulative counts at specific forecasting dates . Projection distributions were constructed using kernel density estimation with leave-one-out cross-validation to determine bandwidth , using a log-normal kernel for final sizes , due to the extended tail of the values , and a normal kernel for all other estimates . We calculated median values and 95% prediction intervals using the 2 . 5th and 97 . 5th percentiles of simulated outbreak size . The frequencies of occurrence of the three vaccination coverage scenarios modeled ( zero , low , and high coverage ) among the simulated outbreaks accepted by the stochastic model’s filtering step were used to estimate likelihoods of the three scenarios . The frequencies of ( Rinitial , τ ) pairs selected by the filtering process were similarly recorded as an estimate of the likelihood of those transmission rate parameters . We provide a detailed report of the parameters , simulations , and performance of the stochastic model in the Supplemental Material . A negative binomial autoregressive model was chosen through a validation process to forecast additional new case counts at time points one week , two weeks , four weeks , and eight weeks from the current date . To adjust for disparities in the frequency of case reporting in historic outbreaks , the data were weighted by the inverse square root of the number of observations contributed to the model . Models considered included parameters for historic cumulative case counts ( probable and confirmed ) at different time points , logs of historic case counts , ratio of historic case counts to try and capture the trend of the epidemic curve , log ( time ) , and an offset for current case total . When historic case counts for specific dates were missing , each missing case count was linearly interpolated from the two nearest case counts , allowing the model to remain agnostic about the current trend of the epidemic . After model fitting and validation , the final model chosen was a log-link regression for additional cases on the number of new cases identified in the previous two and four weeks and the ratio of these two case counts . We conducted a simple regression forecast based solely on outbreaks of size 10 or greater , based on prior outbreaks [17–30] . Nonparametric Theil-Sen regression ( R package mblm [38] ) was used to project the final outbreak size based on values of the outbreak size at a specific earlier time . All time series were aligned on the day they reached 10 cases . Finally , we reported the median and 95% central coverage intervals for the prediction distribution , conditional on the predicted value being no smaller than the observed value for each day . More details can be found elsewhere [16] . Gott’s rule assumes we have no special knowledge of our position on the epidemic curve [39] . Given Y0 cases reported , assuming a non-informative uniform prior for the portion α of the epidemic observed to date , the corresponding probability density function for the final size Y = Y0/α is Y0/y2 , Y0 ≤ y . We constructed a probability mass function by assigning all probability density to the whole number of days given by the integer part of each value . We used this probability mass function as a projection of the final outbreak size . Models’ performance on short-term projections was scored using the natural logarithm of the probability assigned to subsequently known reported case counts . The stochastic and negative binomial auto-regression models were scored based on projections at multiple time points , where available: For each projection , we generated an assignment of probability to possible values of multiple quantities . As Ebola situation reports were released before February 25 , we generated short-term projections in real time with the stochastic and negative binomial auto-regression models . We scored these projections and calibrated the models during the outbreak . We then used these calibrated models for our projections on February 25 . The Theil-Sen and Gott’s rule models were not calibrated , because the outbreak’s final size is not yet known . Final outbreak size projections generated by the stochastic , Theil-Sen , and Gott’s rule models were recorded for future evaluation of their performance . Because the question has been raised of whether the current outbreak might grow to the scale of the catastrophic West Africa outbreak , we evaluated each model’s projected probability of exceeding three large outbreak scenarios of 1 , 000 cases , 10 , 000 cases , or the 28 , 616 cases reported in the West Africa outbreak [30] . Each of these three thresholds is more than double the size of the previous second largest outbreak ( n = 425 , Uganda , 2000 ) [22] .
The stochastic model estimated likelihoods of the three scenarios of zero , low , and high vaccination coverage , based on how often models using the different coverage assumptions were able to pass the particle filtering step . In the estimate based on data through February 25 , zero vaccination coverage was estimated substantially more likely than low or high coverage , as was also true in most earlier estimates . The lower vaccination coverage scenario was estimated more likely than the higher one . Higher vaccination coverage scenarios were estimated more likely in estimates made before October , at which time the outbreak epicenter shifted into the conflict zone and situation reports described an increase in case counts ( S5 Fig ) . Assuming that other transmission factors were held constant , our result may provide evidence that that geographical shift in transmission contributed to a decreased likelihood of a high vaccination scenario .
As of February 25 , 2019 , with 875 cases reported to date , most of our model projections were concentrated in a range up to about 300 additional cases overall , and even the Gott’s rule forecast , whose prediction intervals are especially wide , placed low probability on outcomes on the scale of the West African outbreak . The current outbreak , however , has so far behaved unpredictably , possibly due to complex social circumstances , and could continue to be unpredictable and render our projections inaccurate . EVD has never before been introduced into a conflict zone with such political instability , potential disease mobility and community impenetrability [2] . In October , WHO reported that up to 80% of contacts were not being traced [3] . At present , the most reliable data source of EBOV transmission has been the weekly case counts that can be found in the WHO situation reports . Despite such situations of data scarcity and new outbreak circumstances , our models generated relatively accurate short-term projections of outbreak size in the months of January and February 2019 , suggesting that short-term projections made in real time can be useful in decision making and resource allocation . Another outbreak response challenge has been ascertaining the level of vaccination coverage that has occurred during the ring vaccination program . For the ring vaccination program to be theoretically effective at reducing transmission , an uncertain proportion of contacts and contacts of contacts need to receive the vaccine [6] . We estimated that transmission rates are higher than would be expected under target levels of 62% coverage due to contact tracing and vaccination . Under our stochastic model’s assumptions , the elevated transmission rates , relative to those of past outbreaks , needed to reproduce this outbreak’s epidemic trajectory are highly unlikely to be randomly observed with high vaccination coverage . This model estimate may serve as a loose proxy for the outbreak response challenges . Thus , outbreak response teams may need to consider other control and care strategies to end the outbreak . Under current circumstances , mass vaccination in regions of high prevalence or areas where the outbreak has newly arrived may be a more effective strategy to reduce EBOV transmission than ring vaccination . Aggressive supportive care , experimental therapeutics and high-quality facilities ( e . g . air-conditioned , individualized ) have also been described as part of the outbreak response . These interventions have the potential to improve health-seeking behaviors and possibly reduce EBOV transmission in communities that are resistant to control efforts [15] , but further studies are needed . The mathematical models we adopted for this project near the beginning of the outbreak were developed to predict the course of prior outbreaks , and did not consider circumstances unique to the current outbreak . To improve the accuracy of short-term projections , we used the short-term forecasting performance of the mathematical models during the current outbreak to calibrate them and improve their performance on this outbreak . This may be a useful pattern for short-term forecasting of ongoing disease outbreaks in real time . The performance of our short-term prediction models can be assessed on the outbreak to date . The relatively simple auto-regression model we used performed more consistently on the range of partial data sets used for scoring than the more complex stochastic simulation model did . The stochastic model has tended to produce tighter prediction distributions that are prone to extreme failure when they get it wrong , while the auto-regression model’s predictions are more tolerant of unpredictable outcomes . Conversely , the stochastic model outperforms the auto-regression when it gets the prediction right . It should also be noted that because the stochastic simulation model is based on mechanistic knowledge of the transmission process generating the outbreak while the regression model is a purely statistical inference from past outbreaks , it may be that if conditions emerge that are substantially different from past outbreak conditions , the mechanistic model may produce sensible predictions where a purely statistical model fails . Because the other included models produce only final outbreak size projections , they can not be fully evaluated before the outbreak has ended . However , we can note that our early projections of final size ( S9 and S10 Figs ) fell below the counts that have been observed as of February . In other words , our forecasts based on early reports and on an assumption that past outbreaks can be used to forecast the present one were more optimistic than warranted by subsequent events . Events to come may shed light on whether this outbreak is qualitatively different than the past ones we have used to construct forecasts , perhaps due to the impact of conflict conditions on the outbreak . There are limitations to our projections . Projection distributions are right-skewed , with long tails ( and we therefore report the median instead of the mean ) . We were unable to include all the 23 observed EVD outbreaks with a case count greater than ten cases in our estimates due to data availability . Our regression models are based entirely on past outbreaks of Ebola virus disease ( measured and reported in different ways ) , and cannot account for the improved control measures and vaccination in the way that a mechanistic model does . We included as much real-time information in our models as possible , but situations such as the introduction of EVD into a zone of armed conflict and the recent introduction of vaccination are not reflected by the suite of past outbreaks . The stochastic model used estimated vaccination effectiveness , reported cases , and timing of onset dates affected by vaccination from studies from West Africa , not DRC , and did not include vaccination of healthcare workers . Our forecasts do not account for possible unreported cases or changes in reporting over time; such gaps in reporting can not be ruled out , though given the intense efforts at case finding that started in July and carried forward , we think it unlikely that there were large changes in reporting . Furthermore , as the outbreak moved into areas affected by violent conflict as the outbreak continued , we think it likely that case reporting , if anything , decreased over time , and therefore underreporting would not explain the apparent increase in transmission from June to October . While it would be desirable to use the vaccination coverage estimates to estimate the number of cases prevented by the vaccination program , our models were not designed to produce a testable estimate of such an effect . Gott’s rule [39] , presented initially as reflecting a “Copernican” principle with respect to time , assumed that ones observation of the age of a phenomenon occurred at a random time during its lifetime . Gott applied this principle in a variety of settings , including the lifetime of Broadway shows , the Berlin Wall , and of the human race [39 , 40] . Critics suggest that Gott’s rule gives misleading predictions when the phenomenon in question exhibits known time scales , such as in prediction of human lifetimes [40 , 41] . Gott’s rule has not been validated for epidemics , and in this case , is based on an assumption that the current case count in an epidemic occurs at a random point in its progression . For an easily detected but uncontrollable supercritical epidemic , we would expect forecasts based on the first reports to underestimate the final size . For an easily controlled epidemic , forecasts would more likely be too pessimistic , since control measures would ( by assumption ) swiftly end the epidemic after recognition . Our application of the rule is intended to serve as a null hypothesis-like comparator for the other methods . Our models do not account for spatial heterogeneity in transmission , which may be relevant to the course of this outbreak , in which an increase in transmission appears to coincide with a shift in location . The stochastic model may be sensitive to the assumption of an exponentially decaying curve for transmission rate by day , and alternative assumptions might lead to a different distribution of forecast outcomes . Indeed , all of our projections are conditional on model assumptions being met . If unpredictable events were to change patterns of EBOV transmission in ways not seen in past outbreaks , assumptions used for model projections could be violated and our results could change . For example , exceedingly improbable events such as a catastrophic outbreak ( more than 10 , 000 cases or approaching the size of the 2013–2016 West Africa outbreak ) might become more probable . A strength of our approach was the use of multiple methods to estimate the outbreak size , including both mechanistic modeling and purely statistical approaches . Before October , there was limited EBOV transmission in active , armed conflict zones . When more reported EVD cases occurred in Beni , it was unclear how the context would affect secondary EBOV transmission . The October data suggests that EBOV transmission increased there . We believe the increased rate of reported EVD cases corresponded to the shift of EBOV transmission into conflict zones . It may be that a model that explicitly distinguishes transmission rates in these zones from those in other areas would model the dynamics underlying these cases more precisely and produce more accurate projections . As this report , written in February 2019 , goes to print in June 2019 , we note that , as even a casual observer of the situation will know , the outbreak has exceeded the final size projections listed here , and the spread of disease and mortality is still ongoing . The predictions of our stochastic branching process and regression models reflect an assumption that this outbreak can be modeled by the ensemble of past outbreaks located in countries with previous experience responding to EVD outbreaks . This criterion of non-naive countries has the consequence of excluding the massive West African outbreak of 2013–2016 from the distribution of past outbreaks modeled , which may also be justifiable per se as it is a single statistical outlier . However , as the outbreak has exceeded the resulting projections , their assumptions must be called into question . In particular , the range of quenching scenarios considered by the branching process model appears to overestimate whatever damping of transmission may be occurring in the present outbreak , leading to its undersized projections of future case counts . An additional consequence of overestimating quenching is that because vaccine coverage is modeled as a reduction in transmission from the quenched rates assumed otherwise , the resulting estimates of vaccine coverage may be overly low . The ability of the non-mechanistic , data-agnostic Gott’s rule projection to predict the unusually large size of this outbreak more accurately than the other models suggests that broad estimates of its kind , reflecting the true extent of ignorance imposed by the unpredictable nature of events such as this outbreak , may be valuable tools in epidemic prediction and decision support despite the natural desire to predict outcomes with more precision . Many of the reported EVD cases in this outbreak have been identified as new transmission chains . While a reduced ability to detect and vaccinate contacts in difficult-to-access communities has been anecdotally reported , our stochastic model provided empirical evidence of outbreak challenges , suggesting that transmission rates were consistent with lower than target levels of vaccine coverage beyond those already reported . While a catastrophic outbreak of 10 , 000 or more cases is not projected as probable , vigilance is warranted . New circumstances—such as epidemic spread to Uganda—call for newly validated projections , whenever possible , even in the short term . | As of February 25 , 2019 , 875 cases of Ebola virus disease ( EVD ) were reported in North Kivu and Ituri Provinces , Democratic Republic of Congo . Since the beginning of October 2018 , the outbreak has largely shifted into regions in which active armed conflict has been reported , and in which EVD cases and their contacts have been difficult for health workers to reach . We used an ensemble of models to estimate EVD transmission rates and to forecast the short- and long-term course of the outbreak . Our models project that a final size of roughly up to 300 additional cases is most likely , and estimate that transmission rates are higher than would be seen under optimal levels of contact tracing and vaccination . While a catastrophic outbreak is not projected , is it not ruled out , and prevention and vigilance are warranted . | [
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] | 2019 | Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019 |
A substantial proportion of the global burden of typhoid fever occurs in South Asia . Kathmandu , Nepal experienced a substantial increase in the number of typhoid fever cases ( caused by Salmonella Typhi ) between 2000 and 2003 , which subsequently declined but to a higher endemic level than in 2000 . This epidemic of S . Typhi coincided with an increase in organisms with reduced susceptibility against fluoroquinolones , the emergence of S . Typhi H58 , and an increase in the migratory population in Kathmandu . We devised a mathematical model to investigate the potential epidemic drivers of typhoid in Kathmandu and fit this model to weekly data of S . Typhi cases between April 1997 and June 2011 and the age distribution of S . Typhi cases . We used this model to determine if the typhoid epidemic in Kathmandu was driven by heightened migration , the emergence of organisms with reduced susceptibility against fluoroquinolones or a combination of these factors . Models allowing for the migration of susceptible individuals into Kathmandu alone or in combination with the emergence of S . Typhi with reduced susceptibility against fluoroquinolones provided a good fit for the data . The emergence of organisms with reduced susceptibility against fluoroquinolones organisms alone , either through an increase in disease duration or increased transmission , did not fully explain the pattern of S . Typhi infections . Our analysis is consistent with the hypothesis that the increase in typhoid fever in Kathmandu was associated with the migration of susceptible individuals into the city and aided by the emergence of reduced susceptibility against fluoroquinolones . These data support identifying and targeting migrant populations with typhoid immunization programmes to prevent transmission and disease .
Typhoid fever is a febrile disease , arising exclusively in humans , which is caused by the bacterium Salmonella enterica subspecies enterica serovar Typhi . S . Typhi is transmitted through contaminated food or water , and is a major cause of morbidity in countries plagued by lack of clean water or poor sanitation [1] . A substantial proportion of the typhoid fever burden occurs in South Asia , where the disease is widely endemic and is estimated to cause 3 . 7 to 7 . 0 million cases and 76 , 000 deaths annually [2–4] . Nepal is amongst the world’s most impoverished countries [5] and suffers from endemic typhoid fever [6 , 7] . Between 2000 and 2003 , there was a marked increase in the number of S . Typhi cases at Patan Hospital , one of the main hospitals treating febrile patients in Kathmandu , rising from 245 cases in 2000 to 1 , 792 cases in 2003 [6] . The number of S . Typhi cases subsequently declined but to a higher endemic level than in 2000 [7] . This epidemic of S . Typhi coincided with the increased prevalence of organisms with reduced susceptibility against fluoroquinolones [8 , 9] and the emergence of S . Typhi H58 variants [10 , 11] . H58 S . Typhi are associated with reduced susceptibility against fluoroquinolones and other forms of antimicrobial resistance , and this genotype has become the dominant variant in many countries where S . Typhi is endemic [11 , 12] . Nepal additionally has a highly migratory population , which could also have fuelled the typhoid fever epidemics [13] . Mathematical modelling is a useful methodology for evaluating the hypotheses that underlie disease processes , which can be applied to assess the impact of control efforts and predict future trends . We have previously developed a mathematical model of typhoid fever transmission dynamics , which we used to predict the impact of vaccination on typhoid fever in South Asia [14] , to explain the drivers of the emergence of typhoid fever in Malawi [15] , and to determine the cost-effectiveness of vaccination strategies [16] . Here , we assessed the potential drivers associated with typhoid fever from April 1997 to June 2011 in Kathmandu , Nepal . We further adapted the previous model to investigate whether the epidemic of typhoid fever in Kathmandu was driven by heightened migration , the emergence of organisms with reduced susceptibility against fluoroquinolones and/or increased fitness , or a combination of these factors .
We adapted our previous typhoid fever transmission model [14] to assess factors associated with S . Typhi transmission dynamics in Kathmandu , Nepal ( S1 Fig ) . Briefly , we assumed that individuals in Kathmandu were born susceptible to S . Typhi infection ( S1 ) and were infected at rate ( λp+ λw ) , which is composed of short-cycle transmission from the immediate environment ( λp ) and long-cycle transmission from the broader environment and contamination of water ( λw ) , the latter occurring with a slight delay . Infected participants with a primary infection ( I1 ) were assumed to be infectious for a period of 1/δ and then either recovered ( R ) , became chronic carriers ( C ) or succumbed to the infection . We assumed that a fraction ( α ) experienced disease-induced mortality , a fraction ( θ ) became chronic carriers , which varied with age [17] , and that the remaining ( 1-α-θ ) individuals recovered . Recovered individuals ( R ) did not have life-long protection; we assumed that immunity waned at rate ω , which resulted in individuals becoming partially susceptible ( S2 ) . Partially susceptible individuals could be re-infected , but we assumed that this infection was subclinical ( I2 ) and thus not observed in our data . Participants that were infected sub-clinically either recovered ( R ) or became chronic carriers ( C ) . Further , we assumed that all infected participants shed infectious particles into the broader environment ( W ) at rate γ and that the bacteria remained infectious for a period of 1/ζ . Chronic carriers and sub-clinically infected participants had a reduced infectiousness by factor r . Additionally , the model assumed that the environmental transmission parameter ( βw ) varied seasonally , e . g . due to seasonal variation in rainfall [18] . Model parameters are described in Table 1 and a more detailed description of the model , with model equations can be found in the S1 Text . In preliminary analyses , we found that the basic reproductive number of short-cycle ( R0p ) and long-cycle ( R0w ) transmission were not well identified even though the overall basic reproductive number ( R0 = R0p + R0w , defined as the expected number of secondary cases from an infectious individual in a fully susceptible population ) was well identified in the fitted models . Therefore , we attributed a fixed proportion of R0 to R0p and R0w , and examined how the model fit and estimated parameters varied for different proportions . For our primary analysis , we assumed 20% of transmission occurred via the short-cycle and 80% via the long-cycle , as previous studies have found a large variety of genotypes within typhoid clusters in this setting , suggesting that most transmission occurred via the long-cycle [19] . Further , we observed that the model tended to over-predict the proportion of cases in those <5 years of age among participants in randomised clinical trials ( RCTs ) evaluating treatment strategies for typhoid fever . We assumed this was due to under-reporting of cases in this age group ( e . g . due to different symptoms or less severe disease in children <5 years old and/or the exclusion of the <2 year olds from the RCTs ) and allowed for a reduction in the reporting fraction ( k ) in this age group . We assessed the robustness of our results to these assumptions in sensitivity analyses . We assessed five different scenarios that were all modifications of a baseline model , based on hypotheses regarding the possible drivers of typhoid transmission dynamics in Kathmandu . In the baseline model , we assumed no changes in the epidemiology and transmission of typhoid over time . We estimated the basic reproductive number ( R0 ) , the amplitude of seasonality ( q ) and seasonal offset ( l ) for the long-cycle transmission , and a reporting fraction ( f ) to scale the predicted number of infections to the number of cases observed ( Table 2 ) ( details given in S1 Text ) . We observed a higher incidence of typhoid fever among men in young adulthood ( Fig 1 ) , particularly in 2005 , and hypothesised that this was due to an influx of susceptible male workers that migrated to Kathmandu from rural settings where they were previously unexposed to typhoid ( Scenario 1 ) . We assumed that individuals in the 15–25 year old age groups migrated into the population and entered the fully susceptible state ( S1 ) at a constant rate and estimated the week when immigration started ( timmig0 ) and ended ( timmig1 , with timmig1>timmig0 ) and the number of weekly immigrants during that period ( immigration ) , in addition to the four parameters in the baseline model ( Table 2 ) . We also evaluated the emergence of antimicrobial resistant ( AMR ) S . Typhi , which in Nepal is primarily S . Typhi with reduced susceptibility against fluoroquinolones , on the transmission dynamics of typhoid by allowing for an increase in the duration of infectiousness ( Scenario 2 ) or allowing for an increase in bacterial fitness , related to the emergence of H58 organisms ( Scenario 3 ) . For Scenario 2 , we hypothesised that the emergence of AMR S . Typhi would increase the time taken to clear the infection , and therefore we allowed for a linear increase in the mean duration of infectiousness ( 1/δ ) . We estimated the time of emergence of AMR S . Typhi ( t0 ) , when the prevalence stabilized ( t1 , with t1>t0 ) , the magnitude of the corresponding increase in the duration of infectiousness ( m ) , as well as the parameters in the baseline model ( Table 2 ) . For Scenario 3 , we assumed the increased fitness of AMR S . Typhi was associated with an improved growth rate [24] , and therefore allowed for a linear increase in both the short- and long-cycle transmission parameters ( βp and βw ) . Similarly , we estimated the time of emergence ( t0 ) , when the prevalence stabilized ( t1 , with t1>t0 ) , and the magnitude of the increase ( m ) in transmissibility , as well as the four parameters in the baseline model ( Table 2 ) . Finally , we determined whether the pattern of typhoid fever in Kathmandu was the result of both the migration of susceptible individuals and the emergence of AMR S . Typhi organisms , either through an increase in duration of infectiousness ( Scenario 4 ) , for which we estimated all parameters in Scenarios 1 and 2 , or an increase in bacterial fitness ( Scenario 5 ) , for which we similarly estimated all parameters in Scenarios 1 and 3 ( Table 2 ) . For all scenarios , we conducted three sensitivity analyses . First , we allowed for a different appropriation of R0 to the long- and short-cycle pathways to assess the robustness of our results to the primary mode of disease transmission . Second , we assessed the sensitivity of the results to assumptions about the relative infectiousness of chronic carriers and individuals with subclinical infection , with two different fixed model parameters ( details given in S1 Text ) . Finally , we determined whether the low proportion of cases in those aged 0–5 years was a result of reduced exposure , rather than reporting of cases , and allowed for a reduced short- and long-cycle force of infection for this age group . Weekly data on the number of typhoid fever cases from April 1997 to June 2011 were obtained retrospectively from laboratory records at Patan hospital , Kathmandu . Patan hospital is a government hospital in the Kathmandu Valley , which provides emergency , out- and in-patient services . All inpatients and outpatients with suspected bacteraemia at Patan hospital have a blood culture performed . A case of enteric fever was defined as a positive blood culture for either S . Typhi or Salmonella Paratyphi A; the data consisted of laboratory records of the total number of blood culture confirmed enteric fever cases , as well as the number that were positive for S . Typhi or S . Paratyphi A . We did not have access to patient records . There were no known changes in diagnosis and blood culturing practices over the time period of the analysis . For one year , from June 2001 to May 2002 , we only had data on the total number of enteric fever cases . Therefore , we estimated the missing number of S . Typhi and S . Paratyphi A cases for that year as the weekly number of enteric fever cases for that period times the proportion of enteric fever cases that were diagnosed as S . Typhi and S . Paratyphi A throughout the entire study period . Sex- and age-specific data was obtained from patients in three RCTs conducted at Patan hospital evaluating the treatment of typhoid fever . The trials , all open-label randomised superiority trials , measured the treatment outcomes for gatifloxacin versus cefixime [25] , gatifloxacin versus chloramphenicol [26] , and gatifloxacin versus ofloxacin [27] . Eligible study participants were recruited between June 2005 and May 2009 from the emergency and outpatient department at Patan hospital . Participants had a fever for more than three days and were clinically diagnosed with enteric fever . Exclusion criteria applied to those who were pregnant , lactating , <2 years of age , weighing <10kg , had signs of severe typhoid fever ( such as shock , jaundice or gastrointestinal bleeding ) , or had a previous known treatment with antimicrobials within a week of hospital admission ( except when pretreated with amoxicillin or cotrimoxazole , as long as patients did not show evidence of clinical response ) . Additional details regarding the design and conduct of the RCTs can be found in Pandit et al . [25] , Arjyal et al . [26] and Koirala et al . [27] . We fit the model to weekly data on the number of S . Typhi cases between April 1997 and June 2011 and also to the age distribution of S . Typhi cases collected during the RCTs by maximum a posteriori estimation . To estimate the parameters for each model , we first specified an initial parameter set , within a reasonable parameter range . We then calculated the overall log-likelihood for each parameter set , which consisted of two components: ( 1 ) the log-likelihood of the number of weekly observed S . Typhi cases and ( 2 ) the log-likelihood of the age-specific number of S . Typhi cases ( details given in S1 Text ) . Briefly , we assumed that the number of weekly observed S . Typhi cases was Poisson-distributed , with mean equal to the model-predicted number of cases ( equal to the number of infections , summed over all age groups for each week , over the duration of infectiousness times the reporting fraction ) . For the log-likelihood of the age-specific number of S . Typhi cases we assumed that it was multinomially distributed , with the probability equal to the model-predicted proportion of cases in each age group ( in five-year age groups from 0 to ≥80 years of age ) and the number of events equal to the number of observed cases in each age group . We obtained an overall log-likelihood for each model by summing the log-likelihoods of the model fitted to the Poisson-distributed time series and multinomially distributed age distribution plus the prior log-likelihood of the model parameters . To determine the optimal parameter set , we minimised the negative overall log-likelihood using a simplex search method ( using the ‘fminsearch’ command in MATLAB 8 . 6 . 0 ) and the parameter set with the highest posterior probability was selected to obtain the best-fit model for each scenario . We assumed uniform prior distributions for all model parameters and calculated the Akaike information criteria ( AIC ) for model comparison for each best-fit model ( Table 2 ) . Moreover , we assessed whether our parameter estimates converged by varying the initial starting values ten times .
The best-fit baseline model did not adequately predict long-term trends in S . Typhi cases ( Fig 2A ) . This model overestimated the number of cases from 1997 to 2000 and failed to capture the increase in cases between 2000 and 2007 . For scenario 1 , which assumed a constant weekly migration of fully susceptible individuals 15–25 years of age , the best-fit model provided a better fit to the data and accurately predicted the trend of cases ( Fig 2B ) . Notably , the best-fit model identified a peak of 47 cases in August 2002 , which was consistent with the peak in the real data . In this scenario , the model estimated that 342 susceptible people migrated weekly between January 2001 and July 2002 . Although the best-fit model captured nearly all the seasonal peaks of S . Typhi , it failed to predict the peak in May 2000 . Scenario 2 assumed that the emergence of AMR S . Typhi would lead to an increase in the duration of infectiousness . The best-fit model again provided a reasonably good fit to the data , although with a higher AIC compared to scenario 1 ( Fig 2C ) . The best-fit model appeared to capture all peaks in the observed data and estimated an increase in the duration of infectiousness from 4 to 8 . 4 weeks between March 1999 and February 2002 . When we assumed that the increase in S . Typhi was a consequence of elevated bacterial fitness and thus an increase in the transmission parameters ( scenario 3 ) , the best-fit model again showed an acceptable fit to the observed cases similar to scenario 2 and with a higher AIC than scenario 1 ( Fig 2D ) . The basic reproductive number in this scenario increased from 2 . 1 to 4 . 0 between January 2000 and June 2002 . Scenario 4 assumed a constant weekly migration of 15–25 year old susceptible individuals and the emergence of AMR S . Typhi throughout the study period , resulting in an increase in the duration of infectiousness . The best-fit model estimated a weekly migration of 296 susceptible people between December 2000 and March 2002 and an increase in the duration of infectiousness from 4 . 0 to 6 . 0 weeks between August 1998 and February 2005 ( Fig 2E ) . The duration of migration was slightly shorter than in scenario 1 , although the estimated rate of migration was comparable . Additionally , the increase in duration of infectiousness was protracted in this scenario in comparison to scenario 2 and plateaued at a shorter duration of infectiousness than in scenario 2 . The best-fit model provided a good fit to the data and performed better , with respect to AIC , than the best-fit model in scenario 2 and comparably to the model in scenario 1 . Scenario 5 mimicked scenario 4 , although in this case the emergence of S . Typhi was attributed to an increase in bacterial fitness . The best-fit model predicted a weekly migration of 1043 susceptible people from May to September 2001 ( Fig 2F ) . This was a shorter time period than in scenario 1 , although the rate of people migrating was more than twice as large as in scenario 1 . The model also estimated an increase in the transmissibility of S . Typhi , with R0 increasing from 2 . 8 to 5 . 3 between September 1998 and August 2005 . In terms of AIC , the model performed equally as well as the best-fit models in scenarios 1 and 4 . All models fit the age distribution of S . Typhi cases among children under the age of five years , adolescents aged 15 to 19 and those older than 30 years reasonably well ( Fig 3 ) . However , the model-predicted age distribution for cases in the other age categories varied widely between scenarios . In particular , scenarios 2 and 3 , which provided a poorer fit to the weekly number of cases , underestimated the number of typhoid cases in the 20–29 year olds . However , the latter two scenarios provided the best fit for the proportion of cases aged 10 to 14 years , while scenarios 1 , 4 and 5 under-predicted this proportion of cases , particularly in 2005 . All models over-predicted the proportion of cases among 5–9 year-olds . All model scenarios predicted a similar proportion of chronic carriers for each age category ( Fig 4 ) . The proportion of chronic carriers among those younger than 20 years was up to 1% , after which the proportion increased linearly until it reached a maximum of approximately 10% for those 50 years or older . Our models converged to comparable parameter estimates , and the findings were generally robust to attributing different proportions of R0 to R0p and R0w except when R0 was wholly attributed to the short-cycle basic reproductive number ( S2 and S3 Figs ) . Moreover , reducing or increasing the relative infectiousness of chronic and short-term carriers in the sensitivity analysis resulted in lower and higher values of R0 , respectively , but the best-fit models still provided a good fit to the data ( S4 and S5 Figs ) . Finally , attributing the low proportion of S . Typhi cases in those under five years of age to reduced short- and long-cycle exposure resulted in best-fit models that did not differ , in terms of AIC , from the best-fit models in the main analysis , with the models again providing a good fit to the weekly number of S . Typhi cases and to the age distribution of cases ( S6 and S7 Figs ) .
Typhoid fever is endemic in Nepal , with Kathmandu coined “the typhoid capital of the world” [28] . We assessed the drivers of typhoid fever in Kathmandu from April 1997 to June 2011 . During this period , the burden of typhoid fever increased markedly from January 2000 to December 2003 , after which the epidemic declined , but to a higher endemic level than in 2000 . Our analysis is consistent with the hypothesis that the epidemics were caused by the migration of susceptible individuals to Kathmandu and aided by the emergence of S . Typhi with reduced susceptibility against fluoroquinolones . The burden of typhoid fever in rural Nepal has been recently estimated to be commonly over diagnosed , as the prevalence of blood culture confirmed cases was low ( <1% ) , suggesting that individuals in rural settings have low immunity to infection [29] . In Nepal , young men often migrate from rural areas to cities in their mid- to late-teens to pursue further education or in search of employment . A survey by the Nepalese government in 2007 suggested that high rates of internal migration occur . They identified that in nearly 40% of the country’s households , at least one person travelled away from home for employment in the last 12 months , with those most likely to migrate in search of employment being men between 15 and 30 years of age [13] . The timing of the migration estimated by our best-fit models , from December 2000 to July 2002 , also coincided with a period of political instability in Nepal . During this period , Nepal experienced an increase in violence due to Maoist rebel forces , especially in the Nepalese countryside , which also was likely to impact dramatically on migration from rural areas to Kathmandu [30] . The emergence of AMR S . Typhi organisms is a well-established problem in South and Southeast Asia . Following the emergence of multi-drug resistant ( MDR ) S . Typhi , which are resistant to chloramphenicol , amoxicillin and co-trimoxazole , in the 1990s , fluoroquinolones have been the preferred treatment option [31] . However , the widespread use of fluoroquinolones in Nepal in since the mid 1990s has led to a shift in the drug resistance pattern; an increase in S . Typhi exhibiting reduced susceptibility to fluoroquinolones ( as indicated by resistance to nalidixic acid ) coincided with a decline in the prevalence of MDR S . Typhi [8 , 9] . Moreover , in Nepal , the increase in typhoid fever cases broadly corresponds to the introduction of the dominant H58 lineage [10–12] . The H58 S . Typhi variants have been associated with reduced susceptibility to fluoroquinolones , particularly in South and Southeast Asia [11] . The reduced susceptibility to fluoroquinolones is due to mutations in the DNA gyrase gene ( gyrA ) and the topoisomerase gene ( parC ) [32 , 33] . These mutations are not associated with a fitness cost , and may even impart a fitness advantage , enabling widespread proliferation in the absence of antimicrobial pressure [24] . The emergence of S . Typhi with reduced susceptibility to fluoroquinolones and/or introduction of H58 variants were supported by our best-fit models , which predicted an increase in disease duration or transmissibility beginning in August/September 1998 . While this is broadly consistent with the timing of the emergence of the H58 haplotype and reduced susceptibility to fluoroquinolones in Nepal [9 , 11] , the scenarios we modelled were agnostic to the specific AMR profile . A comparable analysis of the recent emergence of typhoid fever in Blantyre , Malawi , identified MDR S . Typhi and/or the emergence of H58 variants as the primary driver of the increased number of typhoid fever cases [15] . However , typhoid fever was previously endemic in Kathmandu , and therefore our model suggests the impact of the emergence of H58 and associated AMR would not have been as profound as in Blantyre , where previously a very low incidence of S . Typhi infections was reported [15 , 34 , 35] . The sex- and age-specific data that led to the investigation of migration as a potential driver of the increase in typhoid cases in Kathmandu and to which the models were fitted were from participants enrolled in clinical trials . These age-specific data also showed a very low number of S . Typhi cases among those aged under five years . This was mostly likely due to the exclusion of <2 years olds from the RCTs; we accounted for this by allowing the reporting rate of cases to vary in the <5 year olds . In contrast , in Dehli ( India ) and Dhaka ( Bangladesh ) , <5 year olds were amongst those with the highest number of typhoid fever cases [36 , 37] . However , it is also possible that the dearth of cases in this age group is due to reduced exposure of young children to S . Typhi or different or less severe symptoms in young children . Understanding the true age distribution of typhoid fever burden , particularly in young children , has important implications when considering typhoid vaccination strategies in this setting . Chronic carriage is an important but poorly understood part of S . Typhi epidemiology . A proportion of infected cases will become chronic carriers , defined as shedding typhoid in their stool for at least one year; the vast majority of these individuals are asymptomatic [38] . Chronic carriage has been associated with gallbladder disease , and previous studies have found that S . Typhi was prevalent in 3% of individuals in Kathmandu , aged 23 to 39 years , undergoing cholecystectomy [39] . This is congruent with the model-predicted proportion of chronic carriers among those aged 20 to 40 years , which was predicted to be 2–6% in these age groups ( Fig 4 ) . Other Salmonella serovars are also prevalent in South and Southeast Asia , such as S . Paratyphi A in Nepal [7] and non-typhoidal Salmonella ( NTS ) in Vietnam [40] , and may confer cross-protective immunity to S . Typhi infection . This hypothesis was evaluated for NTS in Malawi using a comparable mathematical modelling approach , but was not found to explain the pattern of S . Typhi infections [15] . The natural history of S . Paratyphi A is not fully understood , which would complicate the incorporation of S . Paratyphi A in our model . However , the interaction amongst Salmonella serovars remains a subject that should be further explored . An important caveat of our approach is that our model is an over simplification of typhoid fever transmission dynamics . We assumed homogeneous mixing , such that susceptible and infected individuals randomly come in contact . Moreover , we used age- and sex-specific data from RCTs between 2005 and 2009 , which does not overlap with the estimated time period of migration in our best-fit models . Unfortunately , no other age- or sex-specific data on the patient population or data on migratory patterns in Nepal was available . Furthermore , we did not account for spatial patterns in the distribution of cases nor for potential environmental and host risk factors ( aside from age ) [18 , 19] . Many of these factors are the subject of on-going research [41–43] , and evidence from epidemiological and clinical studies concerning these factors will enable us to incorporate this in future mathematical models . Our findings are consistent with the hypothesis that the increase in S . Typhi infections in Kathmandu , Nepal , was due to the migration of susceptible male workers into Kathmandu and may have been further aided by an increase in S . Typhi with reduced susceptibility to fluoroquinolones . The emergence of AMR typhoid fever is an important public health problem in Nepal , with few antimicrobial drugs remaining as treatment options [44 , 45] . This underlines the importance of vaccination and other control measures , such as water , sanitation and hygiene approaches , to prevent disease transmission and infection . Identifying and targeting migrant populations should be an important component of these efforts . | Typhoid fever is endemic in Nepal , with Kathmandu coined “the typhoid capital of the world” . We developed a mathematical model to assess the importance of migration and antimicrobial resistance on the transmission of typhoid fever in Kathmandu , Nepal from April 1997 to June 2011 . During this period , the burden of typhoid fever increased markedly from January 2000 to December 2003 , after which the epidemic declined , but to a higher endemic level than in 2000 . Our findings are consistent with the hypothesis that migration of susceptible individuals into Kathmandu played an important role in the epidemic , and may have been further aided by the emergence of typhoid fever with reduced susceptibility against fluoroquinolones . This study showed that identifying and targeting migrant populations with control efforts could be an important avenue to prevent typhoid transmission and disease . | [
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] | 2017 | The impact of migration and antimicrobial resistance on the transmission dynamics of typhoid fever in Kathmandu, Nepal: A mathematical modelling study |
Traditional approaches to cognitive modelling generally portray cognitive events in terms of ‘discrete’ states ( point attractor dynamics ) rather than in terms of processes , thereby neglecting the time structure of cognition . In contrast , more recent approaches explicitly address this temporal dimension , but typically provide no entry points into cognitive categorization of events and experiences . With the aim to incorporate both these aspects , we propose a framework for functional architectures . Our approach is grounded in the notion that arbitrary complex ( human ) behaviour is decomposable into functional modes ( elementary units ) , which we conceptualize as low-dimensional dynamical objects ( structured flows on manifolds ) . The ensemble of modes at an agent’s disposal constitutes his/her functional repertoire . The modes may be subjected to additional dynamics ( termed operational signals ) , in particular , instantaneous inputs , and a mechanism that sequentially selects a mode so that it temporarily dominates the functional dynamics . The inputs and selection mechanisms act on faster and slower time scales then that inherent to the modes , respectively . The dynamics across the three time scales are coupled via feedback , rendering the entire architecture autonomous . We illustrate the functional architecture in the context of serial behaviour , namely cursive handwriting . Subsequently , we investigate the possibility of recovering the contributions of functional modes and operational signals from the output , which appears to be possible only when examining the output phase flow ( i . e . , not from trajectories in phase space or time ) .
Human ( and animal ) function is thought to emerge from the embedded dynamics of the organism in its natural and social environment [1] , [2] . Its phenomenology thus includes overt behavior ( for instance motor behavior ) as well as processes internal to the organism . By implication , human function comprises multiple interdependent ( i . e . , coupled ) dynamics operating on a diversity of time scales . The ensemble of dynamics and interactions amongst them constitutes a functional architecture . A common theme in biology and the life sciences is that ( ‘complex’ ) function is decomposable into elementary functional units ( or building blocks ) that can thus be considered as the basic components of functional architectures . Functional units should preserve some of their properties invariant among different utilizations ( which identifies them as units ) . As building blocks , they are brought into meaningful relationships ( such as concatenation in time ) resulting in longer sequences . Consequently , the resulting complex processes exhibit a meaningful hierarchical structure spanning distinct time scales . For instance , movement ( or motor ) primitives [3]–[6] and motor programs [7] , [8] have been proposed as building blocks of complex and sequential movements . In birdsong notes and syllables ( groups of notes ) are thought to compose hierarchical sets with a crucial role in their production [9] and perception [10] . ‘Temporal primitives’ have been linked with related lexical items in speech perception [11] and are identified elsewhere [12] as “articulatory gestures that constitute the phonemic elements of both speech generation and perception” and thus “the primitives underlying linguistic communication” ( p . 188 ) . Cognitive linguistics [13] , [14] supposes that language and cognition are based on so-called conceptual schemas and cognitive mechanisms ( as e . g . , metaphors and blends [15] , [16] ) to compose conceptual structures as complex as mathematics [16] . In all these instances , ongoing cognitive ( in its broadest sense ) function results as elementary units are somehow put into a meaningful relationship . Two approaches , symbolic computation and connectionism , have dominated cognitive modeling over the last decades . Both define static information representations and focus on operations ( ‘computation’ ) for the generation of complex function . Symbolic computation explicitly represents information in terms of organized symbols that are combined via syntactic rules [17] . In connectionist models , parallel computation occurs via patterns of activation distributed across the network's nodes [18]–[20] . Hybrid cognitive architectures combine symbolic representations with connectionist learning algorithms [21] . Common to these approaches is that they define functional units as static patterns or ‘states’ onto which cognitive architectures converge in the process of information processing ( in this context; the generation of complex function ) [22] . Even when dynamics beyond point attractors is included ( as in recurrent neural networks [23]–[25] ) , it is usually broken down into a succession of discrete states ( encoding past context ) or transients inside a basin of attraction [25] and treated as such . Identifying functional units as static patterns or states , however , is clearly at odds with one of the cornerstones of biology , namely the process . The notion of a process signifies change; basic functional units should thus contain temporal evolution . In the approach below we explicitly focus on the dynamical phenomenology of human function instead of on computation . Accordingly , we first propose a general framework in which functional modes—dynamical building blocks into which processes are grounded [26]—capture the geometry of elementary processes' dynamics . Functional modes are brought together into meaningful organizations unfolding in time via a ‘selection’ mechanism that ‘turns processes on and off’ . Some functional modes operate autonomously , while others require that quasi-instantaneous ‘kicks’ initiate their functioning . Crucially , so-defined functional architectures embed dynamics that operate on the time scale of the basic functions ( adhering to the functional modes ) as well as a dynamics whose corresponding time scale is defined over the entire eschewing process ( adhering to the selection mechanism ) . A third characteristic times scale pertains to the ( typical ) involvement of the brief ‘kicks’ . In other words , the architecture comprises a time scale hierarchy . Next , we construct an autonomous functional architecture providing proof of concept and illustrating the approach in the context of handwriting . In this specific realization we implement the time scale hierarchy of the functional architecture through a Winner-Take-All ( WTA ) competition dynamics [27]–[29] on one level of organization , and a dynamical sequencing mechanism on another . We want to stress , however , that while the outline of the functional architecture is general , the implementation is but one realization of numerous possible ones . The cornerstones of our functional architecture are ( i ) their constitution as low-dimensional phase-flow governed spatiotemporal patterns ( functional modes ) describing processes; and ( ii ) a hierarchical multi time-scale organization allowing for pattern competition of the functional modes . Due to a competition of processes one functional mode dominates during a particular time window . The notion that human function emerges in terms of low-dimensional spatiotemporal dynamic patterns is key to coordination dynamics [30] , [31] and , more generally , synergetics [32] . The latter , a physical theory of self-organized pattern formation , postulates that in the proximity of ( pattern ) instabilities ( brought about by critical values of control parameters ) the dynamics is separated into fast and slow variables . The fast variables can be adiabatically eliminated by expressing their dynamics as a function of their slow counterparts , in which case the former are ‘enslaved’ by the latter ( the ‘slaving principle’ ) . That is , reduced system descriptions for the collective dynamics ( order parameters ) can be derived . Low-dimensional order parameters thus provide functional representations of high-dimensional system . Synergetics has been successfully applied to the perception of ambivalent patterns [33] , [34] as well as to behavioral coordination [35] . Coordination dynamics models the dynamical phenomenology of the emerging patterns in experimental paradigms of bimanual [36] , [37] , sensorimotor [38] , and social coordination [39] , and learning [40] as low-dimensional , nonlinear dynamical systems via a few ( usually one or two ) order parameters ( see [30] for an overview ) . Consistent therewith , we adopt the notion that human function is constituted by meaningfully structured low-dimensional patterns , the ‘Structured Flows on Manifolds’ ( SFMs; see Figure 1 ) [41] , [42] . Accordingly , during the engagement in a specific function , the functional dynamics adiabatically collapses from an inherently high-dimensional space onto a functionally relevant subset of the phase ( state ) space , the manifold . On the manifold , a phase flow is prescribed and a trajectory evolves for the duration of the functional process . SFMs aim at linking the dynamics of large-scale brain networks interacting with bodily and environmental dynamics ( high-dimensional systems ) to low-dimensional phenomenological descriptions of functional ( or behavioral ) dynamics . Hence , functional processes are ‘encoded’ in terms of structured phase flows , mathematical ( structured ) entities that unambiguously and quantitatively describe the evolution of autonomous , deterministic , and time-continuous systems in their phase ( state ) space ( see [43] for an introduction ) . Phase flows not only encode a system's past and future states ( given any initial condition and in the absence of stochastic influences ) but also its stability and response to perturbations . The vector field describing a flow establishes causal relationships among the system' states by assigning at each state a vector determining the next state . Furthermore , the phase flow topology uniquely determines a system's qualitative behaviour , i . e . , it encodes the invariant features of a dynamical process relative to quantitative variation , thus identifying all functional possibilities within a class in a model-independent manner . Indeed , structured phase flows ( on low-dimensional manifolds ) satisfy the requirement that the dynamics be meaningfully structured , referred to elsewhere as dynamical constituency [44] . In planar systems ( systems of two dimensions ) , common phase flow topologies include point attractors and limit cycles ( commonly used to model discrete and rhythmic functions , respectively ) and separatrices , that is , structures that locally divide the phase flow into opposing directions , endowing the system with threshold properties and ( potentially ) multistability [45] . Such 2-dimensional flows have led to ( confirmed ) counter-intuitive predictions on false starts [46] , the discovery of a discontinuity in Fitts' law [47] , and the establishment of a taxonomy of discrete and rhythmic movements [48] . For systems of higher ( still relatively low ) dimensionality , the dynamic repertoire may contain a large variety of functional modes that are in principle adequate to account for elementary processes . In summary , phase flows can be viewed as functional units that incorporate the properties of low-dimensionality , class-defining invariance together with within-class variation , executive stability ( i . e . , performance maintenance in the presence of perturbations ) , meaningful structure ( dynamical constituency ) , and compositionality ( i . e . , they can be embedded into a larger functional organization . We will use the term functional modes to refer to phase flows incorporating this set of properties , and refer to the ensemble of modes that an actor has to his/her proposal as the dynamical repertoire . The second feature of the functional architecture , i . e . , its multi-time scale character , is founded on the fact that complex processes arise in an organism-environment context that inherently covers multiple scales , as the above mentioned examples suggest . Indeed , multiscale architectures have proven a promising choice to describe behavioral , cognitive , or brain dynamics [49]–[53] . Armed with functional modes as essential building blocks , we propose additional dynamics ( called operational signals ) on time scales slower and faster than that of the modes . The slower process effectively binds functional modes together into sequences . More precisely , a given functional mode emerges via a competition process [27]–[29] to temporally dominate the functional dynamics , after which it destabilizes and gives way to another mode . The transient dynamics between modes can be triggered either by ‘internal’ events ( as in pre-constructed sequences ) or by ‘external’ ones ( such as perceptual events ) . The modes' temporal attractivity guarantees functional robustness , whereas transitions between modes flexibility for meaningful changes . Further variability in the functional dynamics may potentially arise via additional dynamics operating on times scales faster than ( or similar to ) that of the modes . Accordingly , human function is organized in multilevel dynamical hierarchies . In sum , functional architectures combine invariant features ( phase flows ) with those that vary across distinct instances of a functional mode's appearance in an agent's behavior ( via multiscale operational signals involved; e . g . , due to different contexts ) . For instance , syllables are ( largely ) invariant units but their embedding in words and phrases depends on context . See [26] for a classification of operational signals based on time scale hierarchy , computational evidence for the ‘efficiency’ of composing complex behaviors out of simpler ones , and functional architectures in particular . We formulate a functional architecture for serial processes ( see [54] for a classic study and [55] for a review ) , and exemplify it in a specific implementation for cursive handwriting . Handwriting is a typical human behavior involving parallel functions that are related to processing across multiple levels: the linguistic , semantic or word , graphemic , allographic or letter , and stroke level [56] , [57] . Observations of a principal periodicity in normal cursive handwriting of ∼5 Hz [58] and a slower one ∼1 Hz ( 3–4 characters ) [59] supports the presence of multiple time scales in cursive handwriting . As pointed out above , the architecture sequentially ‘selects’ functional modes via a competition process among the slow operational signals . In our handwriting example , the modes code for specific characters ( or parts thereof ) . For a word to arise ( functionality ) , specific modes ( characters ) have to dominate the functional dynamics at an appropriate serial order . We model the serial order behavior using a variation of Competitive Queuing models ( CQ ) [60]–[63] , a class of state of the art models for serial behavior ( for a recent review including behavioral and brain data , see [55] ) . CQ is based on parallel representations of learned sequences; at each stage of a given sequence , the participating elements compete for their activation following an order of priority . We opted for a CQ variation as these models allows for competition dynamics , which has been successfully used in the context of handwriting and related kinematical phenomena [64] . The CQ is not a defining ingredient of our architecture , however . The architecture models the interaction of processes acting on different time scales . A dynamical repertoire accounts for the generation of cursively written characters and a slower competitive dynamics ( operating on the word-generation scale ) activates the corresponding modes at appropriate times . These mechanisms are feedback coupled from the output trajectories to the slow competition . An additional instantaneous operational signal , which is ( sometimes ) used for movement initiation by providing a meaningful perturbation , is coupled to the modes' and competition dynamics . Word generation thus emerges autonomously from the multi-time scale , high-dimensional system , which is constructed out of simpler constituent ones . Below , we present the mathematical formulation of the general functional architecture ( Methods ) , after which we provide proof of concept via simulations of a specific architecture generating a desired cursively written word ( Results ) . Subsequently , we show how our framework can be used to identify functional units ( i . e . , how to decompose human function into its generating components ) . As will become evident , the latter is not trivial due to the multi-scale hierarchy generating observable trajectories . By implication , the slow dynamics is an envelope of the functional modes' dynamics , rendering the resulting process non-stationary [65] , [66] . The difficulty of the functional decomposition is reflected in attempts to identify programming units in serial behavior [54] , cursive handwriting , in particular . Based on word presentation – movement initiation reaction times , movement time scaling relative to the number of hypothesized units ( strokes , letters , graphemes , or syllables ) and their individual movement times , interletter times , and errors or measures of disfluency , syllables [67]–[69] , complex graphemes [70] such as digraphs [71] , letters [72] , [73] , and single or pairs of up and/or down strokes [74] have all been ascribed this role . As we will show , however , the decomposition into functional units based on the system' functional output is likely compromised due to the generating system' multi-scale character .
We first briefly review the formulation of Structured Flows on Manifolds ( SFM ) [41] , [42] , [75] , [76] which reads ( 1 ) where the so-called ‘smallness’ parameter µ is constrained as 0<µ<<1 , g ( . ) defines the manifold , f ( . ) describes the subsequent flow on it , and h ( . ) represents the fast dynamics that rapidly collapses onto the manifold; here and below τ is the time constant of the fast contraction onto the manifold . Due to µ being small , the dynamics collapsing on the manifold is much faster than that pertaining to the phase flow . The flow is constrained on the manifold for an appropriate attractive function g ( . ) . Unlike the center manifold theory [77] , which is a local theory valid around instabilities only , systems of the form of equation ( 1 ) need to contain an inertial manifold [78] , a global structure used in the reduction of infinite dimensional dynamical systems to finite dimensional spaces . Systems exhibiting inertial manifolds have to be dealt with on a case-by-case basis . SFM can be generated by distributed multi-component systems such as networks of firing-rate neural populations if multiplicative couplings and small connectivity asymmetries are present [41] . The former provide the necessary non-linearities whereas the latter allow for the emergence of the flow on the manifold . Ongoing work [75] , [76] attempts to encode SFM into networks of spiking neural populations . Here , we consider SFM as the macroscopic functional dynamics that emerges from interactions in a high-dimensional system ( an agent ) under environmental constraints and perturbations . After adiabatically eliminating the fast variables sj ( by solving for sj ) , the dynamics of a functional mode is described as: ( 2 ) Thus , we consider a functional mode F ( {ui} ) as a ( transiently emerging ) N-dimensional functional dynamics in {ui} space originating from a ( {ui} , {si} ) -space of much higher dimensionality M . In its most general formulation , we can describe a functional architecture through its flow F ( . ) in phase space potentially subjected to additional operations ( for a detailed treatment see [26] ) : ( 3 ) where {ui} are the system's state variables and σ ( t ) is a time-dependent operational signal that , if constant in time ( ) , renders the process autonomous . In that case , F ( {ui} ) is identified as the SFM of a particular functional mode . The dynamical repertoire is the set of functional modes available to an agent; it represents the ensemble of elementary functions that appear in relatively invariant manner across different instances of the agent's behavior . In contrast , the operational signals are task-specific dynamics that operate upon the modes in a context-dependent fashion . Operational signals may evolve on various time scales relative to the functional modes and can in principle span a continuum of scales . Let τf = τ/µ and τσ denote the time scales corresponding to a particular functional mode and operational signal σ ( t ) , respectively . Following [26] , we distinguish four different instantiations of time scale separations . In cases in which σ ( t ) acts much faster than the functional mode ( i . e . , τσ<<τf ) , σ ( t ) operates ( quasi-instantaneously ) upon the mode and we denote it as δ ( t ) . In cases where σ ( t ) acts on a time scale similar to that of the functional mode ( i . e . , τσ≈τf ) , σ ( t ) may be said to operate the functional mode , and we write it as η ( t ) . In cases in which σ ( t ) acts much slower than the functional mode ( τσ>>τf ) , we write it as ξ ( t ) . Finally , in cases in which σ ( t ) can be considered as time-independent ( i . e . , σ ( t ) ≈constant during the process; i . e . , τσ→∞ ) , the mode is autonomous . Functional modes ( F ( {ui} ) ) and operational signals ( ξ ( t ) , δ ( t ) ) compose functional architectures in the spirit of physics of pattern formation [28] , [32] , where spatiotemporal patterns are expressed as a linear combination of a few dominating modes . The critical and novel concept we introduce here is that the modes correspond to elementary processes ( expressed as SFM ) rather than static spatial patterns . Thus , at each moment in time , the expressed phase flow is given as a linear combination of all functional modes available in an agent's dynamical repertoire: ( 4 ) where {ui} are the state variables and Fj ( . ) is the j-th mode . ξj acts as a weighting coefficient for the j-th mode , is constrained to positive values , and operates on a slower time scale than that of the functional modes ( even though transitions between modes involving fast contraction on the respective manifold are fast ) . That is , {ξj} ‘select’ a particular mode Fj during its activation phase ( when ξj = 1 and all other {ξk} = 0 , for k≠j ) . Figure 1 sketches the resulting multi-scale dynamics as the transient emergence of a spirally structured flow on a cylindrical manifold for the time that the {ξj} dynamics stays in the neighborhood of a particular node . Recall , next to the slow dynamics that changes the ( expressed ) flow topology , the architecture provides for the optional involvement of the instantaneous operational signal {δi ( t ) } that does not affect the flow and that acts as a functionally meaningful ( context-specific ) perturbation . For example , the {δi ( t ) } can move the system beyond a threshold ( separatrix ) and initiate a significant change in the trajectory's evolution . To reiterate , the ensemble of subsystems ( Fj ( {ui} ) , ξj , {δi ( t ) } ) operating on distinct time scales ( τδ<<τf<<τξ ) constitutes the functional architecture as summarized in Figure 2 . In the following sections we provide an illustration of how the {ξj} dynamics can be designed to organize functional modes so that more complex functions emerge , in particularly serial order behavior . We require that the modes' activations do not overlap and implement a ‘Winner-Take-All competition’ ( WTA ) for the {ξj} dynamics: ( 5 ) where τc is a time constant ensuring that the competition evolves fast , The competition evolves among modes with Lj>0 , and its outcome is determined by parameters {Cj} and {Lj}: the ‘winning’ ξj is the one with . The competition dynamics has one unstable node at the origin , one point attractor ( the ‘winner’ ) at , all other equilibrium points being saddles nodes ( constraining the dynamics for {ξj}>0 ) . ( For a linear stability analysis of all equilibrium points of this system , see Text S1; for the phase space of a 2-dimensional WTA competition , see Figure S1 Supporting Information . ) Thus , functional modes are organized via mutual competitive interactions . Such a functional mode decomposition based on a competition scheme follows previous work on the Synergetic Computer [28] , and is well established in the literature of biological competition [27] . An alternative to the WTA competition could be the winner-less competition based on transient heteroclinic sequences [52] , [53] , as also used in [75] . In order to model serial order , suitable dynamics has to be designed so as to activate the appropriate functional modes sequentially with the correct timing . Our here chosen implementation is inspired by Competitive Queuing models of serial behavior [60]–[63] that combine parallel representations of alternative ‘action plans’ with a competition process that selects the action to be executed next . The competition is due to lateral inhibition among the candidate actions; the order of activation depends on a so-called primacy gradient ( i . e . , a gradient of excitation across the sequence's elements ) . Every executed action is via inhibitory feedback excluded from the competition for the remaining of the sequence . Accordingly , in our implementation , at each stage of the sequence , the functional modes compete ( via the {ξi} ) , one of them wins , dominates the output dynamics for the duration of its activation and is subsequently inhibited , after which the competition continues among the remaining available modes . Equation ( 5 ) implements the competition among modes by means of mutual inhibition . The order of activation depends on the {Cj} parameters ( the primacy gradient in this case ) ; Lj>0 is the condition for mode j to take part at a specific competition round . Thus , parallel representations of the sequence ( encoded in the arrays of {Cj} parameters ) are combined with serial processes of WTA competition . The timely inhibition of an active mode is achieved through a ‘bottom-up’ coupling ( feedback ) from the output {ui} to the slow operational signal: . Here we describe this bottom-up feedback in detail . We introduce the index , indicating the specific order of the sequence , starting with mode j = 1 and terminating at j = K′ , while running among the K′ modes ( out of a repertoire of K modes in total ) that participate in a sequence J . The feedback is mediated by a 2-dimensional differential equation per functional mode of J ( variables {νj} and {λj} , respectively; see below ) . First , {ui ( t ) } is slowly integrated , ‘informing’ the competition dynamics about the course of execution of a particular mode j via the linear differential equation: ( 6 ) where τν is the time constant and kjexc and kjinh are time scale parameters . Finh ( . ) and Fexc ( . ) are feedback functions , inhibitory and excitatory , respectively . Finh ( . ) results in the slow increment of the ‘feedback integrating’ variable νj while mode j is being executed , whereas Fexc ( . ) resets νj to 0 when the sequence is completed ( or equivalently , when the last mode K′ has been executed ) . Second , the slow integration above triggers fast transitions of the ‘switching’ variables {λj}: ( 7 ) where τλ is the time constant , S ( νj ) is a sigmoidal function ‘sharpening’ the effect of νj and limiting it to the interval [0 , 1] , and sign ( . ) returns the sign of its argument . λj transits fast to a point attractor at λj = 0 when S ( νj ) →1 and , inversely to a point attractor with |λj| = 1 when S ( νj ) →0 . For intermediate values of S ( νj ) ( far from 0 and 1 ) the system is bistable; the above transitions are thus characterized by hysteresis . ( For more details on the dependence of the phase space structure of equation ( 7 ) on parameter S ( νj ) as well as the functional forms of the feedback functions Finh ( . ) , Fexc ( . ) , and the sigmoidal S ( . ) , see Text S1 as well as Figure S2 of the Supporting Information ) Finally , {λj} are inserted into the competition equation ( 5 ) determining , as mentioned above , the availability of a functional mode to participate in the competition ( or inversely its inhibition ) via parameters {Lj} as well as the outcome of the competition via parameters {Cj} . The Ls transit fast between values 0 and 1 following: ( 8 ) Thus , a functional mode j participates in the competition ( Lj = 1 ) when neither j nor any of the subsequent modes in the sequence are inhibited . The modes that do not form part of sequence J still take part in the competition ( ) but fail short due to their low {Cj} activations . The {Cj} transit fast to C0 ( and vice versa ) from a value greater than C0 according to: ( 9 ) Since Lj = 1 for all modes participating in the competition , the winner j at each time moment is the one with . In sum , the transitions of {λj} , {Lj} and {Cj} , occurring due to feedback from the output , affect ( the outcome of ) the {ξj} competition , which in turn selects a different functional mode to dominate the architecture's dynamics at each stage of the sequence . In fact , as the different ingredients of the functional architecture are intricately coupled in various ways , the {ξj} competition is effectively influenced by all the relevant variables constituting the architecture . As for the characteristic time scales of each one of these variables and parameters , {νj} integrate the output at a time scale much slower than the one of functional modes ( τv>>τf ) , whereas the transitions of {λj} , {Lj} and {Cj} , as well as the evolution of ‘individual competition rounds' are fast ( τλ , τc<<τf ) . The transition times notwithstanding , during the execution of a particular functional mode the corresponding variables and parameters remain relatively constant . Indeed , the resulting trajectory of the {ξj} dynamics , which passes sequentially from the neighborhood of each mode of the sequence ( where it dwells for a long time during a mode' activation ) , exhibits a time structure that is defined across the entire sequence . As such , the effective time scale that determines the serial dynamics ( referred to as τξ above ) is slow , following approximately the time scale ( τν ) of the integration in equation ( 6 ) ( τξ≈τv>>τf ) . A glossary of all variables and parameters , as well as the time constants ( as used in the simulations ) rendering the architecture time scale hierarchical can be found in Table 1 . As proof of concept we demonstrate the application of the functional architecture via a typical example of serial motor behavior , namely cursive handwriting . Here , the state variables are ( x , y , z ) , whereas a repertoire of K = 37 functional modes is used implementing characters ( or parts thereof ) modeled as 3-dimensional SFMs . Please note that in choosing K = 37 , the dynamical repertoire is much larger than the number of modes required to establish the task required ( see below ) , as is typically the case . The manifold , the surface of a cylinder with an ellipsoid basis where dynamics unfolds along the x-axis , is chosen to be common for all characters ( for implementation reasons but without loss of generality ) . Thus , the form of the functional dynamics ( exemplifying equations ( 2 ) and ( 4 ) ) is: ( 10 ) where ri is the radius and ci the center of the manifold . y and z obey Excitator-like dynamics [45] ( except for two auxiliary linear point attractor phase flows ) that has been proposed as a unifying framework for rhythmic and discrete movements . Depending on whether , , or , the system exhibits a limit cycle ( rhythmic behavior ) , a point attractor with a separatrix ( monostable system with threshold properties ) , or two point attractors with a separatrix between them ( bistable system ) , respectively . The smallness parameter µ provides the time scale separation responsible for the fast contraction on the manifold , while a second parameter µelc/mn/bi , guarantees the time scale separation that is necessary for the threshold properties of Excitator phase flows . The form of fxj ( . ) yields the desired letter shapes . The modeling strategy consists in modulating the velocity on the x-axis relative to the one on the y-axis , according to the direction of velocity and the position of y , by means of sigmoidal functions ( see Supporting Information Text S1 for detail; Table S1 for parameter values ) . Finally , the functional mode dynamics ( properly scaled and positioned via rj and cj , respectively ) drives the dynamics on the handwriting workspace ( i . e . , the xy-plane ) . The dynamics of δy , z is a function of ( y , z ) , ( Fy , Fz ) as well as of{ξj} . Its implementation is based on firing a δ-‘kick’ when the system approaches a point attractor ( see Supporting Information Text S1 for the generation mechanism ) . Together with fξ ( {ui} ) , they render the functional architecture autonomous ( Figure 3 illustrates the couplings among the components of the architecture ) . The joined contributions of dynamics faster ( the {δi ( t ) } ‘kicks’ and {ξj} transitions ) and slower ( overall {ξi ( t ) } dynamics ) than the functional modes constitute a time scale hierarchy ( see Table 1 ) . The functional architecture is simulated using the Euler-Maruyama method with a fixed time step [79] and normally distributed noise with zero mean and standard deviation s . The code is implemented using GNU Scientific Library ( GSL ) [80] C-code integrated with MATLAB . Noise has an additive contribution to the deterministic dynamics and ensures the robustness of the output by facilitating transitions upon destabilization of a previously stable mode to the next one . Although the effect of noise was not studied systematically , we would like to emphasize that the timing of the transitions depends on feedback rather than on random fluctuations for intermediate amounts of noise , and is thus adequately robust . The parameters were not systematically regulated so as to optimize the output dynamics ( a short trial-and-error process was carried out based on visual inspection of the output ) . The parameters to simulate the word ‘flow’ presented in the Results were set as follows: noise standard deviation s = 0 . 001 , kjinh = [6 , 12 , 5 , 5 , 2 . 67 , 6] , and kjexc = [12] , [11] , [10] , [9] , [8] , [7] for each mode in the sequence , respectively The initial conditions for the functional mode dynamics were x0 = 0 , y0 = 0 . 1 , and z0 = −0 . 1 , while those of {νj} , {λj} , and {ξj} where chosen randomly from a uniform distribution in the interval [0 , 1] for {νj} and {λj} , and [0 , 1/K] for {ξj} . With regards to the analysis of simulated data , the word ‘view’ was generated in 100 trials with either the same initial conditions or with initial conditions drawn from a small neighborhood of the phase space with a uniform distribution . All trials where integrated for the same time duration and sampled with the same frequency resulting into data sets with an equal number of data points . For the analysis , the mean and standard deviation of y ( t ) , z ( t ) , dy ( t ) //dt , dz ( t ) //dt {ξj ( t ) } , δy ( t ) and δz ( t ) were calculated for all trials across each time point , referred to as mean and standard deviation time series , and denoted as wµ ( t ) and ws ( t ) respectively ( where ‘w’ may be y , z , dy/dt , dz/dt ξj , δy or δz ) . ( x was excluded from this analysis because it does not provide any relevant information about the functional modes phase space geometry since is not a function of x ) . The mean time series was used as a guide in order to estimate the phase space trajectories as well as the flow as follows: For each trial and each time point of the mean time series yµ ( t ) and zµ ( t ) ( excluding short segments at the beginning and end of the data sets ) , we searched for the nearest neighbor of ( the mean of ) y and z in phase space ( i . e . , the one with the minimum Euclidian distance ) in a time window of Tw = 300 time points ( smaller than half a movement cycle ) centered around that time point . Thus , the trials' data sets were rearranged such that their corresponding data points were as close as possible in the y-z phase plane . Next , the remaining variables of the data sets ( dy/dt , dz/dt , {ξj} , δy and δz ) were rearranged accordingly so as to correspond to the related ( y , z ) point . We then calculated the mean and standard deviation of the rearranged y , z , dy/dt , dz/dt , {ξj} , δy and δz datasets across all trials at each data point , denoted as wµ and ws respectively ( again , ‘w’ may be y , z , dy/dt , dz/dt ξj , δy or δz ) . Notice that dy/dt and dz/dt , calculated at intermediate steps of the integration algorithm , can only approximate the deterministic phase flow as they contain the additive stochastic contribution of noise to the dynamics , and that the approximation of the integration algorithm is better for choices of smaller time steps dt .
In the following we provide proof of concept that complex functions can be composed of elementary processes organized in an autonomous hierarchy . In the corresponding cursive handwriting illustration , a word is generated via functional modes , each of which ”writes” a character . In other words , functional modes do not code for the individual characters , but rather for the processes involved in generating them . This subtle but fundamental differentiation characterizes our approach towards the emergence of functional dynamics . To be concrete , Figure 4 presents the simulation of the word ‘flow’; it shows the output dynamics and operational signals involved ( panel A ) , and the feedback from the output dynamics to the slow sequential {ξj} ( panel B ) . Four principal functional modes were used , one for each character: ‘f’ , ’l’ and ‘o’ are implemented using monostable phase flows requiring one δ-‘kick’ for the initiation of each movement cycle [45] , whereas ‘w’ is implemented as a limit cycle phase flow ( no external timing is required ) . Two auxiliary ( linear fixed point ) phase flows are used at the beginning and end of the sequence setting appropriate initial and final conditions . The word is robustly generated repeatedly thrice after a short initial transient due to the random initial conditions . Notice in particular how Figure 4 illustrates the distinct time scales of the interacting processes: As can be seen in Panel A , the main time scale of the output dynamics pertains to a movement cycle even though a longer ( slower ) time structure ( at the word scale ) is present also . This slow time scale dominates the {ξj} dynamics . In contrast , δy , z is much faster than the output . ( See Figure S3 , Supporting Information for the generation of δy , z ) . In Panel B , the inhibitory feedback Finhj can be seen to evolve at the ( main ) time scale of the output , whereas the feedback integrating variables vj , the absolute values of the ‘switching’ variables {λj} as well as the WTA competition parameters {Cj} and {Lj} are slower ( except for fast transitions ) . As mentioned in the Introduction , phase flow topologies provide the means to classify functional modes , and allow for quantitative variation under qualitative invariance . In order to demonstrate this feature , here exemplified by scaling a functional mode’s phase flow with respect to the radius of the manifold ( or movement amplitude ) , we doubled the manifold’s radius of character ‘w’ is ( default r = 1 ) to r = 2 , and halved it to r = 0 . 5 . All dynamical properties of the output , captured by the shape of its time series , remain invariant ( see Figure 5 ) . This scaling of movement velocity with movement amplitude illustrates the so-called isochrony principle [81] , which is a well-documented phenomenon in motor behavior and handwriting , in particular . We next investigate if and how complex processes arising in functional architectures can be decomposed into their dynamical components . Recall , the presence of multiple time scales [65] , [66] and nonlinearities render this problem far from trivial . As outlined in the Introduction , this difficulty is evident in the quest for programming units of handwriting , and reflected in the numerous different proposals thereto [67]–[74] . Under the assumption that complex processes are composed of invariant functional modes ( except for transitions ) and context-specific operational signals , one would expect locally increased variability among trials’ observable trajectories ( i . e . , pertaining to the functional modes ) where operational signals become effective . Moreover , if these operational signals induce transitions between modes and/or introduce meaningful perturbations , these segments of increased variability should generally be of a shorter duration than the characteristic time scale of functional modes . We tested if these two predictions can be used to separate the effects of operational signals on the architecture’s output from the functional mode dynamics , in order to isolate and identify the latter . Thereto , 100 trials of the word ‘view’ were generated . Its dynamics is composed of functional modes based on a monostable phase flow with the point attractor at the position ( y* , z* ) = ( 2 , 0 ) ( character ‘v’ ) , a bistable phase flow with point attractors at positions ( y* , z* ) = ( 0 , 0 ) and ( y* , z* ) = ( 2 , 0 ) ( character ‘i’ ) , another monostable flow with the point attractor at position ( y* , z* ) = ( 0 , 0 ) ( character ‘e’ ) , and a limit cycle phase flow ( character ‘w’ ) [45] . Regarding transitions in the corresponding phase space structure , it should be noted that no topological changes ( locally around the point attractor ) are present in the first and second transition ( only quantitative variation occurs ) , while , in contrast , in the third transition a point attractor destabilizes via a Hopf bifurcation [43] giving rise to a limit cycle . Here , we focus on segments of increased variability ( among trials ) of the operational signals ( corresponding to segments of {ξj} transitions or of application of δ-‘kicks’ ) and examine their effects on the output variability . Figure 6 shows the means and standard deviation time series of all architecture components . It appears that the δ-‘kicks’ markedly effect the mean and standard deviation of dy ( t ) /dt and dz ( t ) /dt as brief additive contributions . On the contrary , {ξj} variability , although affecting the dy ( t ) /dt and dz ( t ) /dt standard deviation , this is hardly distinguishable from the standard deviation's variability that is due to the ( slightly ) different initial conditions and/or stochastic influences . Crucially , when performing the same analysis to data sets that have been rearranged so that the corresponding data points refer to neighboring points in phase space within a small time window ( see above ) , the effect of the {ξj} becomes evident as well . In addition to the phenomena observable in Figure 6 , Figure 7 reveals that an increased {ξj} variability ( occurring at the moments of transitions between modes ) goes hand in hand with an increase in the standard deviation of dy/dt and dz/dt . These latter variables ( i . e . , dy/dt and dz/dt ) provide an approximation of the phase flow . This phenomenon is caused by the variable changes of the flow due to the {ξj} variability ( {ξj} do not transit identically among trials because of noise ) rather than a topological flow change ( see Figure S4 of Supporting Information ) . Note also that the effect of mode transitions cannot be identified unambiguously in the variability of the phase space trajectory ( ys and zs ) . ( The analysis was performed for noise with standard deviation ten times larger ( s = 0 . 01 ) as well , delivering results that are in general agreement with the above presented ones , as illustrated in Figures S5-7 of the Supporting Information ) . In sum , the effects of {ξj} transitions or δ-‘kicks’ perturbations can be located by focusing on the variability of the output phase flow ( approximated here by dy/dt and dz/dt ) among trials . Both phenomena are short lived due to their intrinsic time scales ( fast transients and instantaneous ‘kicks’ , respectively ) but can be distinguished because the instant δ-perturbations are evident in the mean of dy/dt and dz/dt , which is not the case for the {ξj} transitions . Thus , when mode transitions are identified , a sequence can be segmented into periods where different functional modes dominate the dynamics . Subsequently , evident perturbations can be disregarded as external influences on the functional modes . Finally , the remaining dynamics within a time segment can be considered as an approximately stationary process generated by a particular functional mode . The latter can be recovered by techniques of phase flow reconstruction such as the ones based on Fokker-Planck formalisms [48] , [82] , [83] .
We presented a functional architecture comprising multiple subsystems operating on distinct time scales: a dynamical repertoire of functional modes modeled as SFMs , slower operational signals organizing the modes via a Winner-Take-All competition as well as faster ones acting on the modes as meaningful events or perturbations . Crucial to our approach is the idea that functional modes characterize prototypical processes . As proof of concept , we illustrated our approach by generating a cursively written word , a typical instance of serial behavior . Our framework represents a theoretical perspective on process execution and the organization of complex ( human ) function via a hierarchy of interacting time scales . The approach we adopt , based on modeling motor , perceptual as well as complex cognitive functions in a deterministic , dynamical way , enhances explanatory powers in the context of a specific scientific methodology: abstract dynamics modeling the essentials of biological phenomenology constrain mechanistic models of finer biological detail and suggest possible classes of generating mechanisms; they then feed back to the experimenter with further implications and intuitions based on nonlinear dynamical systems' theory . The presented functional architectures operates on low-dimensional dynamical patterns ( functional modes ) that explicitly model the specific dynamical structure of an elementary human function ( quantitatively as well as qualitatively ) , and satisfy the requirement for dynamical constituency [44] ( viewed here as meaningful structure in the phase ( or state ) space ) . By modeling functional modes as dynamical processes instead of states , it may be possible to naturalize ( human ) function while minimizing complexity reductions typifying traditional approaches [17] , [18] , [21] . Moreover , the hierarchy of time scales , as a principle of organization , conciliates continuous dynamics with the ‘discrete’ nature of a repertoire of distinct functional modes . Our approach presents similarities and differences with related ones pushing forward heteroclinic sequences or chaotic attractors to account for brain and cognitive dynamics . For instance , over the last few years , Rabinovich and colleagues developed an approach centering on ( cognitive ) change via the introduction of heteroclinic cycles [52] , [53] , which is similar in spirit to ours in several ways , importantly so in focusing on the time structure of cognitive processes . In a nutshell , in their approach the system ( cognitive agent ) sequentially transits from one unstable equilibrium point ( a saddle ) to another . Due to the nature of the equilibrium points , the transitions are typically fast and short lived relative to the time spent in their neighborhood ( i . e . , time-scale separation ) . A drawback of sequences built on equilibrium points , however , is that their corresponding processes are functionally rather constrained . True , while dynamical objects more complex than ( unstable ) equilibrium points , such as limit cycles or even chaotic attractors , can be placed at the nodes of a heteroclinic sequence [84] , [85] , this potential has to our best knowledge not yet been applied to behavioral and cognitive modeling . The limitation to transitions among equilibrium points limits the explicit formulation of the ‘shape’ of given dynamical processes and provides no obvious entry points to their classification . In contrast , our hypothesized range of possible ( low-dimensional ) dynamical objects ( SFM ) provides a natural entry point to the classification of cognitive events [84] , [85] . Moreover , heteroclinic cycles become slower and slower as a trajectory approaches a saddle point ( or subspace ) , and , importantly , the timing of transitions or the effect of a week perturbation scale with the amount of noise so that robust timing is difficult to achieve . In contrast , in the present implementation , feedback ensures the robust timing of transitions albeit at the expense of an increase in the architecture's dimensionality . The issue of robustness pertains to chaotic attractors ( as generated by recurrent neural networks [86] , [87] ) too: Although they may exhibit dynamics of arbitrary complexity , they are sensitive to initial conditions and thus fail to account for the robustness of human functioning . Our theoretical framework is complementary to the Bayesian theory proposed in [49] . In a series of papers , hierarchies of transient dynamics were developed in order to account for brain [88] , perceptual [50] , [51] and behavioral [89] phenomena . In those studies generative models , based on non-linear dynamics , and hierarchical organizations thereof were proposed that can be considered as equivalent to functional modes and architectures respectively . In perception the high levels of the hierarchy encoded slow contextual changes in the environment as the underlying causes of the faster sensory dynamics , the temporal structure of which was captured by the lowest level [50] , [51] . In motor behavior slow high-level dynamics were proposed as prior expectations about proprioception , which enslaved the peripheral ( faster and low-level ) motor system [89] to fulfill them . This Bayesian approach , however , focuses rather on the statistical computation that this dynamics implements in order to ‘tune’ the human and animal brain to the causality structure underlying human–environment interactions ( as described by generative models and hierarchies thereof ) as well as on the basic principle governing this computation ( minimization of uncertainty quantified as free energy [90] ) . Instead , our work focuses on the actual dynamical objects ( and their interactions ) that can provide us with dynamical descriptions of human function' phenomenology; in other words , on what we can learn from the proposed generative models . The two approaches , being complementary , could be combined in fruitful ways in future work . We illustrated our approach by generating an instance of serial behavior , cursive handwriting , as proof of concept . Our model shares common elements with a previously proposed CQ-model for handwriting [64] that reproduced several phenomena observed in the kinematics of human handwriting such as the 2/3 power law as well as the isochrony principle [81] . Attributing different functional roles to the sequence and character generators ( serial and functional mode dynamics , respectively ) resembles the dual ( motor and cognitive ) processors model for sequence production [91] , [92] ) . In the latter model , the prime role of the cognitive processor shifts ( with practicing ) from executing to initiating sequences as the gradual development of motor chunks allows a motor processor to execute them . However , there is no explicit reference to the characteristic time scales of each one of these processes . The architecture' dynamics ( and particularly the functional modes of the handwritten characters ) were not constructed with the particular aim to implement biologically realistic kinematics , but to demonstrate the general principles of functional architectures and focus on the interactions between their distinct time scales . However , although biologically realistic dynamics for the modes or a more sophisticated serial dynamics can in principle be constructed based on experimental data , it would not change the nature of our theoretical framework . Moreover , phenomena of dynamics connecting subsequent characters [93] , in other words , character variability due to the context of neighboring characters , were not addressed . Doing so either requires a bigger ( and more biologically realistic ) repertoire of phase flows or the design of a mixed parallel-serial [57] , [94] architecture with additional operational signals operating on a time scale similar to the one of the functional modes ( τη≈τf ) that would contribute to ‘sewing’ characters into words . However , the inclusion of phase flow modifications at that time scale is the most costly in terms of operational signal complexity [26] . The output {ui ( t ) } of the architecture represents the observable behavioral trajectory , in our specific implementation , the endpoint trajectory ( x ( t ) , y ( t ) , z ( t ) ) of handwriting , whereas the operational signals {ξj ( t ) } and {δi ( t ) } correspond to internal variables . Only {ui ( t ) } allows for a direct mapping to behavioral observables , observations of perceptual and other internal contributions must be inferred . The implemented feedback loop that informs the competition process about the evolution of an active functional mode's trajectory likely incorporates both sensory and internal ( or ‘planned’ ) effects; their relative contribution may well depend on the extent to which the movement is automatized . In principle , however , sensory feedback can be explicitly introduced at different levels of the architecture . For instance , it can contribute to the competition between modes , or trigger a fast perturbation to initiate a movement at the correct timing . Sensory feedback can also parameterize the dynamics of a functional mode at time scales similar to the one of functional modes , for instance when a high degree of precision is required . We further investigated the possibility to identify the functional modes underlying the sequence generation , an endeavor that an experimentalist might find herself faced with . Our results suggest that the various sources contributing to the variance in and across instantiations of a process cannot be unambiguously delineated when focusing on a process' evolution as it unfolds in time . Rather , they urge the experimenter to focus on phase space analysis in order to identify the functional components of serial processes and their interactions . Moreover , our results indicate that unlike phase transitions that occur as the relevant state variable ( e . g . , relative phase ) transits from one stationary value to another , and that are observed in behavioral and sensorimotor coordination [30] and revealed by increased ( phase space ) trajectory variability , identifying transitions between ( possibly non-stationary ) processes requires analysis of the variability of the phase flow . To that aim , more elaborate methods of phase flow reconstruction could be considered , such as the one employed in [48] , [82] , [83] based on Fokker-Planck formulations . However , important modifications or extensions have to be made for those methods to be able to handle non-stationary processes as well [82] , [83] and our results may contribute to this end by probing to the importance of time scale separations . The cornerstone of our approach is the SFM concept , according to which the dynamics of a high-dimensional system ( such as the embodied brain ) temporarily collapses onto a low-dimensional manifold containing a structured functional flow . This vision is in line with reports of network dynamics , dynamical models as well as biological data indicating that the ensemble dynamics of populations of neurons may effectively reduce to a structured flow in phase space ( i . e . , a functional mode ) . For instance , recent as well as ongoing work in our lab progresses in designing large scale neural networks of firing rate populations or spiking neurons coding for SFMs and functional architectures [41] , [75] , [76] . Other ( computational ) examples in which a network dynamics generates low-dimensional topological objects in phase space are provided in [95] . Real biological networks of spiking neurons have been reported to generate heteroclinic sequences [96] . Also , central pattern generators , i . e . , relatively small autonomous neural networks , are typically constrained to produce limit cycle dynamics . An explicit example of the generation of a 3-dimensional closed orbit in phase space generated in a central pattern generator of the lobster stomatogastric ganglion can be found in [96] , [97] . Evidence favoring biological realism for the operational signals ( slow and fast ) can be found in the literature too . In that regard , the time scale hierarchies of the organization of sensorimotor interactions are proposed to be reflected in the hierarchical organization of the nervous system , in particular the cortex [49] , [88] . Structurally , the hierarchy is formed via convergence and divergence of forward and backward connections , while their differential functionality introduces a temporal ( and spatial ) separation of scales of operation . Presumably , ( local ) processes in the primary areas occur faster than the modulating influences thereon from the higher levels . For instance , oscillation in the human β and γ band ( corresponding roughly to 13–30 Hz and 30–100+ Hz , respectively ) are thought to be associated with feature integration ( i . e . , content related ) while the slower θ and α band ( corresponding roughly to 4–8 Hz and 8–13 Hz , respectively ) are presumably involved in top-down regulations adjusting the faster processes in a context-dependent fashion [98] . The instantaneous signals δ ( t ) have been associated with timing ( ‘or clock’ ) mechanisms [48] , [99] . In fact , the notion of brief pulses initiating timed movements is well established in the psychological literature [100] , [101] , and is accompanied by a plentitude of neuro-imaging studies aiming to identify the corresponding anatomical substrate ( for a review , see [102] ) for which the cerebellum [103]–[105] and basal ganglia [102] have been put forward as candidate structures . According to the ‘Good Regulator’ theorem ( a central theorem in Cybernetics due to Ashby and Conant [106] ) , any regulator that is maximally successful and simple must be isomorphic with the system being regulated . Whether this applies to the relationship between the neural system ( the ‘regulator’ in our case ) and human behavior as the SFM framework implies remains an open question . However , initial results of ongoing experimental work on EEG imaging in a behavioral paradigm of rhythmic versus discrete movements [107] and existing literature [37] , [38] , [108]–[110] are open for interpretation along these lines: In those studies , low-dimensional behavioral patterns and transitions among them ( here {ui} and {ξj} dynamics , respectively ) were associated with corresponding low-dimensional spatiotemporal modes in EEG and MEG dynamics and their transitions . In particular , preliminary results in [107] reveal that low-dimensional EEG patterns ( at the low frequency range ) appear to be isomorphic to the behavioral ( movement ) patterns . How these patterns relate to the oscillations ( and synchrony across them ) in the α , β , and γ-ranges is still an open question . In any case , the potential isomorphy of dynamics ( more specifically phase flows ) between brain and behavioral signals offers an intriguing entry point towards the understanding of representation in the human brain . Our framework is compatible with embodied intelligence approaches , since functional modes may be isomorphic to patterns of closed sensorimotor loops or human-environment interactions . The property of dynamical isomorphy or topological equivalence of sensorimotor interactions offers a novel perspective to phenomena such as motor equivalence [8] , [111] and sensory substitution [112] , [113] . Motor equivalence refers to the fact that humans can accomplish a given goal via different ‘motor means’ as in using different effectors' systems ( writing with one's foot ) , or , in the present context , via different hierarchical organizations [26] . Sensory substitution is the phenomenon that sensorimotor interactions utilizing a given sensory modality can be effectively substituted by other interactions using another modality . According to our approach , what remains invariant among such different behavioral or sensorimotor patterns would be the ‘shape’ of their dynamics , that is , their meaningful structure in the phase space . The argument holds even if one considers the so-called ‘cognitive’ topology [15] , [114] to be different than the mathematical one [44] , [115] . We demonstrated how the appropriate choice of the functional ‘circuitry’ ( i . e . , serial dynamics ) within the available dynamical repertoire can lead to the emergence of more complex functions such as serial order behavior . The latter is an example of how such ‘circuitries’ among functional modes can prescribe different causal relationships between them , forming a network of elementary processes . Thus , a variety of functional architectures can emerge , even conditional ones , mimicking the IF-THEN rules found in traditional Artificial Intelligence or architectures with coexistence of cooperative and competitive interactions among functional modes . Moreover , although the proposed architecture is presented in a closed form and executes a prescribed serial behavior , internal ( e . g . , goals , affective inputs ) or external ( i . e . , perceptual ) cues could bias the WTA competition via parameters {Lj} , {Cj} , thus ( co- ) determining the flow within such a network of processes . In that manner , arbitrary decision-making or behavioral sequences ( including perception-action coupling ) can be modeled , such as stereotypical interactive behaviors ( e . g . , browsing in the internet or cooking ) . Additionally , a hierarchy of multiple levels of such functional architectures could be designed in order to account for a repertoire of even more complex functions necessary to model more rich mental/cognitive constructions . In any case both functional modes and their organizational interactions would result out of a process of pattern formation in structurally coupled agent-environment systems in an autonomous self-organized manner reflecting the agents’ urge to survive or , in other words , conserve its autonomous organization ( referred to as autopoiesis [1] , [2] ) . The proposed framework suggests that different processes of adaptation or learning are required for such complex function to emerge . The acquisition of an initial repertoire of elementary functions would precede processes constructing functional architectures allowing for a multitude of complex behaviors . At the same time , the initial repertoire could be extended with new functional modes by composition of existing ones that would either ( or not ) qualitatively change the constituent modes ( see [26] ) . The latter mechanism could provide us with the means to model phenomena found in cognitive linguistics literature such as conceptual metaphors and blends [14]–[16] , [114] . Those are cognitive mechanisms responsible for transferring the causality structure of a conceptual schema ( constructed out of generalization over a class of sensorimotor interactions ) to another that is defined in a different conceptual space or domain , as well as for the merging of such domains . In this process , the so-called ‘cognitive’ topology is preserved in order to allow for inference in the target domain based on relationships in the source domain . Another interesting question would be whether the learning dynamics themselves could be described by trajectories generated by distinct phase flows in a relevant phase space , corresponding to qualitatively different learning strategies [116] . Functional architectures , besides dealing with some of the most interesting questions in modern science , the ones concerning human function , can also lead to interesting engineering applications in motor or sensory rehabilitation based on motor equivalence and sensory substitution , as well as in Artificial Intelligence and robotics where multi-time scale functional architectures are already being implemented [117] , [118] . Combined with their neural network implementations , a novel paradigm of analog biologically inspired computation with possible materializations in integrated circuits , such as Very-Large-Scale Integration ( VLSI ) [119] ones , may emerge . | In most established approaches to cognitive modelling , cognitive events are treated as ‘discrete’ states , thus passing by the continuous nature of cognitive processes . In contrast , some novel approaches explicitly acknowledge cognition’s temporal structure but provides no entry points into cognitive categorization of events and experiences . We attempt to incorporate both aspects in a new framework , which departs from the established idea that complex ( human ) behaviour is made up of elementary functional ‘building blocks’ , referred to as modes . We model these as mathematical objects that are inherently dynamic ( i . e . , account for change over time ) . A mechanism sequentially selects the modes required and binds them together to compose complex behaviours . These modes may be subjected to brief inputs . The ensemble of these three ingredients , which influence one another and operate on different time scales , constitutes a functional architecture . We illustrate the architecture via cursive handwriting simulations , and investigate the possibility of recovering the contributions of the architecture from the written word . This appears possible only when focussing on the dynamic modes . | [
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] | 2011 | Time Scale Hierarchies in the Functional Organization of Complex Behaviors |
Each year millions of travelers visit Southeast Asia where rabies is still prevalent . This study aimed to assess the risk of rabies exposure , i . e . , by being bitten or licked by an animal , among travelers in Southeast Asia . The secondary objective was to assess their attitudes and practices related to rabies . Foreign travelers departing to the destination outside Southeast Asia were invited to fill out the study questionnaire in the departure hall of Bangkok International Airport . They were asked about their demographic profile , travel characteristics , pre-travel health preparations , their possible exposure and their practices related to rabies during this trip . From June 2010 to February 2011 , 7 , 681 completed questionnaires were collected . Sixty-two percent of the travelers were male , and the median age was 32 years . 34 . 0% of the participants were from Western/Central Europe , while 32 . 1% were from East Asia . Up to 59 . 3% had sought health information before this trip . Travel clinics were the source of information for 23 . 6% of travelers . Overall , only 11 . 6% of the participants had completed their rabies pre-exposure prophylaxis , and 15 . 3% had received only 1–2 shots , while 73 . 1% had not been vaccinated at all . In this study , the risk of being bitten was 1 . 11 per 100 travelers per month and the risk of being licked was 3 . 12 per 100 travelers per month . Among those who were bitten , only 37 . 1% went to the hospital to get post exposure treatment . Travelers with East Asian nationalities and longer duration of stay were significantly related to higher risk of animal exposure . Reason for travel was not related to the risk of animal exposure . Travelers were at risk of being exposed to potentially rabid animals while traveling in Southeast Asia . Many were inadequately informed and unprepared for this life-threatening risk . Rabies prevention advice should be included in every pre-travel visit .
Rabies remains an important neglected disease worldwide . Approximately 50 , 000–55 , 000 people die from rabies each year [1] . Although most deaths are reported among local people in high endemic area especially in Asia and Africa [2] , travelers in those areas are inevitably at risk if they are bitten by infected animals or if the saliva of an infected animal comes into contact with broken skin or mucosa . Pre-exposure vaccination is an excellent preventive measure against rabies among travelers . However , it is not routinely recommended to all travelers in endemic areas . Its high price and cost-effectiveness are often debated as discussed in many papers [3]–[6] . Travel medicine practitioners should consider several factors , including the risk of being bitten or licked during trips , rabies endemicity and the availability of medical care at the travel destination and travelers' preferences before recommending a vaccine . Among those factors , the actual risk of animal exposure is thought to be a major one [5] , [7] , [8] . Southeast Asia is one of the popular tourist destinations for travelers worldwide . Each year , up to 60 million tourists visit Southeast Asia [9] , where rabies is still endemic and stray dogs and cats are common . Information regarding the risk of rabies exposure among travelers in Southeast Asia is limited . Therefore , in this study , we aim to determine the incidence and risk factors of possible exposure to rabies , i . e . , by being bitten or licked by animals , during their trips in Southeast Asia . The secondary objective was to assess their pre-travel preparation , vaccination rate , knowledge , and practices related to the risk of rabies .
Statistical analysis was conducted using SPSS for Windows , version 10 . 0 . 7 ( SPSS Inc , Chicago , IL ) software . Continuous data were presented as mean with standard deviation ( for normally distributed data ) , or median with range ( for non-normally distributed data ) . Categorical data were presented as numbers and percentage . The Student t-test was used to compare means of two groups , while the Chi-square test was used for categorical data , as appropriate . Relative risk ( RR ) and 95% Confidence interval were calculated to determine factors potentially associated with animal exposure and receiving pre-exposure vaccination . Factors with a p-value below 0 . 10 in the univariate models were considered eligible for the multivariate analysis . In this study , a p-value of <0 . 05 was considered as statistically significant . The research protocol as well as the questionnaire was approved by the Ethics Committee of the Faculty of Tropical Medicine , Mahidol University ( Approval No . MUTM 2010-015-02 ) . Since this study was a voluntary , anonymous survey among adults and was non-experimental in nature; so the Ethics Committee had waived the written consent and approved to imply that filling the questionnaire represent their consent to participate in this study . All participants were informed of the study's objective and grants verbal consent before filling the questionnaires . No participant-identifiable data was recorded in the questionnaire to maintain confidentiality .
Of the 7 , 681 travelers studied , 1 , 809 ( 23 . 6% ) had received pre-travel health advice from a travel clinic; 56% of the travelers in the travel clinic group had received information about rabies , which was significantly higher than travelers who sought pre-travel health advice from other sources ( 56 . 0% vs 37 . 5% , p<0 . 001 ) . 21% of travelers in the travel clinic group had completed a course of pre-exposure rabies vaccine while only 8% of travelers in non-travel clinic group had completed their prophylaxis ( 21 . 4% vs 8 . 4% , p<0 . 001 ) . When the details of traveler knowledge about rabies was analyzed , it was found that most travelers knew that they could get rabies if bitten by an infected animal and that dogs could carry rabies . However , nearly one out of two travelers was not aware that cats could also carry rabies . Moreover , more than one-fourth of travelers thought that the bite of a healthy-looking dog or cat posed no risk of rabies . Subgroup analysis also revealed that the travelers who had visited a travel clinic possessed some more specific knowledge items than those who did not visit the clinic including that being licked by an animal poses a risk of contracting rabies . The mean knowledge score for those who visited a travel clinic was significantly higher than the score of those who had not received pre-travel health advice from a travel clinic . The details are shown in Table 2 . Several factors including female sex , older age , longer duration of stay were found to be related with low vaccination rate . The rate of rabies vaccination also differed among travelers from different continents of origin . Travelers from North America or from Oceania had significantly lower vaccination rate when compare to travelers from Western/Central Europe while travelers from South Asia had significantly higher vaccination rate than travelers from Western/Central Europe . Details are shown in Table 3 . The actual cost of rabies vaccine and its cost index , which was adjusted by the GDP per capita , differed significantly from country to country as shown in Table 4 . Travelers from countries where the vaccine cost index was <20 ( n = 5556 ) were 1 . 4 times more likely to receive vaccination against rabies before travel , compared to those from countries where the cost index was > = 20 ( n = 2125 ) ( 27% vs . 21% , RR 1 . 43 , 95% CI 1 . 27–1 . 61 ) . Of 7 , 681 participants , sixty-six travelers ( 0 . 9% ) had been bitten , while 185 travelers ( 2 . 4% ) had been licked on the average stay of 23 . 2 days . Virtually all countries in Southeast Asia were reported as countries of exposures where travelers had been exposed to animals . The incidence of animal exposure ( bitten or licked ) varied from country by country ranging from 0 . 3% ( 1/325 ) among travelers in Malaysia to 3 . 6% ( 4/110 ) among travelers in Myanmar . The overall animal exposure rate in Southeast Asia was 2 . 8% . Among those who were bitten , information regarding their actual practice after exposure was available in 35/66 travelers . Base on that data , 3/4 had cleaned the wound , but 2/3 did not seek medical care and did not receive post-exposure treatment . The animals most commonly encountered were dogs , followed by monkeys and cats . Detail analysis was performed to determine risk factors that might be related to animal exposure . Age , gender , reason for travel and knowledge score had no influenced on animal exposure while the length of stay and continent of origin had some effects . Travelers from East Asia had a higher rate of exposure than Western/Central European ( Adjusted RR 2 . 83 , 95%CI 1 . 87–4 . 2 ) . Conversely , travelers from South Asia were at lower risk ( Adjusted RR 0 . 20 , 95% CI 0 . 03–0 . 66 ) . Apart from the nationality of travelers , the length of stay was found to be directly related with the risk of exposure . Travelers who stayed more than 20 days had a higher risk than travelers who stayed less than 5 days ( 5% vs 1 . 3% , Adjusted RR 7 . 78 , 95%CI 4 . 71–13 . 01 ) . Detailed of the results are show in Tables 5 and 6 .
To our knowledge , this was the largest study that aimed to determine the risk of animal exposure among travelers . In our study , the risk of being bitten was 1 . 11 per 100 travelers per month and the risk of being licked was 3 . 12 per 100 travelers per month . These incidences were close to the overall estimation of risk published in one recent review . In that review , based on all available evidences [5] , [10]–[13] , it was estimated that 0 . 66% ( 0 . 02%–2 . 31% ) of tourists will experience animal bite during one month stay [6] . It was not possible to compare our incidence rate directly with all previous studies since there were vast variations in term of the population studied , destination , definition of exposure and so on . However , several important points should be noted . Firstly , the highest incidence of animal exposure had been reported among travelers in Thailand in 1994 airport study . In that report , up to 1 . 3% of travelers had been bitten during an average stay of 17 days [11] . Compared to the 1994 study , our study found an approximately two-fold decrease in the risk of being bitten ( 1 . 1% per month VS 2 . 2% per month ) . The lower incidence of animal bite may result from better awareness of rabies among travelers which could by imply from the vaccination rate i . e only 1 . 1% of travelers in the previous study had received rabies pre-exposure prophylaxis while up to 25% of travelers in our study had received rabies vaccine before their trips . Apart from risk of animal bite , the endemicity of rabies in the destination is also the major factor that determines the real risk of exposure to rabies virus . Fortunately , data from Thailand showed that local situation of rabies was much improved when compared to the last few decades . For example , the number of human rabies in Thailand cases had decreased from 185 cases per year in 1990 to 78 cases per year in 1994 and to less than 20 cases annually since 2001 [14] . Moreover the percentage of FAT positive animal specimens among those examined for rabies were also decline i . e . from 41% in 1990 to 28% in 2000 and to 12% in 2004 [15] . Several factors were contributed to this success such as the control of stray dogs and cats , vaccination programs in animals , mass campaigns to raise public awareness and better and more accessible post-exposure treatment [3] , [14] . However it is important to note that , although the rabies situation in Thailand was much better and the risk of being bitten among travelers seemed to be lower than previous report , this risk was still high when compare to the other studies outside Southeast Asia [5] , [13] . Partly , it may be due to the poor control of stray dogs and cats in many countries in Southeast Asia where more than 1 million people are estimated to be bitten annually [16] . Not only local people , but travelers in these areas are inevitably at risk also . Given that rabies is an untreatable disease once the symptoms develop , travelers in rabies endemic areas need a good basic knowledge regarding rabies risk and prevention . Unfortunately , our study found that , travelers' attitudes and knowledge related to rabies risk were far from ideal . As seen in several previous reports [10] , [17] , [18] , many misconceptions and misunderstandings were found among our participants , such as , up to 59% were not aware that they might get rabies after being licked by an infected animal and 50% did not know that they needed a booster vaccination once they were bitten . These misconceptions were critical and might lead to serious consequences if they actually had been exposed to the rabies virus . In our study , we also confirmed that the travelers' practice after being exposed to animal was poor i . e . one fourth of the responding travelers who were bitten had not cleaned the wound and two third of responding travelers did not go to the hospital to get a rabies vaccination . These were serious and dangerous misunderstanding . Therefore , travelers to rabies endemic areas should receive proper advice regarding rabies before their trip . Travel clinic might be a good source of information as found in several studies [10] , [19] , [20] . However , in our study , although travelers who had visited a travel clinic had higher mean knowledge scores than those who did not visit the clinic , some misconceptions were also found in comparable percentage between these two groups of travelers . In this study , the length of stay in Southeast Asia was significantly related to higher rate of animal exposure . Age , gender , and travelers' knowledge , had no significant relationship to rate of animal exposure . Apart from length of stay , multivariate analysis indicated that the nationality of a traveler was related to the risk of animal exposure . Travelers from East Asia had a 2 . 8-fold higher risk than travelers from Western/Central Europe , while travelers from South Asia had a significantly lower risk . These differences might imply that travelers from different cultures might have different attitudes and different risk behaviors that can be related to a higher or lower risk of animal exposure . For example , travelers from South Asia where rabies was highly endemic might have higher rabies awareness than travelers from Europe , so they were less likely to risk encounter with an animal . Through the analysis , we also found that the reason for travel was not related to the risk of animal exposure . Hence the magnitude of risk among tourists , businessmen and students in Southeast Asia could be considered the same . This finding might challenge the general belief that the activities of travelers play some role in terms of risk . Although it is logical to assume that , so far there was no available evidence to support this belief , at least in Southeast Asia . This may be in part be due to the fact that stray dogs and cats in Southeast Asia are not restricted to only certain areas , but rather can wander freely around in urban and rural areas . This might explain why , when compared to our recent study done in backpackers in Bangkok [10] , the risk of being bitten in the backpacker group was even lower than that in general travelers in this study ( 0 . 69 per 100 backpackers per month VS 1 . 11 per 100 travelers per month ) . Similar findings were also reported in a study conducted in Nepal , where trekking did not increase the risk of animal exposure [5] . Although many authorities recommend pre-exposure rabies vaccination in high risk travelers [21]–[23] , there was no consensus what defines “high risk” . In our study , twenty-seven percent of our participants received rabies vaccine before their trips . Several factors including male sex , younger age , travel for tourism and , surprisingly , a shorter length of stay were found to be correlated to higher vaccination rates . We also found that travelers from countries with a cost index <20 were more likely to receive the vaccine . As in many studies , this was confirmed that cost of the vaccine was an important factor that travelers consider before receiving the pre-exposure vaccines [10] , [24] , [25] . Our study had some limitations . Although we surveyed more than 7 , 000 departing travelers from Suvarnabhumi International Airport , which is the main airport hub in Southeast Asia , data from a single airport is not ideal for representing the whole of Southeast Asia . Our data should strongly represent travelers in Thailand and its neighboring countries such as Lao PDR , Cambodia and Vietnam , because most of them use Suvarnabhumi International Airport as a travel hub . But our data may underrepresent people who travel mainly in Indonesia , Singapore and the Philippines , since they may use other airports . Ideally , a multi-airport study could provide more comprehensive data . Second , the language barrier may have led to selection bias in our study . In this study , apart from English , we translated our questionnaire to 3 different languages i . e . Chinese , Japanese , and Korean . However , the questionnaire were not translated into Arabic , Hindi , Spanish , or any African languages . So those travelers from the Middle East , India , Africa and Latin America , who did not understand English , had to be excluded from the study . It is possible that travelers from those areas who understood English and those who did not may have different risk characteristics . Third , children , who represent a recognized at-risk population for animal bites and rabies , [1] , [2] were not included in our survey , which may have biased the results . We could conclude that travelers in Southeast Asia , regardless of their reasons for travel , had a significant risk of being bitten or licked by animals while traveling . A longer duration of stay was associated with a higher risk . However , it must be pointed out that 53 . 8% of travelers with exposure to potential rabies infected animals were actually exposed while traveling for less than 3 weeks . Many were inadequately informed and lacked a basic knowledge of this life-threatening risk . Rabies prevention advice should be included in every pre-travel visit . | Rabies is a fatal disease most commonly transmitted through a bite or a lick of a rabid animal on the broken skin . Most deaths from rabies are reported in Asia and Africa where animal rabies is poorly controlled . Not only local people , but travelers in these areas are inevitably at risk also . In this study we surveyed foreign travelers just before they departed Southeast Asia at Bangkok International Airport . We aimed to determine the risk of possible rabies exposure and their attitudes and practices related to rabies . The risk of being bitten among 7 , 681 participants studied was 1 . 11 per 100 travelers per month and the risk of being licked was 3 . 12 per 100 travelers per month . Among those who were bitten , only 37 . 1% went to the hospital to get rabies post exposure treatment . Travelers with East Asian nationalities and who stay longer were more likely to be exposed to animals . The risk of animal exposure was not related with the reason for travel . These findings confirm that travelers in Southeast Asia were at real risk of possible exposure to rabies . However , most of them were inadequately informed and unprepared for this life-threatening disease . Rabies prevention advice should be given to all travelers in rabies endemic area . | [
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] | 2012 | Risk of Potentially Rabid Animal Exposure among Foreign Travelers in Southeast Asia |
Salmonella enterica serovar Typhimurium is a Gram-negative bacterial pathogen causing gastroenteritis in humans and a systemic typhoid-like illness in mice . The capacity of Salmonella to cause diseases relies on the establishment of its intracellular replication niche , a membrane-bound compartment named the Salmonella-containing vacuole ( SCV ) . This requires the translocation of bacterial effector proteins into the host cell by type three secretion systems . Among these effectors , SifA is required for the SCV stability , the formation of Salmonella-induced filaments ( SIFs ) and plays an important role in the virulence of Salmonella . Here we show that the effector SopD2 is responsible for the SCV instability that triggers the cytoplasmic release of a sifA− mutant . Deletion of sopD2 also rescued intra-macrophagic replication and increased virulence of sifA− mutants in mice . Membrane tubular structures that extend from the SCV are the hallmark of Salmonella-infected cells . Until now , these unique structures have not been observed in the absence of SifA . The deletion of sopD2 in a sifA− mutant strain re-established membrane trafficking from the SCV and led to the formation of new membrane tubular structures , the formation of which is dependent on other Salmonella effector ( s ) . Taken together , our data demonstrate that SopD2 inhibits the vesicular transport and the formation of tubules that extend outward from the SCV and thereby contributes to the sifA− associated phenotypes . These results also highlight the antagonistic roles played by SopD2 and SifA in the membrane dynamics of the vacuole , and the complex actions of SopD2 , SifA , PipB2 and other unidentified effector ( s ) in the biogenesis and maintenance of the Salmonella replicative niche .
The virulence of Salmonella enterica serovar Typhimurium requires its intracellular replication within a membrane-bound compartment called the Salmonella-containing vacuole ( SCV ) . This is achieved by the expression of a Type 3 Secretion System ( T3SS-2 ) encoded by the Salmonella Pathogenicity Island 2 ( SPI-2 ) that enables Salmonella to translocate bacterial effectors into the infected cell [1] , [2] , [3] , [4] , [5] , [6] . More than 20 T3SS-2 effectors have been identified so far , but their molecular activities remain largely unknown [7] . Some of these T3SS-2 effectors display enzymatic activities [8] , [9] , [10] , [11] , [12] or target host proteins [13] , [14] . The T3SS-2 effector SifA is required for the stability of the SCV membrane [15] . The vacuolar membrane instability results in the gradual release of sifA− bacteria into the host cytosplasm . As Salmonella is unable to replicate in macrophage cytosol [15] , [16] , this mutant presents a strong virulence defect in mice [15] , [17] . Salmonella-infected cells are characterized by the presence of membrane tubules enriched in late endosomal markers , known as Salmonella-induced filaments ( SIFs ) [17] , [18] . In addition , a recent study by Mota et al . shows that Salmonella recruits membrane from a TGN-derived Scamp3-containing pathway to induce the formation of Scamp3-positive tubules , comprising the SIFs and the Salmonella-Induced Scamp3 Tubules ( SISTs ) [19] . While SISTs are devoid of late endocytic markers , SIFs are positive for both late endocytic markers and Scamp3 [19] . Both SIFs and SISTs require SifA for their development . Upon translocation , SifA and the T3SS-2 effector PipB2 localize to the cytosolic face of the SCV and recruit the mammalian proteins SKIP and kinesin-1 , respectively [13] , [14] . By interacting with kinesin-1 [13] , SKIP promotes the transport of SCV-derived vesicles towards the cell periphery [20] . A sifA− mutant is unable to recruit SKIP and is therefore defective in the transport of these membrane vesicles [21] . As a consequence , a sifA− SCV accumulates high levels of kinesin-1 [13] . The disruption of the sifA− vacuolar membrane is not due to the PipB2-mediated accumulation of kinesin-1 [14] , [22] but requires the lipase activity of the T3SS-2 effector SseJ [23] . A sifA−sseJ− strain resides in a stable vacuole but displays the same intracellular replication defect as a sifA− mutant [24] . In this paper , we show that deletion of sopD2 in a sifA− mutant stabilizes the SCV and restores intra-macrophagic replication and virulence . It induces the formation of LAMP1- and Scamp3-negative membrane tubules , which constitute a so far undescribed tubular network that forms independently of SIFs and SISTs .
A sifA− mutant is progressively released into the host cell cytoplasm whereas an ssaV− strain is enclosed in a stable vacuole [15] . This suggests that T3SS-2 effectors are responsible for the vacuole instability in the absence of SifA . We confirmed this hypothesis [15] using a sifA−ssaV− mutant whose vacuole stayed intact ( Fig . 1A ) . In order to identify the T3SS-2 effector ( s ) involved in the instability of the sifA− SCV , we tested individually the effect of their deletion in a sifA− mutant strain . HeLa cells were infected with wild-type or mutant bacteria and the percentage of bacteria enclosed in LAMP1-positive vacuoles was determined . Most of the mutants behaved similarly to a sifA− strain in terms of SCV stability ( Fig . 1A ) . In agreement with previous reports [23] , [24] , a sifA−sseJ− SCV was more stable than a sifA− SCV . Remarkably , the deletion of sopD2 in a sifA− strain also significantly increased the percentage of bacteria present in SCVs . We noticed a modest but constant effect on SCV stability upon deletion of pipB2 and found a more pronounced effect upon deletion of sopD2 in a sifA−pipB2− strain ( Fig . 1A ) . The complementation of sifA−sopD2− and sifA−sopD2−pipB2− strains with a plasmid for the expression of SopD2 reduced the percentage of bacteria in SCV ( Fig . 1A ) . We observed a similar complementation using a 2HA-tagged version of SopD2 ( data not shown ) , indicating that both native and 2HA-tagged proteins were functional . We localized SopD2 in infected HeLa cells . We found SopD2 on SCVs , SIFs and cytoplasmic vesicles in cells infected with wild-type Salmonella but its localisation was exclusively restricted to the SCV in absence of SifA ( Fig . 1B ) . These results indicate that SopD2 is present on the sifA− SCV and that it plays a role in the membrane instability of this bacterial compartment . Confocal microscopic observations of infected HeLa cells revealed that wild-type SCVs contained a single bacterium while the sifA−sseJ− mutant formed micro-colonies of 2 to 6 bacteria ( Fig . 2 ) . The sifA−sopD2− mutant was present either in wild-type-like SCVs or in large vacuoles containing several bacteria and LAMP1 inclusions . sifA−sopD2−pipB2− vacuoles containing a single bacterium were rarely found and rather contained several bacteria with large inclusions of LAMP1 ( Fig . 2 ) . An electron microscopy ( EM ) qualitative and quantitative analysis was undertaken to better characterize both the vacuoles and the ultrastructural appearance of the bacteria within these compartments . Bone marrow-derived mouse macrophages ( BMDMs ) infected with the different Salmonella strains were fixed and processed for EM . Bacteria were observed in three types of compartments , namely normal SCVs ( Fig . 3A ) , phagolysosomes ( Fig . 3B ) and autophagic vacuoles ( Fig . 3C ) . The diverse types of vacuole were indistinguishable between strains but the percentages of bacteria within these different compartments or in the cytosol were different as shown by the scores at 12 hr post-invasion ( p . i . ) ( Fig . 3D ) . As expected , over 75% of the WT and 60% of the sopD2− bacteria were confined to SCVs that usually contained a single bacterium , while at least 50% of the sifA− mutant had escaped the SCV . Deletion of sopD2 in a sifA− or sifA−pipB2− strain considerably reduced the number of cytosolic bacteria . However , while about 50% of the sifA−sopD2− bacteria resided in normal SCVs , the sifA−sopD2−pipB2− mutant was predominantly found in autophagic vacuoles . The latter usually contained several bacteria that were mostly morphologically intact . Finally , and for all strains , about 15 to 30% of the bacteria were enclosed in phagolysosomes . Bacteria were either morphologically intact as in Figure 3B ( 30 to 70% depending on strains ) or undergoing degradation . All in all , the EM observations of infected BMDM confirmed that the deletion of sopD2 in a sifA− mutant stabilizes the SCV and showed that the additional deletion of pipB2 increases significantly the proportion of vacuoles of autophagic nature . Since some of the mutants resided in autophagic vacuoles in BMDMs , we asked whether vacuoles previously observed in HeLa cells were also of an autophagic nature . To address this issue , we analysed by immunofluorescence the recruitment of different markers to the SCVs: i ) the FK2 antibody that detects both mono- and poly-ubiquitinylated proteins , ii ) P62 , also known as sequestosome-1 ( SQSTM1 ) , that links ubiquitinated proteins to the autophagic machinery [25] , [26] , iii ) LC3 that binds directly P62 and is a marker for membranes that are undergoing autophagy [27] . While little or no wild-type , sifA− or sopD2− SCVs were positive for these markers , around 20% of sifA−sopD2− or sifA−sopD2−pipB2− SCVs were ubiquitinylated and positive for P62 and LC3 ( Fig . 4A and B ) . The phenotypes displayed by sifA−sopD2− and sifA−sopD2−pipB2− mutants were complemented when a plasmid for the expression of SopD2 was introduced in the corresponding mutants ( Fig . 4A ) . To investigate whether sifA−sopD2− and sifA−sopD2−pipB2− mutants sequestered in the FK2/P62/LC3-positive vacuoles were viable or undergoing degradation , we analysed their metabolic activity using strains that expressed DsRed fluorescence under the control of an arabinose-inducible promoter . HeLa cells were infected and DsRed expression was induced at 14 h p . i . for 4 h before fixing the cells . All the bacteria within P62/LC3-positive vacuoles expressed DsRed ( data not shown ) . Altogether , these results indicate that deletion of sopD2 in sifA− or sifA−pipB2− mutants stabilizes the SCV membrane , which is in 20% of the cases positive for autophagic markers in HeLa cells . The bacteria contained in these vacuoles did not seem to be degraded , as they were still metabolically active at late times post-infection . Given the above data and since Salmonella is unable to replicate in the macrophage cytosol [16] we asked whether deletion of sopD2 would restore bacterial replication in cultured macrophages . RAW 264 . 7 mouse macrophages were infected with wild-type or mutant bacteria and the fold increase of intracellular bacteria between 2 and 16 h after phagocytic uptake was determined ( Fig . 5A ) . As expected [15] , a sifA− mutant had a strong replication defect and a sifA−sseJ− mutant [24] did not replicate better than a sifA− mutant . In contradiction to former studies [28] , a strain lacking sopD2 was not impaired in intracellular replication ( Fig . 5A ) . Deletion of pipB2 in a sifA− mutant had no effect on bacterial replication ( Fig . 5A ) [14] . However , deletion of sopD2 in both sifA− and sifA−pipB2− mutants promoted bacterial replication . Expression of SopD2 from a plasmid reduced replication of the sifA−sopD2− and sifA−sopD2−pipB2− mutant strains ( Fig . 5A ) . Next , we tested the virulence of these Salmonella mutants in vivo by performing mixed infections in C57BL/6 mice [29] . We found that sifA− and sifA−sseJ− mutant strains exhibited similar virulence ( COI = 1 . 2±0 . 9 ) ( Fig . 5B ) while the sifA−sopD2− strain was slightly but significantly more virulent that the sifA− mutant ( COI = 2 . 6±0 . 9 ) ( Fig . 5B ) . Since deletion of pipB2 increases moderately the virulence of a sifA− mutant [14] and since deletion of sopD2 and pipB2 had an additive effect on the vacuole stability ( Fig . 1A ) , we also tested a sifA−sopD2−pipB2− mutant . The virulence of this mutant was highly increased ( COI = 7 . 35±3 . 8 ) . Deletion of pipB that encodes a PipB2 homolog did not significantly change the virulence of a sifA−sopD2− mutant . Compared to the wild-type strain , the sifA−sopD2−pipB2− mutant exhibited only a modest attenuation ( CI of 0 . 63±0 . 27 ) , while our previous data showed a CI of about 0 . 1 for a sifA− mutant [14] . These results indicate that the stabilisation of the sifA−sseJ− vacuole is not sufficient to increase the virulence . They also indicate that pipB2 and sopD2 contribute independently to the virulence attenuation of a sifA− mutant . PipB2 operates as a linker for kinesin-1 on the SCV [14] and the sifA− vacuole is characterized by a high accumulation of this microtubule motor [13] . In order to define whether SopD2 plays a role in this process , we scored the percentage of SCVs positive for kinesin-1 in infected HeLa cells . In agreement with former studies [13] , [14] , most of the sifA− SCVs accumulated kinesin and this recruitment was completely abrogated upon deletion of pipB2 but not pipB ( Fig . 6A ) . Deletion of sopD2 in a sifA− mutant significantly decreased the percentage of kinesin-1-positive SCVs , although not to the same extent as the deletion of pipB2 . This deficit was complemented when a plasmid for the expression of SopD2 was introduced in the sifA−sopD2− mutant , thus confirming the specificity of this observation . We also observed and quantified that deletion of sopD2 in a sifA− mutant reduced the vacuolar membrane load in kinesin-1 ( Fig . 6B and C ) . These findings show that SopD2 increases both the percentage of vacuoles positive for kinesin-1 and the amount of kinesin-1 present on sifA− vacuoles . Since we observed removal of a significant amount of kinesin-1 upon deletion of sopD2 in a sifA− mutant , we asked whether it could result from the restoration of a vesicular transport from the SCV . To address this point , we infected HeLa cells with Salmonella strains expressing 2HA-tagged PipB2 from a plasmid . Consistent with the previous studies [20] , [22] , we observed the presence of PipB2/kinesin-1-positive vesicles at the periphery of cells infected with wild-type Salmonella , while these molecules accumulated on sifA− SCVs and also on sifA−sseJ− SCVs ( Fig . 7A ) . Strikingly , we observed vesicles positive for both kinesin-1 and PipB2 at the periphery of HeLa cells ( Fig . 7A ) and RAW 264 . 7 macrophages ( data not shown ) infected with a sifA−sopD2− mutant , thus giving images very similar to cells infected with wild-type Salmonella . These results clearly indicate that , in contrast to SseJ , SopD2 inhibits the formation and/or transport of PipB2/kinesin-1-positive vesicles from sifA− SCVs . To gain better insights into the genesis of PipB2/kinesin-1-positive vesicles , and to avoid the over-expression of PipB2-2HA , we engineered Salmonella strains expressing PipB2-2HA from the chromosome . We observed that in HeLa cells PipB2 localized on sifA− SCVs and , very surprisingly , on the sifA−sopD2− SCVs and tubular structures that extended from the vacuole ( Fig . 7B ) . These tubular structures are reminiscent of SIFs . Therefore we examined whether SIFs , which are LAMP1-positive tubular structures [18] and which have never been observed in the absence of SifA , were formed in concomitant absence of SifA and SopD2 . Most tubular structures were positive for both LAMP1 and PipB2 in cells infected with wild-type Salmonella , while tubules formed in cells infected with the sifA−sopD2− mutant were negative for LAMP1 ( Fig . 7B ) . In agreement with a previous report [30] , a discontinuous distribution of LAMP1 along effector-positive tubule and also LAMP1-negative tubules ( Fig 7B ) were observed in the sole absence of SopD2 . Strains expressing SseJ-2HA from the chromosome were used to score the occurrence of these membranous tubular structures . SIFs ( LAMP1-positive tubules ) were present in nearly 65 and 45% of cells infected with wild-type and sopD2− strains , respectively , while SseJ-positive tubules were seen in about 80% of cells infected with either strain ( Fig . 8A ) . In agreement with previous data [17] , [31] , neither SIFs nor effector-positive tubules were detected in cells infected with a sifA− mutant . SseJ-positive tubules were formed in 55% of cells infected with sifA−sopD2− mutant , while SIFs were barely detected . A sifA−sopD2− strain is therefore able to form T3SS-2 effector-positive and LAMP1-negative structures that are hereafter referred to as LNT for LAMP1-Negative Tubule . We scored LNTs in cells infected with strains expressing either 2HA-tagged PipB2 or SseJ from the chromosome and very similar results were obtained ( Fig . 8B ) . LNTs were more frequently found in cells infected with a sopD2− mutant than with wild-type Salmonella . Since PipB2 is involved in the centrifugal extension of SIFs [32] , we tested whether this effector was involved in the formation of LNTs . A sifA−sopD2−pipB2− strain was still able to form effector-positive tubules ( Fig . 6C and D ) . However these tubules were much shorter compared to those of the sifA−sopD2− mutant ( data not shown ) , indicating that PipB2 participates to their elongation . Conversely , the accumulation of peripheral vesicles in cells infected with Salmonella over-expressing PipB2 ( Fig . 7A ) suggests that these vesicles come from the PipB2-mediated fragmentation of LNTs . All in all , these results indicate that SopD2 inhibits the formation of LNTs and that SopD2 and SifA have antagonistic roles regarding their formation , while PipB2 is involved in their centrifugal extension . To gain more insights into the structure and the kinetics of LNT formation , we observed and scored both LNTs and SIFs at selected times post-infection . At 16 p . i . LNTs appeared as continuous tubular structures emerging from SCVs and extending towards the cell periphery . PipB2 localized all along LNTs but presented a patch-like distribution ( Fig . 9A ) . As we noticed that LNTs were frequently in contact with LAMP1-positive vesicles , both in wild-type and sifA−sopD2− infected cells ( arrows in Fig . 9B and C ) , these structures ( LNT in contact with LAMP1 vesicle ) were scored separately . In wild-type infected cells , the three types of structures appeared simultaneously at 3–4 hours p . i . . Then , the occurrence of SIFs increased and reached a plateau at 8 hours p . i . , while the percentage of cells with LNTs and LNTs in contact with LAMP1-vesicles remained lower than 20% of infected cells starting from 4 hours p . i . ( Fig . 9B and D ) . The sifA−sopD2− strain predominantly generated LNTs . The percentage of LNT contacting LAMP1-vesicles was reduced compared to cells infected with the wild-type strain , especially at early times of infection ( Fig . 9C and E ) . In addition to SIFs , Salmonella induces the formation of SISTs , which are Scamp3-positive tubules that lack late endosomal/lysosomal proteins [19] . Therefore , we asked whether LNTs correspond to SISTs . In wild-type infected cells , Scamp3 was found on tubules that were frequently positive for PipB2 . Conversely , Scamp3 was distributed in the perinuclear area both in non-infected ( data not shown ) and cells infected with the sifA−sopD2− mutant ( Fig . 10A ) . These results suggest that LNTs form independently from SISTs . Finally , we examined LNTs for the presence of other markers and analyzed their sensitivity to several treatments ( Table 1 ) . Unlike SIFs , LNTs were negative for Rab7 , the lysosomal glycoproteins LAMP1 , CD63 and the late endosomal lipid LBPA . Similar to SIFs , LNTs were positive for the vacuolar ATPase ( vATP ) and contained cholesterol . As listed in Table 1 , LNTs were negative for most endocytic and exocytic markers . The host protein SKIP was not involved in LNT formation . LNTs were found aligned with microtubules ( Fig . 10B ) and their extension and maintenance were dependent on an intact microtubule network . Treatment with nocodazole at 3h p . i . , when LNTs start to form , impaired their formation . Integrity of the LNT network was affected by disruption of microtubules at 14h p . i . . Treatment with the actin-disrupting agent cytochalasin D had any impact on LNT stability or formation . In contrast to SIST , brefeldin A treatment of infected cells did not significantly affect LNT formation ( Table 1 ) , suggesting that a functional exocytic pathway is not required . These data confirm that LNTs and SISTs are distinct structures . They also indicate that LNTs are likely to form along microtubules , but are devoid of most markers associated with SIFs . Together , these results demonstrate the presence of at least three types of tubules in Salmonella infected cells: SIFs [18] , SISTs [19] , and LNTs .
In this work , we demonstrate that the T3SS-2 effector SopD2 is present on the sifA− SCV and that it contributes to its membrane instability . sifA−sopD2− and sifA−pipB2−sopD2− mutant strains are enclosed and replicate in steady vacuoles . As previously shown [23] , [24] , a sifA−sseJ− mutant resides also in a stable vacuole , which , however , does not support bacterial replication . In addition , in contrast to sifA−sopD2− , a sifA−sseJ− strain is not more virulent than a sifA− mutant . Therefore , it appears that a stable vacuole is necessary but not sufficient for intracellular replication and virulence in the mouse model . In both epithelial cells and macrophages , a fraction of sifA−sopD2− and sifA−pipB2−sopD2− mutant bacteria resides in autophagosome-like vacuoles in which they remain metabolically active . This indicates that the harmonized activities of SifA , SopD2 and PipB2 are necessary for the formation and/or maintenance of a canonical SCV . It also shows that Salmonella is capable of surviving within compartments derived from the autophagic pathway . Yet , it remains unclear why a fraction of SCVs gets ubiquitinylated and recognized by the autophagy machinery . One possibility is that SopD2 is somehow involved in the suppression of autophagy , thereby protecting Salmonella from xenophagy . However , this hypothesis is inconsistent with the fact that this phenotype was not observed in the sole absence of SopD2 . SifA and SopD2 have antagonistic activities regarding the stability of the bacterial vacuole , the intra-macrophagic replication and the virulence . However , the deletion of sopD2 in a sifA− background is not sufficient to recover certain wild-type phenotypes . Noticeably , a sifA−sopD2− mutant is not able to trigger the formation of SIFs and SISTs , pointing out to a crucial role for SifA in the formation of these membranous tubular structures . Also , a sifA−sopD2− mutant still accumulates kinesin-1 , but to a lower extent than a sifA− mutant . This subtle but reproducible change results from the increased SCV membrane exchange activity , which in HeLa cells and macrophages infected with a sifA−sopD2− strain expressing PipB2-2HA from a plasmid , leads to the accumulation of effector- and kinesin-positive vesicles at the cell periphery . In cells infected with bacteria expressing physiological levels of this effector , this leads to the formation of tubules , the LNTs , which extend from the bacterial vacuole . Our data indicate that SopD2 inhibits the formation of LNT . Consistently cells infected with a sopD2− mutant exhibit more effector-positive tubules , which were either negative for LAMP-1 or presented a patchy distribution of this marker . These tubules have been previously described and named “pseudo-SIFs” [30] . This observation possibly reflects the activity of SopD2 , which , even in the presence of SifA , restricts the formation of effector-positive tubules . Because LNTs do not require SifA nor an intact secretory pathway for their formation , these tubules are undoubtedly distinct from SIFs and SISTs . It is unknown which intracellular compartment LNTs derive from . While SISTs appear to originate from the secretory pathway , SIFs are composed of both endosomal and TGN-derived material [18] , [19] , [33] . LNTs were found to be positive for the vATPase but were devoid of all other exocytic and endocytic markers tested so far . Yet , LNTs are , like SCVs , enriched in T3SS-2 effectors indicating that they might extend from the SCV membrane . Strikingly , effector-positive tubules projecting from the SCV that do not localize with either endocytic or Golgi markers were recently identified by live-cell imaging [34] . Also , how LNTs are generated remains unknown . Like SIFs and SISTs [18] , [19] , [32] , [35] , their formation is dependent on an intact microtubule network . We observed that LNTs were shorter in absence of PipB2 , indicating a role for kinesin-1 in the extension of LNTs along microtubules . Indeed , the accumulation of vesicles observed in cells infected with a sifA−sopD2− mutant that over-expresses PipB2 from a plasmid likely reflects a kinesin-mediated fragmentation of LNT into vesicles . Interestingly , the presence of LNT was concomitant with an increased stability of the SCV membrane . This suggests that LNTs might help recruit and transport membrane towards SCVs . Microscopic observations revealed that LAMP1-positive vesicles were occasionally enclosed in T3SS-2 effector-positive tubules . Indeed , the LNTs seem to wrap around LAMP1 vesicles . The close apposition between LNTs and vesicles might favour fusion and transfer of membrane from these vesicles to LNTs and subsequently to SCVs . This recruitment would ensure a supply in membrane and promote the stability of the vacuole . We propose that , in the absence of SifA and SopD2 , close apposition between LNTs and vesicles is not efficient enough to trigger a massive recruitment of lysosomal membrane markers to the SCV and tubules but that these contacts generate a retrograde flow of membrane sufficient to stabilize the bacterial vacuole . LNTs were not observed in cells infected with a sifA−sseJ− mutant and sseJ-2HA was even used as marker for LNT . Thus , the instability of the sifA− vacuole results from both the enzymatic activity of SseJ [23] , [24] and the inhibition of membrane exchange activity of SopD2 . Altogether this work has identified SopD2 as a major regulator of SCV stability and as an inhibitor of LNTs formation . LNTs are a novel kind of tubules that may represent precursors of SIFs and SISTs . The formation of LNTs likely involves additional T3SS-2 effectors and our work opens the way for their identification and characterization . Another interesting challenge will be to understand the cell biology of LNT initiation and the molecular mechanism by which SopD2 blocks LNT formation .
Animal experimentation was conducted in strict accordance with good animal practice as defined by the French animal welfare bodies ( Law 87–848 dated 19 October 1987 modified by Decree 2001-464 and Decree 2001-131 relative to European Convention , EEC Directive 86/609 ) . All animal work was approved by the Direction Départmentale des Services Vétérinaires des Bouches du Rhônes ( authorization number 13 . 118 to S . M . ) . The S . enterica serovar Typhimurium strains and plasmids used in this study and their relevant characteristics are listed in Tables 2 and 3 . Strains were cultured in LB broth ( Difco ) at 37°C . Ampicillin ( 50 µg/ml ) , kanamycin ( 50 µg/ml ) , and chloramphenicol ( 50 µg/ml ) were added when required . Mutagenesis was carried out by using the gene disruption method described by Datsenko and Wanner , except that 10 mM arabinose was used to induce expression of the red recombinase [36] . The strains expressing PipB2-2HA , SopD2-2HA or SseJ-2HA from the chromosome were obtained using the method described by Uzzau and colleagues [37] . The oligonucleotide primers used to amplify pKD4 kanamycin or pKD3 chloramphenicol resistance genes are listed in Table 4 . All mutagenesis was performed in the 12023 wild-type strain . Strains carrying two or three mutations were created by transduction using the phage P22 HT105 int . Phage-free transductants were selected for analysis . Gene deletions and transductions were checked by PCR . RAW264 . 7 and HeLa cell lines were grown in DMEM ( GibcoBRL ) supplemented with 10% foetal calf serum ( FCS; GibcoBRL ) , 2 mM nonessential amino acids , and glutamine ( GibcoBRL ) at 37°C in 5% CO2 . Bone marrow cells were isolated from femurs of 6- to 10-week-old C57BL6 female mice and differentiated into macrophages as previously described [38] . HeLa cells were seeded in 6-well plates with or without 12-mm diameter glass coverslips at a surface ratio of 1/10 , 24 h before infection . Bacteria were incubated overnight at 37°C with shaking , diluted 1∶33 in fresh LB broth , and incubated in the same conditions for 3 . 5 h . The cultures were diluted in Earle's buffered salt solution ( pH 7 . 4 ) and added to the cells at a multiplicity of infection of 100∶1 . The infection was allowed to proceed for 10 min at 37°C in 5% CO2 . Macrophages were seeded at a density of 106 cells per well in 6-well tissue culture plates 24 h before use . Bacteria were cultured overnight at 37°C with shaking and were opsonised in DMEM containing FCS and 10% normal mouse serum for 30 min on ice . Bacteria were added to the cells at a multiplicity of infection of 100∶1 . Plates were centrifuged at 500 g for 5 min at 4°C and incubated for 30 min at 37°C in 5% CO2 . Cells were washed three times with growth medium containing 100 µg/ml gentamicin and then incubated in this medium for 1 h , after which the gentamicin concentration was decreased to 10 µg/ml for the remainder of the experiment . For enumeration of intracellular bacteria , macrophages were washed three times with PBS and lysed with 0 . 1% Triton X-100 for 10 min , and a dilution series was plated onto LB agar plates . Plates were incubated overnight at 37°C , and colonies were counted . Each time point was performed in triplicate , and each experiment was performed three times or more . 10–20% confluent HeLa cells were transfected with RNA duplexes ( Quiagen ) previously described [13] using siPORT Amine transfection agent ( Applied Biosystems Ambion ) according to the manufacturer's instructions . Cells were further incubated for 72 hours . Brefeldin A ( BFA , Sigma ) , nocodazole ( Sigma ) , cytochalasin D ( Biomol ) stock solutions were prepared in dimethyl sulphoxide ( DMSO ) and kept at −20°C . Working concentrations were 5 µg/ml for BFA and 2µg/ml for both nocodazole and cytochalasin D . Drugs were added on the cells either at 3 h30 or 14 h p . i . for a further incubation of 4 h or 2 h respectively . Stock solution of arabinose ( Sigma ) was prepared ( 20% ) in water and sterile filtered . Bacterial DsRed expression was induced by addition of arabinose to the cells ( 0 . 4% ) at 12 h or 14 h post infection and cells incubated of further 4 h . Filipin ( Sigma ) stock solution was prepared in DMSO ( 25 mg/ml ) and stored at −20°C . To label cellular cholesterol , fixed cells were stained for 2 h with 50 µg/ml filipin in PBS containing 10% normal foetal bovine serum ( FBS , GibcoBRL ) . Cells grown on coverslips were fixed with 3% paraformaldehyde ( pH 7 . 4 ) in PBS at room temperature for 10 min . For immunostaining of tubulin , cell were further fixed in 80% methanol/1% paraformaldehyde for 5 min at −20°C . Fixed cells were washed three times in PBS and permeabilized with 0 . 1% saponin in PBS . Primary and secondary antibodies were diluted in PBS containing 0 . 1% saponin and 5% horse serum . Coverslips were incubated with primary antibodies for 60 min at room temperature , washed once in PBS containing 0 . 1% saponin and then incubated with appropriate secondary antibodies . Coverslips were mounted onto glass slides using ProLong Gold ( Invitrogen ) . Cells were observed with an epifluorescence microscope ( Leica ) or a LSM510 confocal laser scanning microscope ( Zeiss ) . The mouse monoclonal antibodies anti-LAMP1 H4A3 and anti-CD63 H5C6 , developed by J . T . August and J . E . K . Hildreth , obtained from the Developmental Studies Hybridoma Bank ( DSHB ) under the auspices of the NICHD and maintained by the University of Iowa ( Department of Biological Sciences , Iowa , IA ) , were used at a dilution of 1∶1000 . The rabbit anti-LAMP1 was obtained from Dr . Minoru Fukuda ( La Jolla Cancer Research Foundation ) was used at a dilution of 1∶1000 . The mouse monoclonal antibodies anti-LC3 ( clone 5F10; Nanotools ) and FK2 ( which detects both mono- and polyubiquitinylated proteins ) ( Biomol ) were used at a dilution of 1∶50 and 1∶104 , respectively . The rabbit anti-Sequestosome-1 ( SQSTM1 , also known as P62 ) ( H-290; Santa Cruz Biotechnology ) was used at a dilution of 1∶100 . The rabbit anti-kinesin HC ( KIF5B ) antibody PCP42 , generously provided by R . Vale ( University of California , San Francisco ) , was absorbed on Salmonella acetone powder to remove contaminating anti-enterobacteria antibodies and used at a dilution of 1∶100 . The mouse anti-HA ( clone 16B12; Covance , Richmond , CA ) and the rat anti-HA ( clone 3F10; Roche ) antibodies were used at 1∶1000 and 1∶200 , respectively . The rabbit anti-Scamp3 antibody , generously provided by J . David Castle ( University of Virginia , Charlottesville ) was used at a dilution of 1∶250 . The sheep anti human TGN46 antibody ( Serotec ) was used at a dilution of 1∶500 . The mouse anti-tubulin ( Sigma ) was used at a dilution of 1∶2000 . The mouse anti vATPase monoclonal antibody ( OSW2 ) was obtained from Dr Satoshi B . Sato ( Kyoto University ) and was used at 1∶1000 . The rabbit anti-Rab7 [39] and the mouse anti-Rab 5 ( D-11 , Santa Cruz Biotechnology ) were used at a dilution of 1∶200 and 1∶100 , respectively . The rabbit anti-calnexin ( Stressgen ) and the rabbit anti-cathepsin D ( Dako ) were used at a dilution of 1∶500 and 1∶50 , respectively . The rabbit anti-mannose 6-phosphate receptor was a kind gift by Prof . Bernard Hoflack ( University of Dresden ) and was used at a dilution of 1∶250 . Mouse anti-LBPA , generously provided by Prof . Jean Gruenberg ( University of Geneva ) , was used at a dilution of 1∶50 . The mouse ( 1E6 , Biodesign ) and rabbit anti-Salmonella LPS ( Difco Laboratories ) antibodies were used at a concentration of 1∶1000 and 1∶2000 , respectively . Mouse monoclonal antibody against human MHC class II molecules ( clone L243 ) was used at 1/10 . Secondary antibodies ( donkey anti-rabbit , anti-rat , anti-mouse or anti-sheep IgG conjugated to FITC , Texas red , or cyanine 5 ) were purchased from Jackson ImmunoResearch and used at a dilution of 1∶100 . The goat anti-rabbit or anti-mouse IgG conjugated to Alexa Fluor 350 ( Molecular Probes ) were used at a dilution of 1∶1000 . Polymerized actin was stained with Alexa 568-conjugated phalloidin ( Invitrogen ) . SCVs were labelled by using antibodies against the lysosomal glycoprotein LAMP1 . Infected cells were observed by epifluorescence , and the percentage of GFP-expressing bacteria present in a vacuole was determined by counting the total number of bacteria and the number of bacteria encircled by the LAMP1 marker . The percentage of SCVs positive for LC3 , P62 , FK2 or kinesin-1 was determined by visualizing GFP-expressing bacteria in the green channel , LAMP1 in the UV channel ( using Alexa Fluor 350 secondary antibodies ) , and LC3 , P62 , FK2 or kinesin HC in the red channel . The percentage of bacteria expressing DsRed upon induction with arabinose was enumerated by visualizing DsRed-expression in the red channel , Salmonella LPS in the UV channel , and LC3 , P62 or FK2 in the green channel . The percentage of infected cells containing LAMP1-tubules , PipB2-2HA- or SseJ-2HA-positive tubules was determined by counting the total number of infected cells and the number of infected cells containing the respective tubule stained . The scorings for each experiment were at least performed three times . All statistical analyses were performed by using InStat ( GraphPad ) . The two-tailed P value was calculated . Detector settings were optimized to match PMT gain and offset to the most and the less fluorescent area of coverslips , respectively . For each experiment these settings were not changed . Two colour images of GFP-Salmonella ( green ) -infected HeLa cells immunolabeled for kinesin heavy chain ( red ) were recorded . Individual Salmonella-containing vacuoles ( SCVs ) were analyzed using Zeiss LSM510 software as follows . The kinesin-1 enrichment was defined as the mean fluorescence of the SCV minus the mean fluorescence of an identical adjacent cellular area . The relative amount of kinesin heavy chain enrichment of each SCV was plotted for the sifA− and sifA−sopD2− SCVs . Cells were fixed for 1 h at room temperature with 2 . 5% glutaraldehyde ( Sigma ) in 0 . 1 M cacodylate buffer , pH 7 . 2 , containing 0 . 1 M sucrose , 5 mM CaCl2 and 5 mM MgCl2 . Cells were then washed twice with the same buffer and postfixed for 1 h at room temperature with 1% osmium tetroxide ( Electron Microscopy Sciences ) in the same buffer devoid of sucrose . Cells were then scraped off the dishes and concentrated in 2% agar in the same buffer and treated for 1 h at room temperature with 1% uranyl acetate in Veronal buffer . Samples were dehydrated in a graded series of acetone and embedded in Epon resin . Thin sections were stained with 2% uranyl acetate in distilled water and then with lead citrate . 50 to 100 different vacuoles per sample , on two different EM grids , were scored as being within normal SCVs , phagolysosomes , autophagic vacuoles or within the cytosol . Care was taken to avoid serial sections . Six- to eight-week-old C57BL/6 mice were inoculated intraperitoneally with equal amounts of two bacterial strains for a total of 105 bacteria per mouse . The spleens were harvested 48 h after inoculation and homogenized . Bacteria were recovered and enumerated after plating a dilution series onto LB agar and LB agar with the appropriate antibiotics . CIs were determined for each mouse [29] and a minimum of three mice were infected . The CI is defined as the ratio between the mutant and wild type strains within the output ( bacteria recovered from the mouse after infection ) divided by their ratios within the input ( initial inoculum ) . Unpaired t-test analysis was performed to compare two CIs , and a one-sample t-test comparing the log of the CI to 0 was used to determine whether the CI was significantly different from 1 . All statistical analyses were performed using Prism ( GraphPad ) . | Salmonella typhimurium is a bacterial pathogen that causes diseases ranging from gastroenteritis to typhoid fever . This bacterium survives inside eukaryotic cells within a membrane-bound compartment , namely the Salmonella-containing vacuole . Salmonella injects proteins , named effectors , into the infected cell . These effectors change the biology of the infected cell and collectively support Salmonella replication and virulence . The effector SifA plays a key role in the bacterial vacuole stability and in the formation of membrane tubules that extend from the vacuole . Absence of SifA leads to the disruption of the vacuolar membrane and , therefore to the release of bacteria in the cytosolic compartment . Consequently , this mutant presents significant replication and virulence defects . Here , we show that an additional Salmonella effector , SopD2 , is responsible for the membrane instability of the sifA− vacuole . In addition , we demonstrate that SopD2 acts as an inhibitor of vesicle transport from the vacuole and that it down-modulates the formation of tubular structures . These findings describe a role for SopD2 as an antagonist of SifA in terms of vacuolar membrane dynamics . | [
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] | 2010 | The Virulence Protein SopD2 Regulates Membrane Dynamics of Salmonella-Containing Vacuoles |
Cockayne syndrome ( CS ) is a devastating progeria most often caused by mutations in the CSB gene encoding a SWI/SNF family chromatin remodeling protein . Although all CSB mutations that cause CS are recessive , the complete absence of CSB protein does not cause CS . In addition , most CSB mutations are located beyond exon 5 and are thought to generate only C-terminally truncated protein fragments . We now show that a domesticated PiggyBac-like transposon PGBD3 , residing within intron 5 of the CSB gene , functions as an alternative 3′ terminal exon . The alternatively spliced mRNA encodes a novel chimeric protein in which CSB exons 1–5 are joined in frame to the PiggyBac transposase . The resulting CSB-transposase fusion protein is as abundant as CSB protein itself in a variety of human cell lines , and continues to be expressed by primary CS cells in which functional CSB is lost due to mutations beyond exon 5 . The CSB-transposase fusion protein has been highly conserved for at least 43 Myr since the divergence of humans and marmoset , and appears to be subject to selective pressure . The human genome contains over 600 nonautonomous PGBD3-related MER85 elements that were dispersed when the PGBD3 transposase was last active at least 37 Mya . Many of these MER85 elements are associated with genes which are involved in neuronal development , and are known to be regulated by CSB . We speculate that the CSB-transposase fusion protein has been conserved for host antitransposon defense , or to modulate gene regulation by MER85 elements , but may cause CS in the absence of functional CSB protein .
The human genome is replete with interlopers — transposable DNA elements , retrotransposable RNA elements such as SINEs and LINEs , and a dizzying variety of lesser-known elements — which together account for as much as half of our DNA [1] . Although much of this “junk” DNA is selfish and surprisingly harmless , the constant turnover of these elements is an important source of insertional mutagenesis with benign [2] and malign [3] consequences . Indeed , eukaryotes often recruit mobile elements to perform critical functions — a process known as domestication or exaptation [4] . For example , the RAG1 recombinase , which diversifies the adaptive immune response in mammals , was domesticated aeons ago from a Transib-family transposase [5] . A similarly domesticated DNA transposon is responsible for the programmed genomic rearrangements found in many ciliates [6] , and a pogo-like transposase gave rise to the centromeric CEN-P protein family [7] which mediates host genome surveillance for retrotransposons in Schizosaccharomyces pombe [8] . More recently in the primate lineage , a mariner-like transposase was fused to a SET histone methyltransferase domain by de novo exonization; the fusion protein retains the ancestral DNA binding activity of the transposase , and may function as a transcriptional regulator at dispersed mariner-like repeat elements [9] . Here we report identification of an evolutionarily conserved PiggyBac transposase fusion protein that may play a critical , and previously unsuspected , role in a well-studied human disease , Cockayne syndrome ( CS ) . PiggyBac elements , first characterized in the cabbage looper moth Trichoplusia ni [10] , [11] , have now been identified in a variety of eukaryotes from protozoa [12] to primates [1] . A typical PiggyBac element contains a 1 . 8 kb ORF encoding a 68 kDa transposase; the boundaries of the element are defined by 13–15 nt terminal inverted repeats , which are in turn flanked by a duplication of the target site TTAA [13] . The T . ni PiggyBac transposon is a useful tool for germline manipulation because it is active in a wide range of species including mammals [14] and has been considered as a possible gene therapy vector [15] . The five PiggyBac elements in the human genome ( PGBD1-5 ) are variously conserved among vertebrates; PGBD5 dates to before the teleost/tetrapod split , whereas PGBD3 and PGBD4 are restricted to primates [1] , [13] . CS is a devastating inherited progeria characterized by severe post-natal growth failure and progressive neurological dysfunction [16] . Most cases of CS reflect mutations in the Cockayne syndrome Group B ( CSB , also known as ERCC6 ) gene , a SWI/SNF-like DNA-dependent ATPase [17]–[19] that can wind DNA [20] and remodel chromatin in vitro [21]; the remaining cases of CS are caused by mutations in the CSA gene , and by rare alleles of the xeroderma pigmentosum genes XPB , XPD , and XPG [22] . All of these factors were originally identified as being involved in the transcription-coupled repair of UV-induced DNA damage [23] , [24] . While searching for an activity that could better explain the CS phenotype , we found that CSB has a general chromatin remodeling function [25] which could account for the pleiotropic effects of CSB mutations and the characteristic wasting of CS [26] . Alternatively , CS may be caused by defects in transcription initiation [27] , [28] , or by a partial failure to repair oxidative DNA damage . CSB is known to enhance repair of 8-hydroxyguanine lesions [29] , and mice doubly mutant for CSB and the 8-hydroxyguanine glycosylase OGG1 are severely deficient in global repair of endogenous oxidative DNA damage [30] . Similarly , complete inactivation of nucleotide excision repair ( NER ) in mice doubly mutant for CSB and XPA mimics CS and suppresses the somatotroph axis [31] , [32] . As yet unexplained , however , is why complete absence of CSB does not cause CS , although all CS mutations are recessive [33]–[35] . Here we show that the PiggyBac transposable element PGBD3 embedded within intron 5 of the CSB gene functions as an alternative 3′ terminal exon ( “exon trap” ) ; as a result , alternative splicing of the CSB primary transcript generates two mRNAs , one encoding all 21 exons of the CSB protein , and the other an equally abundant CSB-related protein in which the first 5 exons of CSB are fused to the PGBD3 transposase . Sequence comparisons of PGBD3 with PiggyBac pseudogenes in humans and other primates suggest that PGBD3 was domesticated soon after it transposed into the CSB gene . Indeed , conservation of the alternatively spliced PGBD3 element in the CSB genes of chimpanzee , orangutan , Rhesus macaque and marmoset over at least 43 Myr of evolution [36] , together with a preponderance of synonymous mutations , strongly suggest that the fusion protein has been selected for an advantageous function in its primate host . We speculate that the CSB-transposase fusion protein originally played a role in host genome defense by repressing transposition of autonomous PGBD3 elements and the hundreds of nonautonomous PGBD3-dependent MER85 elements derived from them . We also find an association of MER85 elements with a subset of CSB-regulated genes and genes involved in neuronal development , suggesting that the fusion protein may later have acquired the ability to modulate gene regulatory networks . Finally , we show that the CSB-transposase fusion protein continues to be expressed in CS primary cells lacking functional CSB protein , implying that the fusion protein could contribute to the CS phenotype , or even transform the mild UV sensitivity caused by complete loss of CSB-related proteins [33] into a true progeria .
Intron 5 of the human CSB gene is host to a PiggyBac transposable element known as PGBD3 ( Figure 1A ) . We initially noted that the RefSeq transcript for PGBD3 ( along with four of seven deposited mRNAs ) consists of the 3′ region of CSB exon 5 spliced to the entire PiggyBac coding region . The PGBD3 transposase ORF is flanked by a 3′ splice acceptor site just 7 nt upstream of the first methionine , and a polyadenylation site about 130 nt downstream of the termination codon . Moreover , the CSB and PGBD3 coding regions are in frame across this splice junction , suggesting that transcripts initiating at a normal CSB promoter could be alternatively spliced to the PiggyBac element instead of exon 6 , thus generating a CSB-PGBD3 fusion protein ( 1B ) . In this fusion protein , the N-terminal 465 residues of CSB ( including the acidic domain but not the ATPase ) would be tethered to the entire PiggyBac transposase . In fact , two of the seven PGBD3 GenBank mRNA sequences ( BC034479 and AK291018 ) appear to be just such variants , starting at either the noncoding CSB exon 1 ( AK291018 ) or an alternative noncoding exon 1 ( BC034479 ) and ending just beyond the PGBD3 polyadenylation site . Four other PGBD3 GenBank mRNA sequences consist of the 3′ region of CSB exon 5 spliced to the entire PiggyBac coding region , suggesting the existence of an unusual cryptic promoter within exon 5 ( the sixth mRNA , likely incomplete , begins within the transposase ORF ) . We sought to confirm the existence of such alternatively spliced transcripts , and to determine whether the transcripts initiate at the putative cryptic promoter within exon 5 or at a normal CSB transcription start site . We were able to detect the predicted CSB-PGBD3 fusion transcripts by quantitative , real-time RT-PCR ( Q-RT-PCR ) using HeLa mRNA as template , forward primers for the 3′ half of CSB exon 5 which is shared by the CSB and predicted fusion mRNAs , and reverse primers which are specific for either CSB exon 6 or the PGBD3 element ( 2A ) . The fusion products exhibited the expected size ( 2B ) and sequence ( data not shown ) , and were approximately 2-fold more abundant than the equivalent CSB products ( Figure S1 ) . Moreover , we readily detected fusion products using forward primers for CSB exons 2 , 3 and 4 , indicating that a significant fraction of the CSB-PGBD3 fusion transcripts initiate far upstream of the putative cryptic promoter , presumably at a natural CSB initiation site . These full-length CSB-PGBD3 fusion transcripts do not reflect template strand switching by reverse transcriptase or recombination during PCR [37] within exon 5 , because alternatively spliced fusion transcripts lacking exon 5 were also observed ( Figure 2 ) , and the abundance of the fusion products was not diminished in control experiments where either one of the potentially recombining mRNAs was sequestered within a cDNA:mRNA hybrid by a preliminary reverse transcription step using an mRNA-specific primer ( data not shown ) . Using a subset of these primer combinations , we also detected CSB-PGBD3 fusion transcripts in three other cell lines: hTERT-immortalized WI38 normal lung fibroblasts , and hTERT-immortalized CS1AN CSB fibroblasts rescued with CSB-wt cDNA ( CSB-wt line ) or mock-rescued with enhanced green fluorescent protein ( CSB-null line ) [25] . In all four lines , the fusion transcripts were more abundant than the CSB transcripts — as much as 13- to 26-fold more abundant in the immortalized WI38 line ( Figure S2 ) . The CSB-PGBD3 fusion transcript , apparently initiating at or near the normal CSB start site , appears to be the only major alternatively spliced transcript expressed from the CSB/PGBD3 gene . First , the transposase coding region is not an alternative exon within full-length CSB mRNA , because combinations of two upstream primers from the PiggyBac element and four downstream primers located in CSB exons 6 , 7 , 8 and 9 failed to produce RT-PCR products in any of the four cell lines tested ( data not shown ) . Second , the CSB and CSB-PGBD3 transcripts lacking exon 5 appear to be scarce ( Figure 2 , compare smaller and larger bands in lanes 4–6 for CSB primer A and fusion primer D ) . And third , as judged by Q-RT-PCR , the 3′ region of the CSB mRNA appears to be less abundant than the 5′ region ( data not shown ) , arguing that the putative cryptic promoter within CSB exon 5 does not generate significant quantities of an N-terminally truncated CSB mRNA ( ΔCSB , see Figure 1B ) . Consistent with the Q-RT-PCR data , we detected the CSB-PGBD3 fusion protein in four different cell lines ( HT1080 , WI38/hTERT , CSB-null and CSB-wt ) by Western blotting with antibodies specific for the N- and C-termini of CSB protein . The C-terminal antibody revealed one major band of the size expected for intact CSB protein ( Figure 3A ) , whereas the N-terminal antibody revealed two major bands — intact CSB and a smaller band of approximately the size expected for the fusion protein ( Figure 3B ) . Notably , the fusion band was present in an immortalized CSB-null line derived from the severely affected individual CS1AN — a compound heterozygote consisting of one CSB allele with an early truncating mutation ( K337STOP ) and a second allele with a 100 nt deletion in exon 13 [38] . The latter allele should , and does , permit normal expression of the fusion protein in this CS cell line ( Figure 3B ) . The fusion band was also seen in the Saos-2 osteosarcoma and MRC5 fibroblast cell lines ( Figure S3 ) . To confirm the identity of the CSB-PGBD3 fusion band as visualized with the N-terminal CSB antibody ( Figure 3 ) , we used a commercial PGBD3-specific antibody . The PGBD3 antibody revealed three major bands on Western blotting , including one that comigrates with the fusion band ( Figure 4 ) . The CSB-PGBD3 fusion protein ( with calculated mass 120 kDa and pI 6 . 15 ) migrates more slowly than expected , but this is commonly observed for acidic proteins [39] , and the endogenous CSB-PGBD3 fusion protein comigrates with recombinant tagged CSB-PGBD3 fusion protein after correction for tag size ( data not shown ) . In contrast , CSB has a calculated pI of 8 . 2 and migrates as expected for a mass of 168 kDa . We conclude that the endogenous protein reacting with both N-terminal CSB antibody ( Figure 3 ) and the PGBD3-specific antibody ( Figure 4 ) is the abundant CSB-PGBD3 fusion protein . A tabulation of all reported CS cases with known mutations in CSB reveals that 21 of 24 retain at least one allele that should allow continued expression of the CSB-transposase fusion protein ( Table S1 ) . To confirm that CS cells express the fusion protein in the absence of intact CSB , as seen for the hTERT-immortalized CS1AN line ( Figure 3B ) , we screened three different primary CSB cells ( GM10903 , GM10905 , and GM00739B derived from patient CS1AN ) none of which , as expected , exhibited intact CSB protein . All , however , express the fusion protein ( Figure 5 ) . Nor is expression an artifact of immortalization , as the abundance of the fusion protein was similar in primary GM00739B cells ( Figure 5 ) and derived cell lines immortalized either with hTERT ( Figure 2 , GM00739B ) or SV40 ( Figure S3 , CS1AN/SV ) . We were able to identify clear chimpanzee ( Pan troglodytes ) and Rhesus macaque ( Macaca mulatta ) homologs of PGBD3 and all four of the pseudogenes by BLASTing the PGBD3 coding region against the recently-completed chimpanzee [40] and Rhesus [41] genomes . We also identified homologs of CSB and PGBD3 in early assemblies of the orangutan ( Pongo abelli ) and white tufted-ear marmoset ( Callithrix jacchus ) genomes ( Figure 6 ) . All of these sequences predict that PGBD3 will function as an alternative 3′ terminal exon to generate a CSB-PGBD3 fusion protein . Chimpanzee genomic sequences are approximately 98 . 8% identical to their human counterparts overall [40] , and this was true for the four PGBD3 pseudogenes and the 2 kb intronic regions immediately flanking the PGBD3 coding region in CSB intron 5 ( Table 1 ) . As expected , the CSB protein coding region was more highly conserved between chimpanzee and human ( 99 . 5% DNA identity ) than adjacent noncoding sequences . The PGBD3 coding region was also much more highly conserved than noncoding sequence ( 99 . 7% DNA identity ) . For both genes , the degree of conservation lies outside the 95% confidence interval generated from the six noncoding regions we analyzed ( 98 . 4–99 . 3% , see Table 1 ) . This was true for all four primate species examined — for example , PGDB3 in marmoset , which last shared a common ancestor with humans approximately 43 Mya [36] , is 96 . 1% identical in nucleotide sequence and 96 . 5% identical in amino acid sequence to its human homolog , compared to 95 . 2% and 94 . 1% , respectively , for CSB and 85 . 0–88 . 5% for noncoding nucleotide sequence ( Table 1 ) . A complementary method to estimate the degree to which a protein coding sequence is under purifying selection is to calculate the ratio of nonsynonymous ( Ka , residue-altering ) to synonymous ( Ks , silent ) nucleotide substitution rates; a low ratio suggests that the amino acid sequence is under strong purifying selection . We analyzed CSB and PGBD3 coding sequences from human , chimp , orangutan , Rhesus and marmoset with the SNAP program [42] , which implements the Ka/Ks algorithm of Nei et al . [43] . For comparison , the decayed PGBD3 pseudogenes PGBD3P1 and PGBD3P3 have mean Ka/Ks values of 0 . 73 and 0 . 96 , respectively , for pairwise comparisons between the various primate species ( Table 2 ) . CSB , presumably under purifying selection , has a mean Ka/Ks value of 0 . 21 ( P<0 . 0001 vs . both P1 and P3 ) . The mean Ka/Ks for PGBD3 is 0 . 12 ( P<0 . 0001 vs . both P1 and P3 ) , consistent with the transposase being subject to purifying selection at least as strong as that for CSB . In fact , the mean Ka/Ks of PGBD3 is significantly lower than that of CSB ( P = 0 . 0006 ) , though the difference between the entire fusion protein and CSB is not significant ( P = 0 . 12 ) . We did not find a CSB-PGBD3 homolog in the draft genome assemblies of two more distantly-related primates of the Strepsirrhini family: galago ( Otolemur garnetti ) and mouse lemur ( Microcebus murinus ) , though the former may offer insights into the emergence of PGBD3 . The mouse lemur genome contained no recognizable PGBD3 or MER85 elements . However , we found dozens of examples of each in galago although the two species diverged from a common ancestor only after the Strepsirrhini lineage separated from that of humans and marmosets ( Figure 6 ) [44] . Despite this abundance , we confirmed by sequence alignment that the TTAA target site in galago CSB intron 5 is intact and empty . Moreover , of the eight galago PGBD3-like sequences we examined in detail , all are in an advanced state of decay , and all but one are more closely related to human PGBD3 than to each other ( Table S2 ) . Interestingly , a consensus sequence of galago PGBD3's is as similar to human PGBD3 ( 87 . 8% identity ) as galago CSB exon sequences are to their human counterparts ( 87 . 6% identity ) , and both are significantly more identical than the individual PGBD3-like elements are to human PGBD3 ( see Table S2 for confidence intervals ) - suggesting that the ancestor of these galago PGBD3-like sequences was closely related to conserved human PGBD3 . The galago PGBD3's are equally similar to human PGBD3 and this consensus ( P = 0 . 61 by paired Student's T-test , see Table S2 ) , consistent with divergence from a closely related ancestor . Together , these data suggest that an element closely related to the ancestral human PGBD3 independently invaded the galago and human-marmoset lineages . Though invasion of the common galago-human ancestor by ancestral PGBD3 would also explain the PGBD3-like sequences in galago , the monophyly of Strepsirrhini is well accepted [44] and it is unlikely that all traces of PGBD3 and MER85 would have been eradicated from the mouse lemur genome given their abundance in all genomes in which they are found . We conclude that an ancestral PGBD3 element invaded CSB intron 5 at least 43 Mya , before human and marmoset diverged [36]; PGBD3 was then conserved in the human-marmoset lineage because the CSB-PGBD3 fusion protein performs a selectable function ( see Discussion ) whereas the elements ultimately degenerated in galago where the random transpositions were either neutral or harmful . The PiggyBac element has the hallmarks of a transposable element that has survived through evolution by functioning as a natural “exon trap” . In both cabbage looper moth and primates , the transposase ORF is flanked immediately upstream by a potential 3′ splice site ( TTTTCTTGTTATAG in moth PiggyBac , CCTTTTTTCCGTTTTAG in PGBD3 ) and immediately downstream by a potential polyadenylation signal ( AATAAATAAATAAA in moth PiggyBac , AATAAA in PGBD3 ) . This 3′ splice site is perfectly conserved between human , chimpanzee , Rhesus , orangutan and marmoset ( Figure S4 ) , and in all five species PGBD3 possesses a potential polyadenylation signal ( Figure S5 ) despite evidence for strong selection against transcription of intragenic transposable elements [45] . Insertion of an element with these features into a host intron can generate an N-terminal fusion protein as observed for the PGBD3 insertion into CSB intron 5 ( Figure 1 ) . Similarly , PGBD1 and PGBD2 , which are present in mouse and rat ( though reduced to pseudogenes in mouse ) , also appear to have persisted as exon traps: The RefSeq human mRNAs include multiple upstream exons derived from the host gene , with the intact transposase encoded within a single large 3′ terminal exon . Indeed , the ability of the T . ni PiggyBac transposase to tolerate N-terminal fusions unlike the Sleeping Beauty , Tol2 , and Mos1 transposases [15] is consistent with the genomic evidence that PiggyBac evolved as a 3′ terminal exon trap . Evolution as a 3′ exon trap may also explain the impressive host range of T . ni PiggyBac [46] because transcription of the element is driven by an efficient host promoter , instead of relying on fortuitous promoters or a universal species-independent promoter internal to the element itself . In contrast to PGBD3 , the four PGBD3-related pseudogenes are all in an advanced state of decay ( 88–90% identity to PGBD3; see Figure S6 ) . None of the pseudogenes contains an ORF longer than 62 codons and three exhibit major deletions or rearrangements . All are more closely related to PGBD3 than to any of the other pseudogenes ( Table S3 ) , suggesting that all diverged from PGBD3 itself or from a closely related common ancestor before the divergence of the human and Rhesus lineages . The left and right ends of PGBD3 correspond to the left ( 100 nt ) and right ( 40 nt ) halves of the 140 nt MER85 repeat element [47] , an arrangement also found in the four human PGBD3 pseudogenes . We found 613 examples of MER85 elements in the human genome; in almost all cases , these were either intact left ends ( 403 ) , intact right ends ( 119 ) or complete 140 nt elements ( 73 ) . MER85 has been described as a nonautonomous transposable element derived from PiggyBac and presumably mobilized in trans by the PiggyBac transposase [1]; many other transposons have given rise to similar nonautonomous elements known collectively as “miniature inverted repeat transposable elements” or MITEs [12] . The similarly abundant MER75 and MER75B elements appear to be derived from PGBD4 , although the PGBD4 transposase exon is no longer neatly flanked by its derivative elements as PGBD3 is by MER85 . Consistent with previous estimates [48] , neither MER75B nor MER85 has been significantly mobile since the divergence of human , chimpanzee and Rhesus . We found that 36 of 42 MER85 elements on human chromosome 1 had clear homologs on chromosome 1 of at least one of the other primates , as did 20 of 21 human MER75B elements . The few remaining unmatched human elements likely reflect incomplete sequences or recombination . Most PiggyBac transposases have three conserved aspartic acid residues [13] which may be related to the metal-coordinating DDE motif found in the catalytic domain of many transposase and integrase families [49] . The most likely candidates for these conserved residues in PGBD3 [13] are identical in all five primates ( human , chimp , orangutan , Rhesus and marmoset ) : D270 , N352 and D467 ( Figure S7 ) . Strikingly , all four pseudogenes in human , chimp and Rhesus encode D at the second position ( the draft orangutan and marmoset genomes do not yet include all PGBD3 pseudogenes ) . Half of the galago PGBD3-like sequences we examined also encode D at this position , while the remainder harbor one of several changes ( Figure S8 ) . Together , this suggests that the feral ancestor of human PGBD3 encoded a DDD motif , and that its domestication involved mutations that compromised mobility . The exapted mariner transposase in the SETMAR fusion protein retains ancestral DNA binding activity despite attenuation or loss of transposase function [9] . We therefore asked whether genes located closest to MER85 elements might exhibit common themes or functions possibly reflecting a cis-regulatory function of the MER85 elements themselves or proteins that bind to them [50] . Using the ENSEMBL gene database , we located the transcription start site closest to each identified MER85 element ( Table S4 ) . The median distance between MER85 elements and transcription starts was 93 kb , similar to what is seen for other human repeats present in 500 to 4 , 000 copies [51] . Of the 613 MER85 elements , we selected the 585 that were less than 1 Mb from a transcription start site , well within the documented range of proximal enhancer elements [52] . We then used the L2L Microarray Analysis Tool [53] to search for expression patterns among these MER85-associated genes ( Table S5 ) . The strongest pattern to emerge was a striking similarity to genes down-regulated by UV irradiation in both normal and repair-deficient ( XPB/CS , XPB/TTD ) cells: Nine lists overlapped with P<0 . 02 , and there was no similar finding among 1000 random-data simulations ( Table S6 ) . Intriguingly , the list of MER85-associated genes also overlapped significantly with the list of genes we had previously shown to be down-regulated by CSB ( P = 0 . 012; corrected to P = 0 . 015 by random-data simulation ) when hTERT-immortalized CSB-wt and CSB-null cell lines are compared [25] . There was no similar overlap with genes up-regulated by CSB . The most enriched Gene Ontology term was the Molecular Function “Glutamate Receptor Activity” ( Table S7 ) reflecting association of MER85 with six glutamate receptors ( GRM7 , GRID1 , GRID2 , GRIK2 , GRIN2A and GRIN2B ) and two related GPCRs ( 7-fold enrichment , P = 1 . 6e-5; no similar finding among 1000 random-data simulations ) . Similar glutamate-related terms were the most enriched in the other Gene Ontology categories as well ( data not shown ) .
Three mysteries have shaped thinking about Cockayne syndrome . First , the complete absence of CSB protein apparently does not cause CS , but rather a mild UV-sensitive syndrome with no developmental symptoms [33] . Yet all disease-associated CSB alleles identified to date are recessive; no dominant mutations are known . Second , nearly all CSB mutations that cause CS are located downstream of the exon 5/6 boundary ( codon 466 ) in the ATPase and C-terminal regions of the 1493 residue protein ( Figure 7; see Table S1 for details ) . And third , mouse models with either a truncating mutation similar to a severe human CSB allele ( CS1AN; K337STOP ) [55] or a CSA knockout [56] manifest the characteristic UV sensitivity of CS , as well as an unexpected susceptibility to skin cancer not observed for human CSB and CSA mutations , but only a subtle developmental phenotype . However , when the CSB defect is combined with an additional defect in an NER-GGR factor ( XPC [57] or XPA [58] ) , mouse models do recapitulate the full CS-like phenotype including growth retardation , neurological dysfunction , and reduced life span . The conserved CSB-PGBD3 fusion protein is expressed in both primary and established CS cells ( Figures 2 , 3 , 5 , and Figure S3 ) , and could explain these mysteries if the fusion protein , which is advantageous in the presence of functional CSB ( Tables 1 and 2 ) , were detrimental in its absence . According to this hypothesis , mutations downstream of CSB exon 5 would cause CS by impairing expression of functional CSB without affecting expression of the fusion protein; nonsense and frameshift mutations upstream of exon 6 would not cause CS [33] because they would also abolish expression of the fusion protein; mutations that do cause CS would be recessive because functional CSB masks the effects of the CSB-PGBD3 fusion protein; and mouse models of severe CSB mutations or a CSA knockout would not exhibit the full range of CS symptoms because rodents lack the PGBD3 insertion that generates the CSB-PGBD3 fusion protein . Consistent with this hypothesis , 21 of the 24 molecularly characterized CS genotypes appear capable of expressing the CSB-PGBD3 fusion protein ( Figure 7 and Table S1 ) . We have also confirmed experimentally that the fusion protein continues to be expressed in primary cells from 3 severely affected CS patients ( Figure 5 ) including patient CS1AN whose CSB genotype is known ( Table S1 ) . Only 3 of the 24 CS genotypes appear , on first sight , to be unable to express the fusion protein: the R453opal mutation found in first cousins CS1PV and CS3PV [59] , and the +T1359 insertion mutation in patient CS10LO which causes a frameshift at residue 427 and termination at residue 435 [60] . However , all 3 of these CS genotypes could conceivably generate detectable levels of the CSB-PGBD3 fusion protein . UGA codons are often leaky [61] and can be suppressed by several natural tRNAs [62] , [63] . Similarly , the existence and varying efficiency of programmed +1 and −1 frameshifting [64] suggests that frameshift mutations may sometimes be subject to a compensatory ribosomal frameshift that partially preserves the original reading frame . Indeed , ribosomal frameshifting is strongly dependent on context [65] which appears to be very “slippery” in the case of the +T1359 mutation ( TTT TTC CCA to TTT TTT CCC ) and could in principle increase the frequency of +1 frameshifts . Of course , leaky terminators and weak frameshifts might have been expected to rescue expression of both the CSB-PGBD3 fusion and full length CSB protein , but it should be kept in mind that the CSB-PGBD3 and CSB mRNAs are alternatively spliced and polyadenylated transcripts with different intron/exon structures and different 3′ UTRs . The role of mRNA context and intron/exon structure in nonsense-mediated decay is still not fully resolved [66] and it is possible that the same mutation could differentially affect translation or degradation of the CSB and CSB-PGBD3 mRNAs . Alternatively , the 3 anomalous patients ( CS1PV , CS3PV , and CS10LO ) may not express the fusion protein , but have other mutations or modifier genes which phenocopy the effect of the fusion protein . If the CSB-PGBD3 fusion protein does indeed play a role in CS , the complex clinical presentation of the disease [26] might be explained by variable expression of the fusion protein in different individuals and cell types ( Figure S3 ) , or by the degree or nature of residual CSB activity . CS and genetically related syndromes like cerebro-oculo-facio-skeletal syndrome ( COFS ) and the DeSanctis-Cacchione variant of xeroderma pigmentosum ( XP-DSC ) could also be multifactorial , requiring two or more “hits” or perhaps modifier genes — consistent with mouse models showing that a CSB defect must be combined with a second defect in an NER-GGR factor ( XPC [57] or XPA [58] ) to generate a strong developmental phenotype . A highly conserved and abundant protein which shares the first 5 exons of CSB is very likely to affect CSB-related cellular functions , but detailed functional characterization of the fusion protein will be required to understand how it could be detrimental in the absence of functional CSB protein . Unlike the ATPase domain of CSB encoded by sequences beyond the exon 5/6 boundary ( Figure 1B ) which is essential for DNA repair and chromatin remodeling , the N-terminal region encoded by CSB exons 1–5 is less well conserved and is apparently not essential either for transcription-coupled repair ( TCR ) or global genome repair ( GGR ) of UV-induced or bulky lesions [67] . Nonetheless , the possibility remains that in the absence of CSB , DNA repair complexes might recruit the CSB-PGBD3 fusion protein instead , blocking chromatin remodeling after attempted repair , preventing redundant repair pathways from accessing the damage , sequestering key repair factors , or even damaging the DNA if attempted repairs cannot be completed . This could also explain why CSA mutations are clinically indistinguishable from CSB mutations: Failure of CSA to target CSB [68] for ubiquitin-dependent degradation after CSB-dependent repair could have the same effect as the fusion protein in the absence of CSB — freezing repair complexes in place , and blocking subsequent events . Moreover , if the PGBD3 domain of the fusion protein targets CSB-dependent chromatin remodeling complexes to MER85 elements , loss of CSB might affect regulation of MER85-associated genes ( Tables S4 , S5 , S6 , S7 ) or enable MER85 elements themselves to sequester chromatin remodeling factors . The PGBD3 element in intron 5 of the CSB gene has not only been conserved for at least 43 Mya from marmoset to human , but the PGBD3 element itself is at least as highly conserved as surrounding CSB sequences ( Table 1 ) . Moreover , synonymous changes are at least as abundant for PGBD3 as for CSB in the human , chimp , orangutan , Rhesus and marmoset protein coding sequences ( Table 2 ) . We conclude that the initial PGBD3 insertion was selected for a new function advantageous to the primate host , and the CSB-PGBD3 fusion protein was thereafter subject to purifying selection to prevent loss of function . The high correlation of homologous MER85 insertions in human , chimpanzee and Rhesus macaque on chromosome 1 , and the absence of any lineage-specific PGBD3 pseudogenes , suggests that neither PGBD3 nor the related MER85 elements have been mobile since the three lineages diverged . These findings are consistent with several recent studies: an analysis of MER85 and MER75 sequence divergence by the Human Genome Sequencing Consortium [1] , a comparative analysis of repetitive elements within the human , chimpanzee and Rhesus genomes [69] , and an exhaustive study of DNA transposon activity in primates using ENCODE project genomic sequences [48] . The consistent D352 versus N352 difference in the putative catalytic DDD motif between decaying pseudogenes and PGBD3 itself in all species ( Figures S7 and S8 ) suggests that this change may have been critical for both the stability of PGBD3 within CSB and for the demobilization of PGBD-related pseudogenes and MER elements derived from them . The same appears to be true for the domesticated mariner transposase of the SETMAR fusion protein where the catalytic DDD triad has mutated to DDN [9] . We speculate that both the PGBD3 pseudogenes and the abundant MER85 elements are relics of a brief burst of activity when the PGBD3 transposon , newly introduced into an ancestral primate genome , replicated without hindrance , and both spawned and propagated dependent MER elements . Although complete and intact PGBD transposons are rare in all genomes examined [13] , the abundance of MER elements suggests that infection of the primate lineage had the potential to get out of control . Indeed , the apparent independent infection of galago , whether by horizontal transfer from the contemporary human-marmoset ancestor or from an external source , and the dozens of degenerate PGBD3-like sequences generated by this infection , highlight the virulence of feral PGBD3 . Insertional mutagenesis may have been the least of the dangers , as multiplying MER elements could have provided targets for genomic rearrangements mediated by the PGBD3 transposase — a well documented phenomenon for other DNA transposons with terminal inverted repeats such as Drosophila P-elements [70] . Domestication ( i . e . , insertion and fixation ) of PGBD3 within the CSB gene may have been the genetic response that restored genomic stability . Indeed , recruitment of the offending transposase itself in the form of a fusion protein has obvious advantages: The attenuated or inactivated transposase may simply occupy and occlude binding sites for the normal transposase — much as the absence of a germline-specific mRNA splice transforms the Drosophila P-element transposase into a somatic repressor of transposition [71] — or the fusion protein may actively guide host defense complexes to potential sites of excision , insertion , or rearrangement . It is also interesting to note that the S . cerevisiae homolog of XPD , known as Rad3 , inhibits Ty1 retrotransposition [72] . CSB binds to several TFIIH subunits including XPD [17] , suggesting a possible role for the N-terminal CSB domain of the CSB-PGBD3 fusion protein in silencing PGBD3 family elements . Repression of PiggyBac and/or MER85 mobility may explain the initial domestication of PGBD3 more than 43 Mya , but the CSB-PGBD3 fusion protein continues to be conserved and abundantly expressed in primates despite the passage of sufficient time to inactivate existing PGBD-related transposases . This suggests that the CSB-PGBD3 fusion protein may now be conserved for a new or secondary function . Noncoding elements account for the much of the genomic sequence under purifying selection in mammals [73] , and many of these conserved noncoding sequences may be remnants of ancient transposons [50] , [51] . The exaptation of SETMAR , fusing a SET histone methyltransferase domain to a mariner-like transposase , may have marked the emergence of a novel regulatory network based upon thousands of preexisting and now-selectable mariner elements [9] . Indeed , the exaptation of DNA-binding transposases has been proposed by Feschotte and Pritham [74] as “a pervasive pathway to create a genetic network [from] unlinked binding sites previously dispersed in the genome” . Our analysis of the genes closest to MER85 elements ( Table S4 ) suggests that the CSB-PGBD3 fusion protein may have created just such a regulatory network based on MER85 elements . We had previously shown by expression microarray analysis that CSB protein has a general chromatin remodeling function which includes the maintenance of transcriptional silencing; specifically , loss of CSB phenocopied conditions that disrupt chromatin structure such as treatment with inhibitors of histone deacetylation and DNA methylation , and defects in poly ( ADP-ribose ) -polymerase [25] . Surprisingly , many of the CSB-repressed genes are associated with MER85 elements ( Table S5 , “csb_reliable_up” database list ) . Just as striking was the association of MER85 elements with genes that are repressed following UV irradiation ( Tables S5 and S6 ) ; UV is known to cause nuclear translocation of CSA [75] which may in turn be required for full CSB function . Thus , recruitment of CSB or CSB-associated factors to MER85 elements by the CSB-PGBD3 fusion protein , perhaps in combination with independently transcribed PGBD3 transposase ( Figures 1 and 4 ) , may not only inhibit PGBD-mediated transposition , but also transcription of neighboring genes . The overabundance of neuronal genes — specifically glutamate receptors — among those closest to MER85 elements ( Table S7 ) is particularly intriguing because CS exhibits a strong neurodegenerative component . Sarkar et al . [13] note that the independent domestication of PiggyBac in nearly all metazoan lineages suggests that these transposable elements “have repeatedly been turned to advantage by the host . ” We suggest that this is a natural consequence of the PiggyBac lifestyle as a 3′ terminal exon trap in which the transposase ORF is flanked by 3′ splice site and polyadenylation signals ( Figure 1 and Figures S4 and S5 ) , and the activity of the transposase protein readily tolerates N-terminal fusions [15] . We do not yet know why the CSB-PGBD3 fusion protein has been selected and maintained in the primate lineage for over 43 My , but the answers will undoubtedly shed light on both CSB function and the longevity of PiggyBac transposases from cabbage looper moths to humans [13] .
HT1080 ( human fibrosarcoma ) , MRC5 ( human embryonic lung fibroblast ) and Saos-2 ( human osteosarcoma ) cell lines , along with primary CS cells GM0010903 and GM0010905 were obtained from repositories . WI38 human embryonic lung fibroblasts were immortalized by PG-13/neo retroviral transduction of hTERT cDNA [76] . Immortalized CSB ( CS1AN ) fibroblasts expressing either wild-type CSB cDNA ( CSB-wt line ) or enhanced green fluorescent protein ( CSB-null line ) were generated as described [25] . HeLa , WI38/hTERT , and CS1AN-derived lines were cultured in MEMα media with 10% fetal bovine serum plus supplements ( Gibco ) . Selection for expression of hTERT , CSB , and enhanced green fluorescent protein was maintained with 1 mg/ml G418 and 0 . 5 µg/ml puromycin , respectively . Cells were passaged by a wash in Puck's EDTA followed by trypsinization . HT1080 cells were cultured in MEMα media with 10% fetal bovine serum , and passaged by a wash in PBS followed by trypsinization . Total RNA was harvested directly from adherent cells with Trizol reagent ( Ambion ) . Synthesis of cDNA was primed with oligo ( dT ) and carried out using Superscript II reverse transcriptase ( Invitrogen ) . Each real-time reaction consisted of cDNA template from 20–50 ng of total RNA , 300 nM 5′ and 3′ gene-specific primers , and 1× SYBR Green master mix ( Applied Biosystems ) in 20 µl total reaction volume . All reactions were performed in triplicate using the DNA Engine Opticon real-time PCR system ( MJ Research ) . Relative differential expression was calculated from mean threshold cycle difference among the three replicate reactions . Products were visualized by pooling the three replicate reactions , purifying and concentrating over a QIAquick column ( Qiagen ) , and running half of the total sample on a 1 . 0% agarose gel stained with ethidium bromide . Primer sequences are available on request . Pairwise alignments and comparisons of analogous sequences were performed by Needleman-Wunsch global alignment , as implemented in EMBOSS needle . Overhanging ends were excluded from the identity calculations . We compared only homologous sequence regions: For example , we ignored the truncations of several PGBD3 pseudogenes when calculating their homology to PGBD3 . Coding region identity was calculated from translation start to stop codons . Pseudogene identities were calculated from the 3′ SS ( or start of homology ) to the stop codon ( or end of homology ) . We used RepBase RepeatMasker to identify the flanking MER85 and MER75B elements of PGBD3 and PGBD4 , respectively . To determine if the conservation of the PGBD3 and CSB coding regions is statistically significant , we analyzed the conservation of six noncoding sequences for comparison: 2 kb of intron sequence beginning both immediately upstream and downstream of the inverted repeats flanking PGBD3 , and the four PGBD3 pseudogenes . We determined the conservation of each of these six sequences individually by pairwise alignment between species using needle . We calculated a mean identity of all six and then used the inverted t-distribution to generate a confidence interval . The conservation of the PGBD3 and CSB coding regions was considered significant if the identity fell outside the 95% confidence interval of conservation for these six noncoding regions; this calculation is not dependent on the length of the query sequences . In order to determine whether MER85 and MER75B elements have been mobile since the divergence of the three primates , we used NCBI megaBLAST to identify all MER85 and MER75B elements on human , chimpanzee and Rhesus chromosome 1 ( June 2006 NCBI sequence releases ) , based on the consensus sequence for these elements in RepBase Update [47] . We then extracted 1 kb of the surrounding sequence for each element , and used EMBOSS needle to align every such human sequence pairwise with every sequence from chimpanzee and monkey . Marmoset ( version 2 . 0 . 2 , released June 2007 ) and orangutan ( version 2 . 0 . 2 , released July 2007 ) preliminary genome assemblies were downloaded from the Washington University Genome Sequencing Center . Mouse lemur ( draft v2 , released June 2007 ) , galago ( draft v1 , released June 2006 ) and tree shrew ( draft v1 , released June 2006 ) genome sequences were downloaded from the Broad Institute Mammalian Genome Project . Ka/Ks analysis was performed using SNAP ( Synonymous Nonsynonymous Analysis Program ) from the HIV Database at Los Alamos National Laboratories ( USA ) [42] . The significance of differences in Ka/Ks values was calculated with the Student's T-test using a two-tailed distribution and an assumption of unequal variance . All sequences and alignments used in this study are available on request . MER85 elements were identified in the March 2006 release of the NCBI human genome sequence by using NCBI megaBLAST to query each complete chromosome sequence for the RepBase MER85 consensus sequence . The start site of each element was matched to the closest start site of an HGNC-named gene from the ENSEMBL database . The resulting list of genes , excluding those located >1 Mb from their associated MER85 element , was analyzed with the 2007 . 1 release of the L2L Microarray Analysis Tool , including several unreleased lists representing CSB-regulated genes . The list of all HGNC-named genes in the ENSEMBL database was used as the null set . The P values generated by L2L were validated using random-data simulations as described previously [25] . Briefly , we randomly selected 1000 lists of genes from the null set , each the same size as the list of MER85-associated genes , and ran each through an identical L2L analysis . These random-data results were mined for the frequency of the outcomes seen in the analysis of MER85-associated genes . GM00739B/hTERT cells were transfected in 100 mm tissue culture plates with 10 µg of plasmid constructs using 15 µl of Fugene 6 reagent ( Roche ) . After 48 h , cells were washed with PBS and harvested by scraping . Cell pellets were resuspended in 100 µl of SDS loading buffer ( 25 mM Tris , pH 6 . 8 , 2% SDS , 0 . 1% bromephenol blue , 10% sucrose , 0 . 12 M β-mercaptoethanol ) , sonicated to shear DNA , and denatured by heating at 95°C for 10 min . Non-transfected plates of HT1080 and WI-38/hTERT cells were harvested in the same manner . Proteins were separated on a 6% gel by SDS-PAGE using the Mini-Protean 3 Cell ( BioRad ) in a Tris/glycine/SDS buffer ( 1 . 5 g/l Tris base , 7 . 2 g/l glycine , 1% SDS ) . Proteins were transferred to a PDVF membrane in 25 mM Tris , 192 mM glycine , and 20% methanol buffer using a Mini Trans-Blot Cell ( BioRad ) . After transfer , the PVDF membranes were blocked for 2 h at room temperature in TBST ( 50 mM Tris , pH 7 . 4 , 150 mM NaCl , 0 . 05% Tween 20 ) plus 5% nonfat dry milk . The membrane was then incubated at room temperature in TBST plus 5% nonfat dry milk for 2 h with a 1∶1000 dilution of primary antibody , washed twice for 10 min each , incubated for 1 h with a 1∶5000 dilution of HRP-conjugated secondary antibody ( Santa Cruz Biotechnology ) , and finally washed 4 times for 10 min each in TBST alone . Chemiluminescent detection was performed using the ECL Plus™ Western Blotting Detection System ( Amersham ) and Kodak X-Omat Blue film . Anti-CSB antibodies were generated in our laboratory as rabbit polyclonals raised to the C-terminal 158 amino acids or N-terminal 240 amino acids of CSB expressed as bacterial GST fusion proteins . Anti-GST antibodies were removed from the serum by passage over a GST column . Anti-PGBD3 antibody was purchased from AVIVA Systems Biology , catalog number ARP36534 . Human PGBD3 and the four PGBD3 pseudogenes are present in the NCBI Entrez Gene database , but have not yet been curated in the chimpanzee or Rhesus genomes . The accessions and approximate indicies for the coding region sequences used in this study are as follows: | For reasons that are still unclear , genetic defects in DNA repair can cause diseases that resemble aspects of premature ageing ( “segmental progerias” ) . Cockayne syndrome ( CS ) is a particularly devastating progeria most commonly caused by mutations in the CSB chromatin remodeling gene . About 43 million years ago , before humans diverged from marmosets , one of the last PiggyBac transposable elements to invade the human lineage landed within intron 5 of the 21 exon CSB gene . As a result , the CSB locus now encodes two equally abundant proteins generated by alternative mRNA splicing: the original full length CSB protein , and a novel CSB-PiggyBac fusion protein in which the N-terminus of CSB is fused to the complete PiggyBac transposase . Conservation of the CSB-PiggyBac fusion protein since marmoset suggests that it is normally beneficial , demonstrating once again that “selfish” transposable elements can be exploited or “domesticated” by the host . More importantly , almost all CSB mutations that cause CS continue to make the CSB-PiggyBac fusion protein , whereas a mutation that compromises both does not cause CS . Thus the fusion protein which is beneficial in the presence of functional CSB may be harmful in its absence . This may help clarify the cause of CS and other progerias . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
"and",
"genomics/gene",
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] | 2008 | An Abundant Evolutionarily Conserved CSB-PiggyBac Fusion Protein Expressed in Cockayne Syndrome |
Rapid arterial O2 desaturation during apnea in the preterm infant has obvious clinical implications but to date no adequate explanation for why it exists . Understanding the factors influencing the rate of arterial O2 desaturation during apnea ( ) is complicated by the non-linear O2 dissociation curve , falling pulmonary O2 uptake , and by the fact that O2 desaturation is biphasic , exhibiting a rapid phase ( stage 1 ) followed by a slower phase when severe desaturation develops ( stage 2 ) . Using a mathematical model incorporating pulmonary uptake dynamics , we found that elevated metabolic O2 consumption accelerates throughout the entire desaturation process . By contrast , the remaining factors have a restricted temporal influence: low pre-apneic alveolar causes an early onset of desaturation , but thereafter has little impact; reduced lung volume , hemoglobin content or cardiac output , accelerates during stage 1 , and finally , total blood O2 capacity ( blood volume and hemoglobin content ) alone determines during stage 2 . Preterm infants with elevated metabolic rate , respiratory depression , low lung volume , impaired cardiac reserve , anemia , or hypovolemia , are at risk for rapid and profound apneic hypoxemia . Our insights provide a basic physiological framework that may guide clinical interpretation and design of interventions for preventing sudden apneic hypoxemia .
Apnea and its accompanying arterial O2 desaturation are common clinical complications in preterm infants , occurring in more than 50% of very low birth weight infants [1] . In preterm infants , apnea causes a reduction in heart rate [2] and cerebral perfusion [3] , often requires mechanical ventilation , and is associated with neurodevelopmental impairment [4] . Apnea-related hypoxemia is of major concern in light of evidence that repetitive hypoxia in newborn animals results in irreversibly-altered carotid body function [5] , raising the possibility of impaired ventilatory control , and causes neurocognitive and behavioural deficits [6] . Respiratory arrest and hypoxemia are also strongly implicated in sudden infant death syndrome ( SIDS ) [7] , [8] where the speed at which hypoxemia develops is considered to be particularly dangerous . In preterm infants , the rate of arterial O2 desaturation ( ) can be highly variable and rapid , with average rates as high as 4 . 3% s−1 during isolated apneas [9] . An earlier framework to describe proposed that metabolic O2 consumption relative to alveolar volume determines the speed at which alveolar falls [10]; it was envisaged that is then a function of falling and the slope of the oxy-hemoglobin dissociation curve . However , such a model assumes that the rate of alveolar depletion of O2 , denoted pulmonary O2 uptake ( ) , is equal to tissue O2 consumption during apnea ( see Methods – Theory ) . Previous studies in adults have shown that falls from metabolic consumption during apnea [11] , and our previous modeling studies in lambs showed that the difference between and metabolic O2 consumption has a crucial role in determining during recurrent apneas [12] . We found that apneic changes in cause desaturation to occur in 2 stages . During stage 1 , lung O2 stores are depleted , and falls below metabolic consumption . During stage 2 , is close to zero , and tissue O2 needs are provided by depletion of blood O2 stores . To date , no complete theoretical analysis of the factors influencing desaturation during apnea has been published . The only available study [13] has a number of critical limitations . First , the model incorporated a constraint of a fixed difference between and mixed-venous saturation; thus dynamic changes in could not occur and their influence on could not be examined . Second , no assessment was made of the impact of cardiorespiratory factors on the two stages of O2 desaturation . Third , in focusing on adults , the study did not examine profound desaturation to levels well below 60% as can often occur in preterm infants [9] , [14] . Accordingly , the aim of the current study was to quantify the importance of cardiorespiratory factors relevant to during apnea , with particular reference to the preterm infant . Using a model that permits variation of during apnea , we examine a number of factors known to influence , such as lung volume [15] , metabolic O2 consumption [16] and pre-apneic arterial oxygenation [17] as well as factors that are particularly pertinent for the developing newborn , including anemia , hypovolemia , reduced O2 affinity , and chronically and acutely reduced cardiac output . We use the results to develop a conceptual framework for the interpretation of mechanisms underlying rapid during apnea .
To determine the independent influence of clinically relevant cardiorespiratory factors on during a single isolated apnea , we used a two-compartment lung-body mathematical model which incorporated realistic blood O2 stores and gas exchange dynamics ( Figure 1 ) , as described in Methods – Mathematical model ( a full list of symbols is provided in Table 1 ) . We used published parameters for healthy preterm infants born at ∼30 wk gestational age ( Table 2 ) ; the values represent measurements taken at approximately term equivalent age when surprisingly rapid desaturation has been observed [9] . We also derive analytic solutions for to quantify the importance of cardiorespiratory factors on to obtain a detailed view of the arterial O2 desaturation process , as described in Methods – Theory . To examine changes in O2/CO2 exchange during apnea , a single apnea was imposed on the model . During apnea , changes in alveolar O2 and CO2 stores are not constant ( Figure 2 ) ; importantly , alveolar ( ) did not continue to fall at its initial rate as governed by metabolic O2 consumption ( ) , but instead the rate of fall in was reduced as it approached mixed venous ( ) , an observation also reflected in the falling . As a result , two distinct phases for O2 depletion can be seen , which we refer to as stage 1 and stage 2 [12] . During stage 1 , fell rapidly and decreased and became dissociated from ; during stage 2 , with greatly reduced , both and fell together at a reduced rate . The two distinct phases were also observed for alveolar and arterial ( , ) although stage 1 for CO2 was substantially shorter than that for O2 . Such an effect results from the earlier fall in pulmonary CO2 uptake ( ) relative to the fall in ( Figure 2A ) and is reflected in the reduction in respiratory exchange ratio ( ) ( Figure 2B ) . Consequently , a more rapid fall in was observed compared with the rise in ( see Methods – Derivation of equations ) , such that fell by 100 mmHg in the time rose by just 14 mmHg ( Figure 2C ) . The time-course of is complex ( Figure 3 ) , a consequence of the nonlinear O2-dissociation curve in combination with the fall in . At apnea onset , started to fall with a rate equivalent to that predicted by Equation 12 , where ( Figure 3 ) . During apnea , changes in the slope of the O2-dissociation curve ( ) and dominated the time-course of desaturation as hypoxemia progressed . As started to fall after apnea onset , increased with little change in , resulting in a proportional increase in . However , as arterial hypoxemia developed , there was a concurrent decline in . As is directly proportional to the product ( Equation 11 ) it follows that during apnea , the peak of 3 . 5% s−1 occurred when reached a maximum . This occurred when neither nor was at its maximum ( both ∼50% of peak ) . Finally , with greatly reduced during stage 2 , remained at a constant level ( ) , close to that predicted by Equation 13 ( 1 . 8% s−1 ) . The following parameters were individually varied from their ‘normal’ values to quantify their influence on : resting , lung volume ( ) , metabolic O2 consumption ( ) , blood hemoglobin content ( Hb ) , cardiac output ( ) , R-L shunt fraction ( Fs ) , and the at 50% saturation ( P50 ) . All other parameters were kept constant to remove confounding effects , unless specified otherwise . To quantify we used 3 different measures . First , since apnea is considered clinically significant if it lasts for >10 s and is accompanied by bradycardia or O2 desaturation [18] , we calculated the average rate of fall in between apnea onset and 10 s later ( ) ; such a measure describes the immediacy of onset of desaturation and is analogous to the practical measurement of average used in many clinical studies [9] , [15] , [19] , [20] . Second , we determined the peak instantaneous during apnea ( ) , the value during the linear portion of arterial desaturation [10] , [21] which we find is not confounded by resting . Third , we report a measure of during stage 2 apnea ( ) . To quantify the sensitivity of to changes in each cardiorespiratory factor , we defined the term impact ratio as the ratio of proportional increase in to a small increase from the normal value of each factor . For example , an impact ratio of 1 indicates a one-to-one increase in with an increase in the factor , and a negative ratio indicates an inverse relationship . The impact of each cardiorespiratory factor on , , and is summarised in Table 3 .
To evaluate the independent effects of cardiorespiratory factors on we used a two-compartment model , incorporating both alveolar and blood gas stores . The inclusion of a realistic blood store was crucial to reveal that changes in occur as a consequence of arterial and mixed-venous saturation falling asynchronously during apnea ( Figure 3 ) . Our approach allowed us to extend the previous framework based on the assumption of constant [23] , which prevented the recognition that a steep O2-dissociation curve and low lung volume do not accelerate beyond stage 1 . Furthermore , the varying permitted recognition that cardiac output , hemoglobin content , and blood volume have a major influence on . In the current study , the typical value of found using our model was 3 . 5% s−1 whereas Poets and Southall [9] using beat-by-beat oximetry in preterm infants reported a mean value for during isolated apneas . Reasons for our lower value may lie with our simplifying assumptions . Notably , we assumed a homogenous lung compartment and complete gas mixing and as such , the model incorporated neither limitation of alveolar-capillary diffusion nor an uneven ventilation-perfusion distribution , two factors that could cause an increase in . In addition , we assumed a constant lung volume during apnea , equal to published values of functional residual capacity , whereas it is known that lung volume can fall during apnea [15] , [24]; based on our data , a fall in lung volume to 15 . 5 ml min−1kg−1 immediately after apnea onset would achieve of 4 . 3% s−1 ( Figure 5B ) . A final assumption implicit in our model is that all O2 transfer to the blood occurs via the pulmonary circulation . However , in very preterm infants there is evidence of percutaneous respiration in the first few days of life in both room air and with supplemental O2 [25] . With whole body exposure of 90% O2 to the newborn skin , it has been calculated that can be reduced by 8–10% [26] , likely via an increased resting mixed-venous saturation; our study demonstrates that such an effect would decrease during apnea . Our study is consistent with previous observations that and rapidly decline during apnea from their steady-state values [11] , with falling faster than . The relatively low blood capacitance for O2 compared with that for CO2 results in the resting alveolar–mixed-venous partial pressure difference being ∼12-fold greater for O2 than for CO2 . Consequently , when apnea begins ∼12 times more O2 than CO2 must diffuse across the lung to obliterate the alveolar–mixed-venous partial pressure difference . The slower fall in vs . provides for a faster depletion of alveolar O2 vs . CO2 stores; such an effect results in complete desaturation of arterial blood in the time rises by just 14 mmHg . These findings lead us to conclude that short-term O2 homeostasis is more unstable than CO2 homeostasis and thus that the danger of isolated apneas in infants is likely to be mediated via hypoxemia rather than hypercapnia . Our study provides for the first time a comprehensive analysis of the factors that determine arterial desaturation during apnea in preterm infants . We show that resting oxygenation in the form of alveolar has the greatest influence on desaturation at apnea onset . When apnea begins at an increasingly lower alveolar , more quickly reaches its maximum because rapidly arrives at the steepest part of the O2-dissociation curve . This effect explains the inverse relationship between mean and pre-apneic during apnea [17] , but as we show the peak slope itself is negligibly affected by reduced resting within the normal range . We demonstrate that is inversely related to lung volume during stage 1 of apnea as a result of the greater reduction in alveolar in poorly inflated lungs per unit of O2 transferred into the pulmonary capillaries . This analysis is consistent with the inverse correlation between and lung volume [15] , with the view that active upper airway closure maintains lung volume and slows [27] , [28] , and with our recent report that the application of continuous positive airway pressure effectively slows in lambs [29] . However , once stage 2 begins , the blood becomes the principal source of O2 and thus the only store which influences . A novel finding from our study is that reduced resting mixed-venous saturation , caused by either a reduced cardiac output or reduced hemoglobin content , strongly elevates peak , independent of metabolic O2 consumption . We show that reduced resting mixed-venous saturation accelerates via an increase in the peak value of ; in other words , low mixed-venous saturation provides for a greater pulmonary O2 uptake even in the presence of a developing arterial hypoxemia , and thereby increases . A role for hemoglobin in determining is consistent with the finding that elevated hemoglobin content in adults slows during apnea [21] . In contrast , blood transfusion to raise hemoglobin content in anemic preterm infants , a common clinical therapy , has little or no impact on the severity of apneic desaturation [30] . Our proposed explanation for the lack of benefit of raising hemoglobin content via transfusion is that it also reduces heart rate [30] and cardiac output . Thus , in the newborn , the rise in mixed-venous saturation expected after transfusion is counteracted by a tendency for mixed-venous saturation to fall as a result of reduced cardiac output . An investigation that failed to find an effect of cardiac output on [23] did not account for our finding that pre-apneic and transient changes in cardiac output have opposing influence on . Importantly , we find that a transient fall in cardiac output , characteristic of bradycardia during apnea in preterm infants [2] , conserves alveolar O2 via reduced and thus reduces ( see Equations 10 and 11 ) . Consistent with this finding , apneic bradycardia prevents a rapid fall in in adults [21] . We found that each of the factors examined exerts a unique and therefore recognisable influence on the time course of the desaturation process ( Figure 10 ) . Low alveolar can be recognised by a left-shift of the desaturation trajectory so that desaturation begins sooner following the onset of apnea . A steep desaturation slope in the early phase of stage 1 points to a low ratio of lung volume to metabolic O2 consumption . In the late phase of stage 1 , when desaturation proceeds in a linear fashion , a low resting mixed-venous saturation accelerates and leaves the fingerprint of a low inflection point in arterial O2 desaturation; low resting mixed-venous saturation reflects low cardiac output or hemoglobin content with respect to O2 consumption . Lastly rapid during stage 2 signifies a low total blood O2 capacity with respect to O2 consumption which would point to either low blood volume or anemia . The presence of a constant R-L shunt , while having no influence on , causes a parallel downwards shift in the desaturation trajectory . The unique impact of different factors on the desaturation curve may be used to guide preventive clinical intervention . We show theoretically that the lower lung volume [31] and higher metabolic O2 consumption [32] of preterm compared to term infants predisposes to a rapid onset and progression of desaturation during apnea . Two reports offer support for this view . First , rapid desaturation occurs in infants with low functional residual capacity [15] , a finding that may help to explain the more frequent O2 desaturation events during active sleep [33] when functional residual capacity is reduced . Second , frequent desaturation is characteristic of preterm infants with bronchopulmonary dysplasia ( BPD ) [34] whose O2 consumption is 25% greater [35] , and functional residual capacity is 25% less [36] , than in preterm infants without BPD; Equations 11 and 12 predict that such differences increase both immediate and peak by ∼70% . In addition , hypoventilation and reduced resting in infants with BPD , as inferred from elevated [37] , further increase desaturation at apnea onset . Our finding that each rise of 1% in inspired O2 provides ∼1 s of delay ( right-shift ) in the onset of apneic desaturation ( Equation 15 ) may guide the titration of supplemental O2 for the prevention of apneic hypoxemia while minimising the well known side-effects of long-term exposure to hyperoxia . Our study has implications for the management of infants in clinical care . Metabolic O2 consumption can be elevated after feeding [38] , with reduced ambient temperature [39] , and via the adminstration of methylxanthines [40] . Despite the success of methylxanthines in reducing the frequency of apnea and bradycardia , such treatment has surprisingly little impact on hypoxemic episodes [41]; we suggest that the elevated O2 consumption and the absence of bradycardia are likely to increase during those apneas that persist despite treatment . The severity of hypoxemic episodes is reduced by switching preterm infants from supine to prone [42] , which may increase functional residual capacity [43] and improve diaphragm function , increase tidal volume and increase resting alveolar [44] . Our finding that low cardiac output leads to increased during apnea leads to the suggestion that judicious adjustment of inotropic support in infants with cardiac abnormalities could improve resting mixed-venous saturation and reduce apneic hypoxemia . Hypoxemic events become less frequent between infancy and childhood , despite an unchanged apnea frequency [28] , perhaps as a result of a fall in O2 consumption per body weight . However , before this occurs , infants experience a period of susceptibility to rapid desaturation during apnea as a result of a fall in hemoglobin content and O2 affinity [22] and a rise in O2 consumption [45] . The implications for SIDS are obvious in that these changes coincide with the peak incidence for SIDS at 2–3 months [46] . SIDS also occurs disproportionately in preterm infants [46] , who manifest severe anemia [22] and greater O2 consumption . Infants resuscitated from apparent life threatening events have been found to have lower hemoglobin content [47] , pointing to a potential role for rapid in the progression of such events . It is possible that the rapid development of apneic hypoxemia initiates prolonged hypoxic cardiorespiratory depression that in turn leads to SIDS . We have provided a mathematical framework for quantifying the relative importance of key cardiorespiratory factors on the rate of arterial O2 desaturation during apnea , with particular relevance to preterm infants . For the first time we have demonstrated that each of the factors examined has a signature influence on the trajectory of desaturation , providing quantitative insight into the causes of rapidly developing hypoxemia during apnea .
Here we derive the explicit equations used within the current study to encapsulate key relationships pertaining to gas exchange and arterial desaturation during apnea . | When breathing stops , the flow of O2 into and the flow of CO2 out of the body cease . Such an event , termed an apnea , can be especially dangerous in preterm infants in whom it can lead to a rapid decline in arterial O2 saturation , reaching rates of 3–8% per second , rapidly reducing O2 to a level that could lead to neurological damage . Despite extensive experimental research , we have a poor mechanistic understanding of the causes of rapidly developing hypoxemia . We describe a new mathematical model that allows examination of the importance of the major cardiorespiratory factors that are likely to influence the speed at which arterial hypoxemia worsens during apnea . We found that high metabolic rate as well as reduced pre-apneic ventilation , lung volume , cardiac output , hemoglobin content , blood O2 affinity , and blood volume accelerate the development of hypoxemia during apnea . Importantly , the cardiorespiratory factors that contribute to rapid hypoxemia are all pertinent to the preterm infant during early postnatal development . Thus the newborn is highly susceptible to rapid and severe desaturation , potentially explaining the propensity of preterm infants , particularly those with apnea , to neurological impairment . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
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] | 2009 | A Model Analysis of Arterial Oxygen Desaturation during Apnea in Preterm Infants |
Real-world tasks typically consist of a series of target-directed actions and often require choices about which targets to act on and in what order . Such choice behavior can be assessed from an optimal foraging perspective whereby target selection is shaped by a balance between rewards and costs . Here we evaluated such decision-making in a rapid movement foraging task . On a given trial , participants were presented with 15 targets of varying size and value and were instructed to harvest as much reward as possible by either moving a handle to the targets ( hand task ) or by briefly fixating them ( eye task ) . The short trial duration enabled participants to harvest about half the targets , ensuring that total reward was due to choice behavior . We developed a probabilistic model to predict target-by-target harvesting choices that considered the rewards and movement-related costs ( i . e . , target distance and size ) associated with the current target as well as future targets . In the hand task , in comparison to the eye task , target choice was more strongly influenced by movement-related costs and took into account a greater number of future targets , consistent with the greater costs associated with arm movement . In both tasks , participants exhibited near-optimal behaviour and in a constrained version of the hand task in which choices could only be based on target positions , participants consistently chose among the shortest movement paths . Our results demonstrate that people can rapidly and effectively integrate values and movement-related costs associated with current and future targets when sequentially harvesting targets .
Studies of reach planning and control have focused on movements towards single targets , with theoretical accounts focusing on the minimization of various movement-related costs [for reviews , see 1 , 2–5] . However , real-world tasks often involve choosing targets from among multiple alternatives , and therefore not only involve decisions about how to move but also where to move . Moreover , such tasks often involve a sequence of actions in which choices are made at each step . Although decision-making related to target selection has been extensively studied in the context of eye movement preparation [6–9] and in more cognitive tasks such as the traveling salesperson problem [10–13] , comparatively little work has been done on reaching , in which movement-related costs are likely to play a more critical role [14 , 15] . A handful of reaching studies have examined how values and costs influence the selection of targets in single movements and fixed sequences of movements . This work has shown that when pointing to target configurations that have different reward and penalty regions , people are able to choose their average pointing location to minimize the loss that accrues through the variability of pointing [e . g . 16] , but that when reaching to two consecutive targets in a fixed time period , people fail to invest more time in the movement to the more valuable target [17 , 18] . In addition , when ‘harvesting’ a sequence of targets by maintaining a hand-controlled cursor on each target for a fixed duration , people learn to optimize reward by predicting the required duration [19] . However , to our knowledge no study has investigated the selection of targets during a sequential reach task . In performing a task involving the selection of a series of targets , each successive choice decision could be made de novo in order to maximize rewards and minimize costs associated with only the next target selection . However , by ‘looking ahead’ and considering the rewards and costs across future potential targets , it may be possible to further optimize performance . Here we assessed sequential decision-making using a movement foraging task in which participants could choose the order in which they harvested from a set of targets of varying size , value and location across the workspace ( Fig 1A ) , either by moving a hand-held handle to a target and clicking a button on the handle ( hand task ) or making a saccade to a target and fixating it for 150 ms ( eye task ) , with the goal of maximizing reward . Gaze was unconstrained in the hand task . The trial duration was such that , in both tasks , participants could only harvest around half of the targets , placing a premium on efficient decision-making . We examined four conditions: two in which only the size or only the value of targets varied , and two in which both size and value varied in either a correlated or anti-correlated manner ( Fig 1B ) . We evaluated performance using a probabilistic model , inspired by optimal foraging theory [20 , 21] , that predicts target-by-target harvesting probabilities based on rate of reward , costs associated with target distance and size , and decision noise . A key feature of the model is that it can incorporate a number of future successive harvests with temporal discounting; i . e . , it can ‘look ahead’ . Because moving the hand is more costly , in time and energy , than moving the eyes , we predicted that target choice in the hand task , in comparison to the eye task , would be more strongly influenced by movement-related costs and would take into account a greater number of future targets so as to optimize the route through the targets .
Fig 1C–1E shows hand or gaze paths for single trials performed by different participants in each of the three foraging tasks in the ‘small-high’ condition in which target size was inversely related to target value . These trials illustrate several general features of the performance we observed in these tasks . Participants exhibited a strong tendency to move between adjacent targets in the hand task but also tended to make relatively small movements in the eye task . Even in the constrained hand task ( Fig 1E , in which the targets had to be harvested in order of decreasing value ) , participants moved to close-by targets where possible . In the hand task , participants also tended to minimize changes in movement direction between successive harvested targets whereas , in the eye task , sharper changes in direction were often observed . One strategy that participants employed to limit changes in hand movement direction was to harvest targets along a roughly circular route . In all three tasks , participants typically harvested most , if not all , of the high value targets . In the hand task , they often harvested medium or , less frequently , low value targets in between the high value targets . In contrast , in the eye task , there was a stronger tendency to harvest the high value targets prior to lower valued targets . The pattern observed in the hand task suggests that participants were aware that they could typically obtain 8 or 9 targets and could therefore harvest some less valuable targets while ensuring that most , if not all , high value targets were harvested . Harvesting a few less valuable targets en route between high value targets often allowed participants to avoid large amplitude hand movements . Fig 2 shows the mean number of harvests for each condition of the hand and eye foraging tasks . A one-way ANOVA revealed that , overall , the number of targets harvested in the hand ( M = 8 . 4 , SE = . 16 ) and eye ( M = 8 . 2 , SE = . 18 ) tasks were similar ( F1 , 13 = . 12 , p = 0 . 73 ) , allowing us to reasonably compare target preferences across tasks . In both tasks large targets were preferred when only size was varied and high value targets were preferred whenever value was varied ( i . e . , in the three other conditions ) . However , in the small-high condition , in which value and size traded off , the preference for high value targets was weaker in the hand task than the eye task . To quantify these results , for each condition we carried out a two-way mixed model ANOVA to assess the effects of target type ( 3 levels for each condition: see Fig 1B ) and task ( 2 levels: eye or hand ) on the number of harvests . In no condition was there a main effect of task ( p > 0 . 05 in all 4 cases ) . A main effect of target size was found in the size condition ( F2 , 26 = 48 . 8 , p < 0 . 001 ) but there was no target by task interaction . A main effect of target value was observed in the value ( F2 , 26 = 104 . 7 , p < 0 . 001 ) , small-high ( F2 , 26 = 91 . 6 , p < 0 . 001 ) , and small-low ( F2 , 26 = 280 . 6 , p < 0 . 001 ) conditions . A target value by task interaction was found for the value ( F2 , 26 < 5 . 5 , p < 0 . 05 ) , small-high ( F2 , 26 < 91 . 6 , p < 0 . 001 ) , and small-low ( F2 , 26 < 4 . 6 , p < 0 . 05 ) . In these three conditions , participants in the eye task harvested more high value targets than participants in the hand task ( Fig 2 ) , possibly because smaller movement-related costs in the eye task allowed participants to harvest targets according to their value , with limited influence of their sizes and/or distances . Although size similarly influenced hand and gaze target selection in the size condition , when size was paired with value , size had a substantial influence on hand target selection ( compare the small-high and small-low conditions ) but little influence on gaze target selection . Thus , whereas participants in the eye tasks exhibited a clear preference for large targets when only size varied , this preference was largely superseded by value when value also varied . For the probabilistic model , we estimated the time required to move to any given target based on its distance and size . Specifically , for each participant , we performed separate regressions between recorded movement time ( i . e . , time between harvests ) and target distance for each target size , where regressions in the arm task were obtained after pooling data from the free choice and constrained tasks . Two-way mixed model ANOVAs were carried out to assess how the slope and intercept varied with task and target size . There was no significant difference ( F1 , 13 = 2 . 3 , p = 0 . 2 ) between the slopes in the eye ( M = 0 . 9 s/m; SE = 0 . 2 s/m ) and hand ( M = 1 . 0 s/m; SE = 0 . 1 s/m ) tasks . However , the intercepts were slightly greater ( F1 , 13 = 15 . 8 , p = 0 . 002 ) in the eye task ( M = 0 . 3 s; SE = 0 . 02 s ) than the hand task ( M = 0 . 2 s; SE = 0 . 01 s ) . Note that with the required fixation duration in the eye task , the time between successive harvests was similar to the hand foraging tasks . The slope ( F2 , 26 = 18 . 6 , p < 0 . 001 ) and intercept ( F2 , 26 = 6 . 0 , p = 0 . 007 ) also depended on target size . Specifically , the slope and intercept tended to decrease and increase , respectively , as a function of target size . We developed a simple model of harvesting in which the choice of the next harvest target could depend on the distance to each potential target as well as their size and value ( which affects the reward rate ) . We first evaluate the contribution of these factors using the 1-look-ahead version of the model , which only considers the next or immediate harvest when predicting which target will be selected . After establishing , for both the hand and eye tasks , that the full model ( with all three of these components ) provided a better overall fit than any of the reduced models with one component removed , we then examined versions of the model with 1 to 5 look-ahead steps . That is the next harvest could depend on the weighted combination of these components for up to the next 5 harvests . Such look ahead in the model would allow the eye or hand to forgo short term gains for longer terms gains , for example , by moving to regions of the workspace where there are more rewarding targets . Finally , we evaluated the performance of the best-fit N-look-ahead model by comparing model predictions against actual data . In all cases , the models were fit to each individual participant from all trials in all four conditions ( i . e . , size , value , small-high , and small-low ) fit together . We only considered up to 8 harvests in each trial because as the number of harvests increased beyond 8 , the number of trials decreased sharply . We did not analyze the first harvest of each trial because , in the hand task especially , participants tended to rapidly launch their initial movement in a relatively fixed direction and choose between the one or two targets located in this direction ( e . g . , the center target in the first row and the target to its left ) . By limiting decision-making in the initial movement , participants could initiate the task quickly while bringing their hand ( or gaze ) towards the grid of targets and giving themselves time to select the next target or targets . We used maximum likelihood ( MATLAB fminsearch ) to fit the model to the entire dataset of 200 trials by 8 harvests ( max ) for each participant separately . The model assigned , to each available target , a probability that this target would be chosen next . Each selection made by the model began from the most recently harvested target , i and considered the sizes , values and positions of the remaining targets , j ∈ H , where H represents the set of remaining non-harvested targets . We defined the distance from target i to target j as dij , the value of target j as vj and the size of target j as sj . An estimate of the time required to move from target i to target j , tij , was derived from linear regressions , relating movement time to movement distance , that were computed separately for each participant and target size . In our sequential target task , we found that the relationship between movement time and distance was close to linear . Rate of reward is known to be a key factor in decision making [e . g . 22 , 23] and the rate of reward for selecting target j was calculated as rij = vj / tij . A pure reward based model would only consider rate of reward . In this case , target distance and size can only influence choice via effects on movement duration . However , distance and size may also influence target choice via other costs . For example , distance may be associated with a physical effort cost [24] whereas target size may be associated with a cost linked to planning and controlling more precise movements [25] . To capture this possibility , the cost , cij , of choosing target j as the next harvest was calculated as: Cj=cij=−rij+w1dijγ−w2sj ( 1 ) That is , the cost depended on the negative reward rate , a penalty associated with distance and a penalty for reaching to smaller targets . The penalty associated with distance captures possible movement-related energy costs . We included the exponent gamma in this term to accommodate the possibility of a nonlinear mapping between effort and distance . A power function was selected because it can capture a wide range of nonlinear functions . We assume there is noise in the decision making process ( or calculation of this cost ) so that potential harvest targets that have similar costs may be chosen with similar probability . As is commonly used in models of decision making , we used a softmax selection rule [26] so that the probability of choosing target j became: Pj=e−β⋅Cj∑je−β⋅Cj ( 2 ) The parameter β determined the combined noise in perceptual and decision processes , with infinite noise assuming a value of β = 0 . For very large values of β , the probability of choosing the target with the lowest cost approaches 1 . For intermediate values the probabilities are always ordered according to the cost ( highest probability for lowest cost ) but allow higher cost targets to be selected occasionally . To assess the contribution of reward rate , target distance , and target size , we compared the full one-ahead model against the three submodels , obtained by removing each individual factor , using the Bayesian Information Criterion ( BIC ) , with a lower BIC indicating a better fit [27 , 28] . The Bayesian Information Criterion ( BIC ) was used to compare models with different numbers of parameters: BIC=2ln ( L ) +kln ( N ) ( 3 ) where L is the likelihood of the data given the model , k is the number of degrees of freedom of the model and N is the total number of data points . The BIC allow models with different numbers of parameters to be compared , with the one with a lower BIC being preferable . The difference in the BIC scaled by 0 . 5 approximates the log of the Bayes factor , the likelihood that one model is better than another ( 27 ) . A Bayes factor larger than 10 indicates strong evidence in favor of a model , and a value larger than 100 is considered decisive ( 28 ) . Fig 3A and 3B show the change in BIC score ( Δ BIC ) going from the full model to each of the three submodels , for the hand and eye tasks respectively . The full model provided the best fit for all 8 participants in the hand task and for 4 of 7 participants in the eye task . In the other 3 participants who performed the eye task , the best model included reward rate and distance but not target size . However , this model and the full model had very similar BIC scores in all participants in the eye task ( i . e . , Δ BIC was very close to zero ) . For all participants in the hand task , the model omitting target distance was the poorest predictor of target choice by a large margin , and this model was also the poorest predictor in 5 of 7 participants in the eye task ( with the model omitting reward rate being the poorest predictor in the other 2 participants ) . Overall , these results indicate that in both the hand and eye tasks , all three parameters of the full model—i . e . , reward rate , target distance , and target size—influence choice behavior . We next considered an extension of the model that could ‘look ahead’ to take into account potential future harvests when selecting the next harvest . When looking n harvests ahead ( where n = 1 corresponds to the model already described ) , we consider each potential next harvest target j and all possible subsequent sequences of n-1 harvests ( k , l , m , … ) . For each harvest we calculated the cost of each harvest with different weightings , λ , applied to future harvests . For example , when looking n = 5 steps ahead ( i . e . , j , k , l , m , n ) the cost of choosing j as the next harvest is given by: Cj=minH ( k , l , m , n ) ( cij+λ1cjk+λ2ckl+λ3clm+λ4cmn ) ( 4 ) where the minimum is taken over all quadruplets H ( k , l , m , n ) of potential harvested targets after the first harvest . Therefore Cj represents the smallest cost associated with making the next harvest target j when considering the next 4 targets . Again we used the softmax function to select the next harvested target . We modeled look aheads , n , from 1 to 5 targets ( due the combinatorial nature of the problem it was not possible to consider look aheads of 6 or more ) . For a look ahead of n , the model had n+4 parameters . We also fit reduced models in which one of the three components in Eq 1 was set to zero . Fig 3C and 3D show BIC scores as a function of the number of look-ahead harvests for each participant in the hand and eye tasks , respectively . Note that the curves have been vertically aligned so that they start from the same point , which is the mean BIC score , across participants , for the 1-look-ahead model . In the hand task , the BIC score tended to decrease ( indicating a better fit ) as a function of look-ahead steps whereas , in the eye task , the BIC score tended to increase for the majority of participants . The histogram in Fig 3E shows the number of participants best fit by models with 1 to 5 look-aheads . In the hand task , the best-fit model for all participants contained at least 3 look-aheads and , in 5 of the 8 participants , the 5-look-ahead model provided the best fit . With the exception of one participant , the BIC scores appeared to level off somewhat as the number of look-aheads increased . In the eye task , the 1-look-ahead model provided the best fit in 5 of 7 participants , with the other two participants’ choice behavior best fit by models the 3 and 5 look-aheads . Despite the variation across participants within each task group , there is a clear difference between the two tasks in the number of look-aheads . Whereas hand task all participants consider a sequence of forthcoming target choices when selecting the next target whereas , in the eye task , most participants chose targets on a harvest-by-harvest basis . When considering the best-fit models for each participant , the average power exponent on the distance term ( gamma in Eq 1 ) was 0 . 36 ( SE = 0 . 09 ) for the eye task and 1 . 14 ( SE = 0 . 23 ) for the hand task . Thus , the influence of target distance in determining target choice was close to linear in the hand task but compressive in the eye task , consistent with the idea that the additional cost of making larger eye movements was less than the additional cost of making larger hand movements . In the model , different weights are assigned to the costs associated with each look-ahead step . To further assess the contribution of future harvests to the choice of target in the current harvest , we fit the 5-look-ahead full model to each participant . Fig 3F shows the average weights assigned to each look-ahead step in the hand and eye tasks . ( Note that we normalize the weights for a given participant to sum to one . ) For both tasks , the largest weight was assigned to the immediate choice option ( i . e . , 1-look-ahead ) , with this weight being higher in the eye task than the hand task , consistent with the finding that the 1-look-ahead model provided the best fit for most participants who performed the eye task . These effects were confirmed by a two-way look-ahead number by task ANOVA , which revealed a main effect of look-ahead number ( F4 , 52 = 130 . 0 , p < 0 . 001 ) as well as a look-ahead by task interaction ( F4 , 52 = 4 . 6 , p < 0 . 05 ) . To evaluate the best-fit model for each participant ( i . e . , the full model with the number of look-aheads that provided the lowest BIC ) , we compared the actual target selections to those predicted by the model . At each harvesting step , the model assigns a probability of selection to each available ( i . e . , non-harvested ) target . The black traces in Fig 4A and 4B show , for each participant in the hand and eye tasks respectively , the probability that the participant selected the target assigned the highest probability of selection by the model , as a function of harvest number . Overall , participants selected the highest probability target 61 percent of the time in the hand task and 53 percent of the time in the eye task . As is evident from the figure , across all harvests in both tasks , the probability of selecting the highest probability target was well above chance ( dashed grey traces ) , which increases from 1/14 ( 0 . 071 ) to 1/8 ( 0 . 125 ) from the second to the eighth harvest . The black traces in Fig 4C and 4D show the probability , assigned by the model , to the actual target selected by the participant , and the grey traces show the difference between the highest assigned probability and the probability assigned to the selected target . Although participants did not always select the target with the highest assigned probability , they generally selected a high probability target . Overall , the average ranking—from highest to lowest probability—of the selected target was 1 . 61 in the hand task and 1 . 96 in the eye task . These results are indicative of the fact that the probabilities assigned to the most probable few targets were often similar . Note that both the probability of selecting the highest probability target ( black traces in Fig 4A and 4B ) and the probability assigned to the selected target ( black traces in Fig 4C and 4D ) increased over the initial few harvests in the hand task , but were quite constant in the eye task . This observation is consistent with the fact that , in the eye task , the influence of target distance is relatively weak , which yields more target options with similar probabilities of selection . To assess how well participants performed , we estimated each participant’s ‘optimal’ performance using an optimal planner that could look 5 harvests ahead at each harvest . Note that we could not use an optimal planner that considered all targets as this involved 15 factorial ( ~10^12 ) possible harvest orders , which is too many to evaluate . For each harvest choice , the optimal planner uses the predicted duration of each possible sequence of remaining harvests ( up to 5 ) to select the best sequence in terms of maximizing rate of reward ( given the remaining time ) . The predicted duration was determined separately for each participant based on that participant's estimated movement durations as a function of distance and target size ( see above ) . This target-by-target choice was repeated until the trial time elapsed . We computed the efficiency of each participant's performance as the mean , over all trials from all conditions , of the ratio of the actual to 'optimal' points score . For the eye task , the average ratio was 0 . 95 and the ratio ranged from 0 . 85 to 0 . 98 across the participants . For the hand task , the average ratio was 0 . 91 and the ratio ranged from 0 . 84 to 0 . 96 across the participants . A t-test failed to show a significant difference between the two tasks ( t13 = 1 . 63; p = 0 . 13 ) . We also computed the ratio of the actual number of target harvested to the number harvested by the optimal planner . For the eye task , the average ratio was 0 . 95 and the ratio ranged from 0 . 85 to 0 . 99 across the participants . For the hand task , the average ratio was 0 . 92 and the ratio ranged from 0 . 84 to 0 . 99 across the participants . As with the points ratios , a t-tested failed to show a significant difference between the two tasks ( t13 = 1 . 50; p = 0 . 16 ) . Thus , in terms of both points and targets harvested , participants in both tasks were highly efficient and performed almost as well as the optimal 5-ahead planner . To examine how participants prioritized targets of varying value and size across harvests , for each participant and condition we calculated the proportion of targets , of a given size or value , that were selected on each successive harvest . Fig 5 shows these proportions , averaged across all participants , for harvests 2 through 8 in both the hand and eye tasks . The figure also shows the predicted proportions , averaged across participants , obtained with each participant’s best-fit model . ( Note that the data from all four conditions the predicted together . ) Qualitatively , it is evident that the model was able to capture the choice behavior of the participants in both tasks and in all four conditions . In the eye task under conditions in which target value varied ( i . e . , the value , small-high , and small-low conditions ) , participants had a strong tendency to initially select high value targets right from the start ( i . e . , from harvest number 2 onwards ) , and largely ignored the size of the targets . When most or all of the high value targets were harvested , they then strongly favored the middle value targets . In contrast , under the corresponding conditions in the hand task , the influence of value on target selection was weaker , and varied considerably across conditions . Thus , although high value targets were always preferred , this preference was weaker when the high value targets were small ( small-high condition ) and stronger when the high value targets were large ( small-low condition ) . A modest influence of target size was observed under the size condition in both tasks , even though size had little influence on choice behavior in the eye task in the other conditions . These results are consistent with the finding that movement related costs play a more significant role in shaping choice behavior in the hand task than the eye task . Participants in the hand task appeared to exploit the fact that they had enough time to harvest most , if not all , of the high value targets without having to harvest the high value targets first . Presumably , they were willing to harvest lower value targets in order to reduce movement related costs . In contrast , participants in the eye task tended to select the high value targets , presumably because they can do so without incurring large movement related costs . Fig 6 shows , for each of the conditions , frequency distributions of target-to-target distances for all actual and predicted harvests in both the hand and eye tasks averaged across participants . In both tasks , participants most often harvested targets that were directly adjacent ( up , down , left , or right ) to the previously harvested target and were therefore on average 60 mm away ( corresponding to the mode of the distributions ) . Participants less frequently harvested adjacent but oblique targets , located on average 90 mm away , and targets located 2 or , even less frequently , 3 targets away . Although the distributions for the hand and eye tasks were similar in that adjacent targets were strongly preferred , a greater number of larger distances were seen in the eye task ( blue traces ) than the hand task ( red traces ) . Two independent samples Kolmogorov-Smirnov tests revealed significant differences ( p < 0 . 05 ) between the two tasks in the three conditions where target value varied ( Fig 6 ) , but not in the size condition where only target size varied ( upper left subplot ) . Presumably , participants were more willing to move greater distances in the eye task , when value was manipulated , because of the lower movement-related costs involved . However , target size alone did not drive participants to move greater movement distances in the eye task . We included a constrained hand task , in which participants were required to harvest targets in order of decreasing value , for two reasons . First , comparing performance on the constrained and unconstrained ( or ‘free’ ) hand tasks enables us to test whether sometimes selecting lower value targets before harvesting all high value targets ( as in the unconstrained hand task ) , improves performance . Second , by focusing on the first 5 harvests ( i . e . , harvests of the high value targets ) in the constrained task , we could assess how effectively participants minimize movement path distance . Fig 7A and 7B show the average number of points and harvests , respectively , per trial for both the free and constrained hand tasks in the three conditions examined in the constrained hand task . ( Note that the size condition was not performed since the targets all had equal value . ) On average , participants harvested all 5 high value targets and one or two mid-value targets in the constrained hand task . A task ( 2 levels: hand , eye ) by condition ( three levels: value , small-high , small-low ) repeated measures ANOVA showed that participants harvested more points ( F1 , 7 = 68 . 0 , p < 0 . 001 ) in the free choice task than the constrained task . A similar ANOVA showed that participants also harvested more targets ( F1 , 7 = 72 . 4 , p < 0 . 001 ) in the free choice task than the constrained task . These findings indicate that participants’ inclination to sometimes forgo high value targets in the free task led to more optimal performance . The analysis also uncovered a task by condition interaction for both the number of harvested points ( F2 , 14 = 43 . 3 , p < 0 . 001 ) and targets ( F2 , 14 = 6 . 2 , p = 0 . 012 ) . As shown in Fig 7A and 7B , for both variables , the largest discrepancy between tasks is seen in the small-high condition , where high value targets were small . In the constrained task , fewer targets were harvested when participants were required to harvest the small targets first . This could reflect a speed-accuracy trade-off , more challenging visual search , or both . However , even when the high value targets were large , the number of targets harvested in the constrained task was less than in the free choice task . To assess how effectively participants minimized hand path distance , we computed , for each trial in the constrained task , the distance between successive targets for all 120 possible harvest orders of the first five targets . Fig 7C shows the distribution of possible path distances ( dashed line ) together with the distribution of actual path distances that participants chose . It is evident that participants selected harvest paths from the lower end of the distribution of all possible paths . The histogram in Fig 7D shows the proportion of trials in which participants selected the shortest possible path ( rank 1 ) , the next shortest path ( rank 2 ) , and so on up to the 20th shortest path . ( The gray solid line shows the average total distance of the ranked paths . ) Participants chose the shortest path on close to 40% of trials and selected one of the 5 shortest paths on approximately 75% of trials . These data show that participants were efficient at rapidly harvesting a sequence of targets the resulted in a relatively short cumulative distance .
We examined sequential decision-making within the context of a rapid motor foraging task . Participants had a limited time to harvest targets of varying value , position , and size either by moving the hand to the targets ( hand task ) or fixating the targets ( eye task ) . We fit a probabilistic ‘look ahead’ model in which target choice depended on a cost function that included rate of reward , target distance , and target size of up to the next 5 targets , with different weights allowed for the cost of each future target . We found that in the hand task , in comparison to the eye task , target choice was more strongly influenced by target distance and size , although target size did influence target choice in the eye task when target value was constant . In addition , participants in the hand task took into account a greater number of future targets , with most participants considering at least 5 future targets , compared to the eye task , with most participants only considering the next target . In a version of the hand task designed to examine route-finding efficiency , we found that participants were capable of selecting efficient routes through the targets that reduced total distance travelled . These results suggest that participants take into account the motor costs associated with different effectors so as to be efficient in foraging with both the eye and hand . Studies examining the control of reaching movements have typically focused on movements to a single target , and contemporary models of reaching behavior have emphasized the trade-off between accuracy and effort [1–3 , 5 , 24] . A number of studies have recently examined the interplay between motor control and the decisions about where and when to reach during single movements . For example , it is has been shown that people factor into account their spatial movement variability to optimize performance when reaching towards target configurations with different reward and penalty regions [29–31] and similar optimization has been shown for temporal variability [19 , 32 , 33] . Recent work has investigated decision-making processes associated with selecting a single target from among multiple potential targets [15 , for reviews , see 34–37] . To capture choice behavior in such tasks , additional factors need to be considered , including target value and biomechanical costs . However , in many real world tasks people must make a series of choices , each involve multiple potential targets . In this situation , captured by the task we examined , potential costs associated with planning ahead need to be considered . In our task , we found that target value and distance influenced choice behavior in both the hand and eye tasks , with the relative influence of distance being stronger for the hand . Target distance is clearly related to biomechanical costs , especially in the hand task , but may also be linked to temporal costs associated with target search and selection . Although target size did not strongly affect movement time in either task , it nevertheless influenced choice in the hand task . Recently , it has been suggested that participants can improve their accuracy without incurring additional time by increasing the level of control [25] . In other words the trade-off between speed and accuracy can be altered through the cost of control . We suggest that participants’ preference for larger targets in the hand task may partially reflect the greater cost of control involved in attaining small targets . The trade-off between value and biomechanical costs in the hand task agrees with recent work by Cos and colleagues [38–40] showing that people can rapidly predict biomechanical costs associated with competing reaching actions [see also 41 , 42] . Our probabilistic model suggests that participants in the hand task took into account a greater number of future harvests in comparison to participants in the eye task . Such ‘looking ahead’ in the hand task presumably enabled participants to trade off more effectively biomechanical and time costs with the value of the targets harvested . The ability to look ahead has been shown for a task in which participants reached through a series of fixed via-points which had to be visited in a prescribed order [43] . Our results also show that , in both the hand and eye tasks , participants placed the most weight on the current target option with each subsequent target option having progressively less weight in the decision . This finding can be linked to the phenomenon of ‘temporal discounting’ of reward which has been shown to influence the kinematics of single saccadic eye movements , with more rewarding targets leading to faster movements [44–48] . Our study shows that such temporal discounting acts across a sequence of future movements of both the eye and hand . To our knowledge , our study is the first to quantitatively assess how costs and rewards associated with future targets influence current target choice during sequential target acquisition with either the hand or the eye . Thus , the results pertaining to each task , in isolation , are in themselves novel . A priori , it was not obvious that in the eye task participants would only consider the next target when selecting targets . By ‘looking ahead’ ( i . e . , considering multiple future targets , viewed in peripheral vision , while fixating the current target in order to harvest it ) , participants could have reduced , on average , the amplitude and therefore duration of each eye movement . It is also possible that they could have reduced the average distance between the remaining targets and the current fixation point , which may have facilitated search . On the other hand , it strikes us as impressive that , in the hand task , participants consider a number of future targets given the speeded nature of the task . That is , given the short harvesting window , it was not obvious , a priori , that participants would invest resources in looking ahead . In terms of the comparison between the eye and hand tasks , we acknowledge that there are several differences between the tasks beyond the effector involved . Whereas target size directly determined the required accuracy of hand movements , this was not the case for eye movements . Given that the functional fovea is approximately 3° [49] , it was simply not possible to implement similar accuracy constraints in the two tasks . We did not record eye movements in the hand task because it was difficult to obtain accurate recordings while participant generated very vigorous arm movements . However , based on previous work ( as well as our observations ) , we can be quite certain that participants launched eye and hand movements towards each target in synchrony and maintained fixation at the target until the hand cursor arrived and the button was clicked [49–55] . Thus , the overall pattern of eye movements in the two tasks would have been similar . In both tasks , gaze shifts from one harvested target to the next and information about targets remaining to be harvested is provided by peripheral vision . Of course , the function of gaze differs between the two tasks . Whereas visual feedback is presumably used to help keep gaze on target in both tasks , in the hand task retinal and extraretinal signals are also used to guide the hand . Given the additional functions played by gaze in the hand task , it seems unlikely that gaze demands account for the reduced look-ahead behaviour observed in the eye task . However , we would emphasize that our goal was not to perfectly equate the two tasks , which are necessarily somewhat different . Rather , we wanted to keep each task as natural as possible , within the overall timing constraints , so that we could broadly compare the behaviours . We believe that the striking difference between the two tasks , in terms of look ahead behaviour , is likely quite robust and does not depend on subtle details of the two tasks . Activity in sensorimotor areas of the brain has been shown to encode multiple potential reach targets prior to deciding between , and then reaching towards , one of these targets [56] . One interpretation of this activity is that it reflects competing movement plans prepared for multiple potential targets ( Cisek and Kalaska , 2010 ) , an idea consistent with recent behavioral studies showing spatial averaging when reaching towards multiple potential reach targets [57–62] . The formation of multiple motor plans may be an effective way of evaluating the costs associated with these alternatives [40] . In the current task , it is an open question whether participants may prepare competing immediate motor plans ( i . e . , from the current target to different potential targets ) , as well as plans for future actions . Although numerous studies have examined how rewards and costs associated with individual targets are neurally represented [63–65] , our results indicate that the brain must also represent , and evaluate , alternative routes involving multiple targets , potentially in parallel . A number of studies have identified brain regions that may play a role representing and planning such routes . Recent work on navigational planning in an open area has shown that , prior to goal-directed navigation , the rat hippocampus generates sequences of neural events encoding spatial trajectories from the current location to the known goal location [66] . These events may support a goal-directed , trajectory-finding mechanism that identifies important places and relevant behavioural paths and can be used to control future navigational behaviour . This , or a similar , mechanism may support the optimization of sequential reaching movements to objects is reachable space . In behavioural studies , route optimization has been studied using versions of the travelling salesperson problem in which participants attempt to select the shortest path when connecting a set of fixed targets [67] . This work , which has focused on the heuristics used to solve the problem , has shown that , when given ample time , humans are capable of very good performance levels , often with near optimal solutions [10–13] . Our results indicate that even under speeded conditions , participants are able to perform extremely well . Although there are obviously many differences between natural foraging and the reaching task we examined , foraging theory [20 , 21] can nevertheless provide a framework for studying movement decisions in the context of competing costs and rewards [see 68] . In general , a key component of natural foraging involves deciding whether to engage with options as they are sequentially encountered . For example , work on patch foraging examines whether to remain in the current patch ( i . e . to exploit ) or switch to a new patch ( i . e . to explore ) which can incur a cost of time or effort [69 , 70] . In the most general setting there is both uncertainty as to the value of the current patch , which can vary with time as resources are depleted , and the value of other patches that could be visited . In such general settings it has been suggested that distinct neural processes appear to be engaged in choices within a patch and choices to move to forage a new patch [68 , 70] , revealing aspects of the exploration-exploitation trade-off [71 , 72] . In our task , the main pressure is time within a patch and the assessment of target value , size and distance must occur rapidly leading to some uncertainty . Therefore our task examines the efficiency of foraging within a patch to extract valuable resources and the potential trade-off between looking far ahead ( which can incur a time cost ) and myopic decisions that can be faster but less efficient .
Eight women and seven men between 20 and 28 years of age participated in the present study after providing written , informed consent . All participants self-reported having normal or corrected-to-normal vision , being right handed , and being free of sensorimotor dysfunction . Participants were randomly assigned to one of two groups: the hand foraging group ( n = 8 ) or the eye foraging group ( n = 7 ) . The experimental protocols were approved by the General Research Ethics Board at Queen’s University in compliance with the Canadian Tri-Council Policy on Ethical Conduct for Research Involving humans . Each experimental session lasted approximately one hour . Participants received $10 in compensation and an additional monetary sum based on the total points harvested during the experiment . Specifically , a 5¢ bonus was provided for every 250 points harvested , resulting in an additional payoff of between $4 and $5 . Seated participants viewed 15 circular targets located in 3 rows by 5 columns ( see examples in Fig 1 ) . To specify the locations of the targets on each trial , we began with a 3 x 5 grid with equal spacing of 6 cm in both dimensions , providing 15 initial grid locations . A set of 15 target locations was generated from these 15 grid locations by adding random vertical and horizontal offsets to each grid location , independently drawn from a uniform distribution over ± 11 mm . Thus , the positions of the targets , relative to the grid locations , changed from trial to trial . Targets in the display could be one of three sizes—5 , 8 , or 11 mm in radius—and have one of three point values—10 , 12 , or 15 points ( Fig 1B ) . Participants also viewed a circular start position , 5 mm in diameter , located 6 cm closer to their body than the first row of targets and at the participant’s midline . All stimuli were presented using a visual display system , consisting of a CRT projector ( Electrochrome 9500 Ultra ) with a refresh rate of 120 Hz and a horizontal mirror through which participants viewed the images on a horizontal surface . Note that participants could not see their hand or arm . In the hand foraging task , participants selected targets by moving a circular cursor ( 5 mm in diameter ) linked to the position of a handle , grasped with the right hand , to each target . A successful harvest occurred when participants pressed a button fitted to the side of the handle with their index finger when the cursor overlapped with any portion of the target . The handle , which was attached to a lightweight manipulandum ( Phantom 3 . 0 , Sensable Technologies , Cambridge , MA ) , could freely rotate about its vertical axis and was mounted on an air sled allowing participants to move the handle by sliding over a horizontal surface . Optical encoders in the manipulandum measured the handle position at 1000 Hz and the state of the button was also recorded at 1000 Hz . In the eye foraging task , participants selected targets by fixating their gaze on the targets . An infrared video-based eye-tracker ( ETL 500 pupil/corneal tracking system , ISCAN , Burlington , MA , USA ) was used to record gaze position of the left eye in the plane of the target display at 240 Hz . A bite bar was used to help stabilize the head . Gaze position was calibrated to the plane of the target display [for details , see 54] at the beginning of the experiment and recalibrated if drift in the recorded gaze signal occurred . The spatial resolution of gaze in the horizontal plane of the hand was 0 . 36° visual angle , corresponding to ~3 mm when gaze was directed to the center of the targets . In practice , gaze was recalibrated approximately once per participant , typically at around 75% of the duration of the experiment . The gaze signal was smoothed , on-line , using a running average filter computed over the last 50 samples ( oversampled at 1000 Hz ) , which introduced a small time delay of 24 . 5 ms . In performing the gaze foraging task , participants almost always fixated one of the targets . That is , participants shifted their gaze directly from one target to the next . To determine which target was fixated , we simply took the closest target to the gaze position . A successful target selection was achieved when gaze was at a given target for 150 ms . Fixations less than 150 ms were rarely observed , as might be expected if participants ( 1 ) made corrective saccades after fixating a target they did not want to harvest or ( 2 ) briefly fixated targets to explore the scene . With the required fixation duration , the time between successive harvests was similar to the hand foraging tasks . Note that given that the size of the functional fovea , which is about 3° [49] or ~2 . 5 cm in the target plane in our task , it would not have been appropriate to require participants to align the recorded gaze position inside the targets in order to harvest them . Note that we did not record eye movements in the hand task because participants tended to make rapid and vigorous arm movements in this task which make obtaining stable gaze recordings difficult . For a given trial , participants were instructed to harvest as many points as possible ( which translated to a monetary bonus ) . At the start of each trial , the participant had to position the cursor , or fixate , the start position for a random time period of between 0 . 3 and 2 . 3 s . The target display then appeared , and the participant then had a fixed duration of 3 . 25 s to harvest targets . This duration was chosen so that on average subjects could average about half the targets on any given trial . At the moment a target was successfully harvested , the target turned grey and a brief tone ( 1000 Hz ) was sounded for 50 ms . At the end of each trial , the total number of harvested points was presented on the target display . Participants completed one practice block of trials , consisting of a set of targets that were all of medium size ( 8 mm radius ) and value ( 12 points ) . Participants then completed four experimental blocks of 50 trials , in counterbalanced order , with a 3–5 minute rest between blocks . The four experimental blocks differed in how target size and value were combined ( see Fig 1B ) . In the size condition , only target size was varied , with 5 targets of each size , while target value ( 12 points ) was held constant . In the value condition , only target value was varied , with 5 targets of each value , while target size ( 8 mm ) was held constant . For this condition , the low , medium , and high value targets were colored blue , green , and orange , respectively ( in all other conditions , all of the targets were blue ) . In the small-high condition , the target size was negatively correlated with target value with the smallest targets being the most valuable . Participants were presented with 5 small targets of high value , 5 medium-sized targets of medium value , and 5 large targets of low value . Lastly , in the small-low condition , target size was positively correlated with value with the larger targets being the most valuable . Participants were presented with 5 small targets of low value , 5 medium-sized targets of medium value , and 5 large targets of high value . Before each block , participants were explicitly told the size-value pairing of the upcoming block . These blocks were completed in both the hand and eye foraging tasks . Participants in the hand foraging group performed an additional constrained hand foraging task in which they were required to harvest targets in order of decreasing value . That is , participants were required to harvest all of the high value targets first , followed by the medium value targets , and then the low value targets . Participants could not harvest a target out of order; if a participant attempted to harvest a target out of sequence , no tone was sounded and the target did not change to gray . In this task , participants typically harvested between 6 and 7 targets and therefore we could assess their route-selection efficiency by comparing the actual route they selected through the first 5 ( high value ) targets to all possible 5-target routes ( n = 120 ) . In this task , participants performed three blocks of 50 trials , counterbalanced across participants , corresponding to the value , small-high , and small-low conditions described above . For each harvest , we determined the duration from the previous harvest ( i . e . , duration between successive successful button presses ) as well as the distance from the previous harvest , defined as the distance between successive target centers . In the hand foraging task , participants occasionally missed the target by pressing the button while the cursor was slightly off the target . Participants on average missed the target on 7% ( SE = 0 . 01% ) of harvest attempts in the free task and 8% ( SE = 0 . 01% ) in the constrained task . In the eye task , harvests were registered when gaze was closest to a given target for 150 ms and participants did not press a button . Thus , misses akin to those in the hand task did not occur . An alpha level of 0 . 05 was used for statistical tests and a Bonferroni correction was used for post hoc tests . | Many natural tasks involve a series of decisions about which target to acquire next , either with our gaze or hand . We examined the factors influencing such decisions using a task in which targets of varying value and size are sequentially acquired by eye or hand movements . By developing a probabilistic model of decision-making behavior we show that eye movement decisions are made in isolation , independent of potential future targets , and are primarily determined by target value . In contrast , hand movement decisions consider multiple future targets and are strongly shaped by movement-related costs . By examining decision-making in sequential actions , our results and model represent a significant advance over previous work that has focused primarily on decisions about single actions . | [
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] | 2017 | Rapid target foraging with reach or gaze: The hand looks further ahead than the eye |
Cell surface protein and lipid molecules are organized in various patterns: randomly , along gradients , or clustered when segregated into discrete micro- and nano-domains . Their distribution is tightly coupled to events such as polarization , endocytosis , and intracellular signaling , but challenging to quantify using traditional techniques . Here we present a novel approach to quantify the distribution of plasma membrane proteins and lipids . This approach describes spatial patterns in degrees of inhomogeneity and incorporates an intensity-based correction to analyze images with a wide range of resolutions; we have termed it Quantitative Analysis of the Spatial distributions in Images using Mosaic segmentation and Dual parameter Optimization in Histograms ( QuASIMoDOH ) . We tested its applicability using simulated microscopy images and images acquired by widefield microscopy , total internal reflection microscopy , structured illumination microscopy , and photoactivated localization microscopy . We validated QuASIMoDOH , successfully quantifying the distribution of protein and lipid molecules detected with several labeling techniques , in different cell model systems . We also used this method to characterize the reorganization of cell surface lipids in response to disrupted endosomal trafficking and to detect dynamic changes in the global and local organization of epidermal growth factor receptors across the cell surface . Our findings demonstrate that QuASIMoDOH can be used to assess protein and lipid patterns , quantifying distribution changes and spatial reorganization at the cell surface . An ImageJ/Fiji plugin of this analysis tool is provided .
The function of cell surface proteins and lipids is tightly coupled to their spatial organization [1–3] . Membrane constituents cluster in nano- and micro-domains originating from lipid affinity ( e . g . , lipid rafts ) [4] , protein-protein interactions ( e . g . , tetraspanin domains ) [5] , and constraints imposed by the cytoskeleton [6] . Plasma membrane organization is also inherently asymmetric in polarized cells , such as migrating cells [7 , 8] , epithelial cells [9] , and neurons [10 , 11] . The ability to detect this plasma membrane organization is crucial for unraveling the dependency of signaling events , and understanding membrane regulation in a disease-related context [12] . One approach to assess the distribution of plasma membrane molecules is to consider these as bi-dimensional point processes that can be analyzed by spatial statistics . Point processes can be classified as: ( I ) Homogeneous or random , characterized by a constant density of points; ( II ) Inhomogeneous , characterized by a non-constant density of points; ( III ) Regular , with points equally dispersed; and ( IV ) Clustered , where points are grouped [13] . For investigating the spatial organization and especially for studying clusters , Ripley’s K-function [14] and pair correlation ( PC ) function approaches have been established . These measure the number from neighbors within a certain distance of a protein to determine the amount of clustering [15 , 16] . Several modifications and extensions of Ripley’s K-function have been made , including a model-based Bayesian approach [17] , the extension to co-clustering [18] , and an adaptation to account for limited localization precision in single molecule localization microscopy [19] . The PC function has been applied to images acquired by Photoactivatable Localization Microscopy ( PALM ) to quantify the heterogeneity of protein distributions on the plasma membrane [20] . In addition , the Density-Based Spatial Clustering of Applications with Noise ( DBSCAN ) algorithm , a density based tool , was used to identify clusters of varying shape against a background in super-resolution microscopy [21] . Recently , the Ordering Points To Identify the Clustering Structure ( OPTICS ) algorithm was made available to the single molecule community to measure local density changes ( overcoming some of the limitations of DBSCAN ) [22 , 23] . Moreover , the Getis-Ord G statistic [24] has been used to quantify the degree of local protein clustering in super-resolution images [25] and compared to other methods including DBSCAN and PC analyses . A key limitation of the current toolset is the lack of assessment on both clustering and polarity , equally important from a biological point of view . While the ability to detect specific clusters is of great importance , Sengupta et al . [20] among others , have shown that molecules like glycosylphosphatidylinositol ( GPI ) -anchored proteins are organized into different populations of clusters as well as residing as single molecules . All these organizations should be accounted for when analyzing the overall distribution . Furthermore , current tools require knowledge of the precise localization of the proteins and are thus limited to super-resolution or electron microscopy images . Therefore , we aimed to create an analysis tool that investigates biomolecule distributions without upfront information about their organization , and that can be applied to a variety of microscopy methods , including super-resolution and widefield microscopy . The main principle of our approach is to investigate spatial patterns of proteins and lipids and to quantify any deviation from random towards clustered or polarized organization as a measure of increased inhomogeneity . We exploit a geometrical approach , called tessellation , to divide the image into tiles , and use the distribution of tile areas to characterize different patterns . The image analysis algorithm uses both the information of the neighbor relations of segmented objects and the intensity of the tiles in which they are confined . By using fluorescence intensity information for tile area correction , we have made the tool applicable to images acquired by a range of microscopy techniques . We have termed this analysis tool Quantitative Analysis of the Spatial-distributions in Images using Mosaic segmentation and Dual parameter Optimization in Histograms ( QuASIMoDOH ) . An ImageJ/Fiji [26] plugin for QuASIMoDOH analysis is available along with instructional documentation ( S1 File ) .
For QuASIMoDOH analysis we consider single fluorescent emitters distributed on the cell surface as a point process P in a bi-dimensional space . Considering a number N of individual points p of the process placed on the support S , the distribution pattern P is defined as: P=∑i=1Npi with pi ∈ S , ∀i∈{1 , 2 , 3…N} , ( 1 ) where P describes a homogenous , clustered , or inhomogeneous pattern . Considering the brightness f of the fluorescent emitters ( represented by p ) and assuming the emitters being equally bright , the distribution pattern can be defined as: P=∑i=1Npif with pi ∈ S , ∀ i ∈{1 , 2 , 3…N} . ( 2 ) When the point process P ( S1A Fig ) is imaged by an optical system , each point of position r , p ( r ) , will be diffracted by the Point Spread Function ( PSF ) . The resulting microscope image g ( r ) is typically approximated by the convolution ( ⊗ ) of the point p ( r ) with the PSF [27]: g ( r ) =p ( r ) ⊗ PSF ( r ) . ( 3 ) The result is a blurred image ( S1B Fig ) . Due to the band limitation of the PSF , points of the pattern P placed at a distance shorter than the band limit will not be resolved as single points . In the blurred image , the intensity of the pixels depicting the diffraction pattern is directly related to the number of points contributing to this diffracted pattern . Aiding the development of QuASIMoDOH , we generated in silico images of points ( shown as single pixels ) dispersed on a surface ( Fig 1A , S1 Text ) . Blur and noise were then added into the image ( Fig 1B ) to mimic the acquisition process of a fluorescence microscope ( see S1 Fig and S1 Text for detailed description ) . The individual steps of QuASIMoDOH analysis are depicted in Fig 1 and described in detail below: To verify the ability of QuASIMoDOH to detect different distributions , we generated in silico images of different point patterns ( see ‘Creation of discrete point pattern images’ in S1 Text and S3 Fig for a detailed description of the patterns ) . As described before , blur and noise were added to simulate the acquisition process of a fluorescence microscope ( S1 Text , S1 Fig ) . We simulated images from widefield ( WF ) and Total Internal Reflection Fluorescence ( TIRF ) microscopy ( resolution ~200 nm ) , Structured Illumination Microscopy ( SIM ) ( resolution ~100 nm ) , and PALM ( resolution ~20 nm ) . We decided to generate biologically relevant patterns , namely: random distributions ( Fig 2A ) ; random distributions of clusters with diameter d = 80 nm ( Fig 2B ) ; random distributions of clusters with diameter d = 240 nm ( Fig 2C ) ; polar distributions ( Fig 2D ) ; and polar distributions of clusters with diameter d = 240 nm ( Fig 2E ) for WF ( Fig 2F–2J ) and SIM images . For PALM , clusters with increasing sizes were simulated ( see S1 and S3 Figs and S1 Text ) . The correction factor used in the analysis of simulated images was determined by maximizing the accuracy of the analysis to give the correct number of known points . The accuracy is defined as the ratio between the number of areas , obtained by tile size correction , and the total number of points in the image ( see S1 Text and S4 Fig ) . When the tessellation ( Fig 2K–2O ) and intensity correction are applied to images of different point patterns , the result is a set of tile areas typical for each pattern , captured by the histograms of corrected tile areas ( Fig 2P–2T ) . The histograms display an increasingly steep peak for increasingly inhomogeneous patterns , moving from random to polar clusters . Modeling the distribution of tile areas by the Inverse Gamma PDF allows for the discrimination between different point patterns . The two parameters of the function are specifically associated with the histogram fit shape and its broadness . These parameters ultimately vary based on the underlying point pattern . In particular , they decrease with an increase in the inhomogeneity of the distribution , as shown in Fig 2U where the shape and scale parameters are plotted together . The average ( and standard error of the mean , SEM ) from the analysis of 50 images for each point pattern is presented in Fig 2U . The analyzed images have the same density , ρ , defined as: ρ=NA/AreaImage , ( 10 ) where NA is the number of areas obtained upon tile size correction and AreaImage is the total area of the image in μm2 . Fig 2U shows the results from analyzing images with density ρ ~5 ( tiles/μm2 ) ; the images in Fig 2F–2J are examples of this density . Fig 2V shows a plot of the shape and scale parameters obtained by analyzing images with different patterns and densities ( 1 ≤ ρ ≤ 7 tiles/μm2 ) . The value of the shape parameter increases with increasing density , while the scale parameter decreases . Examples of images of density ρ ~1 and ρ ~7 ( tiles/μm2 ) are given in Fig 2W top and bottom , respectively . S5 Fig shows the densities associated with each point on the reference graphs for microscopy techniques with different resolution . The deviation from a random distribution ( calculated as the Euclidean distance from the random reference point ) is used as a measure of the inhomogeneity of a point pattern . This is quantified in Fig 2X . Here the inhomogeneity is calculated for images with ρ ~5 ( tiles/μm2 ) ( Fig 2U ) . To provide comparable results for each density , the interval between the two extremes ( random and polar clusters ) was normalized to one . Analysis of images similar to the one shown in S6A Fig can result in shape and scale parameters larger than shape and scale parameters for the corresponding random distribution reference ( S6B Fig ) . The inhomogeneity measure , in this case , will have a negative value ( S6C Fig ) since the coordinates of the random distribution are used as the origin from which the inhomogeneity is calculated . Shape and scale parameters 6–8 times larger than the random distribution reference can be further explained by a regular pattern where points are equally dispersed ( S6D–S6F Fig ) . Taken together , these results show that QuASIMoDOH can be used to quantify the inhomogeneity of spatial distributions and demonstrate the validity of the approach at the level of simulated images . We subsequently validated our method using images from fluorescence microscopy . Real and simulated images can exhibit analogous features , but they may differ as a consequence of microscope/detector sensitivity and dynamic range . Therefore , the correction factor C for actual microscopy images was selected to establish a comparable degree of correction as that determined by simulated results ( see S1 Text and S7 Fig ) . We took advantage of plasma membrane sheets ( S8 Fig ) [36 , 37] to acquire images of the cell surface without fluorescent background arising from the cytoplasm [38 , 39] . To control for the integrity of the plasma membrane , cells were initially incubated with the lipophilic fluorescent stains DiO or DiI , depending on the combination of subsequent protein/lipid staining . We tested whether QuASIMoDOH analysis was able to replicate findings made using microscopy techniques with different resolution and measured the inhomogeneity value from similar samples . We computed the cluster size of the lipid raft marker glycosylphosphatidylinositol ( GPI ) -anchored protein by comparing the results against the reference graph for simulated WF , SIM , and PALM images ( S5 Fig ) . Images of GPI-anchored protein tagged with green fluorescent protein ( GFP ) or photoactivatable GFP ( paGFP ) [40] , were acquired by WF , SIM , and PALM ( S9A–S9C Fig ) . QuASIMoDOH analysis of SIM and WF data gave comparable results ( S9D and S9E Fig ) . The analysis of 16 PALM images resulted in a GPI cluster size between 50 and 100 nm in diameter ( two regions were identified as random and one region with a low r2 was excluded ) ( S9F Fig ) . PC-PALM analysis of the same 16 images resulted in an average GPI cluster size of approximately 80 nm in diameter ( one region was identified as random ) . Obtained values for GPI cluster size were also consistent with previously published work [41 , 42] . Thus , QuASIMoDOH is capable of providing fast and quantitative analysis of super-resolution data with respect to cluster size . Finally , GPI inhomogeneity measurements from WF , SIM , and PALM data provided similar results when using appropriate tile area intensity corrections ( see S9G Fig ) , indicating the applicability of the approach across microscopy techniques with various resolutions . To further confirm that QuASIMoDOH detects different distributions we labeled three proteins that have well described and distinct spatial arrangements in Mouse Embryonic Fibroblasts ( MEFs ) using indirect immunostaining , and imaged these by WF microscopy . We used the sodium-potassium pump ( Na+/K+ ATPase ) [43] ( Fig 3A and 3B ) , the transferrin receptor ( TfR ) [44] ( Fig 3C and 3D ) , and caveolin1 ( Cav1 ) [45] ( Fig 3E and 3F ) . The Na+/K+ ATPase is an integral membrane protein that is randomly distributed across the cell surface . The TfR is an integral membrane protein responsible for ferric ion uptake , which clusters in clathrin-coated pits ( 200 nm in size ) after ligand binding and prior to endocytosis . Cav-1 is a cytosolic peripheral-membrane protein that functions , together with cavin , in the formation of caveolae . Cav-1 is predominantly localized to the trailing edge of migrating cells , and thus shows a polarized distribution . Analysis by QuASIMoDOH demonstrates that the different distribution patterns of immunolocalized Na+/K+ ATPase , TfR , and Cav-1 can be reliably distinguished based on their inhomogeneity ( Fig 3G and 3H ) . Comparing the results to simulated images indicates that the distribution pattern of Na+/K+ ATPase most closely resembles simulated images with a random distribution , whereas TfR matches those with a clustered distribution , and Cav1 aligns with simulated images highlighting a polar distribution of clusters . Thus , QuASIMoDOH effectively captures different protein distributions . Furthermore , these results demonstrate that our analysis can be successfully applied to images of proteins detected by immunolabeling . The cell surface is also patterned by the wide variety of lipid molecules present in the plasma membrane . We , therefore , explored whether QuASIMoDOH detects lipid distributions , including changes that result from perturbation of lipid trafficking ( Fig 4A–4D ) . For this purpose , we treated MEF cells with U18666A , an amphipathic steroid that causes cholesterol and sphingomyelin accumulation in late endosomes and lysosomes [46 , 47] ( 3 μg/mL for 18 h ) . The treatment was followed by fixation and incubation with Lysenin ( a toxin that specifically binds sphingomyelin in the membrane [48 , 49] ) and immunostaining . We reasoned that blocking endosomal sphingomyelin trafficking would deplete this lipid from the cell surface where it is normally organized into clusters , possibly causing reorganization to a more homogeneous distribution . Indeed , QuASIMoDOH analysis detects a significant difference in sphingomyelin organization between drug-treated and control cells ( t-test: p = 0 . 045; two-tailed; unpaired , Fig 4E and 4F ) . Thus , QuASIMoDOH analysis can also detect plasma membrane lipid distributions and how these are altered in response to cellular perturbations . To further explore QuASIMoDOH analysis in combination with different techniques we turned to TIRF imaging ( with widefield resolution ) . We monitored the internalization of the epidermal growth factor receptor ( EGFR ) [50] ( S10 Fig ) . We performed this in HeLa cells transiently transfected with EGFR-GFP , and treated with two concentrations of EGF reported to direct EGFR internalization distinctly [51 , 52]: a low EGF dose ( 2 ng/ml ) induces EGFR internalization via the clathrin-mediated route , while a high EGF dose ( 20 ng/ml ) directs EGFR internalization through both clathrin-coated pits and caveolae . TIRF images of cells fixed at specific time points ( t0 , before stimulation , t1 = 2 , t2 = 5 , t3 = 7 , t4 = 10 , and t5 = 15 minutes following EGF addition ) were acquired , and the organization of EGFR at the plasma membrane was analyzed by QuASIMoDOH ( Fig 5 ) . This revealed that EGF indeed altered the EGFR distribution ( Fig 5G–5J ) , corresponding to an increase in the measure of inhomogeneity . Furthermore , as predicted , this occurred more rapidly for cells exposed to the high concentration of EGF . These results demonstrate that QuASIMoDOH can be used to follow dynamic processes occurring at the cell surface and distinguish those processes with different rates . The EGFR internalization assay raises the question: Can different spatial distributions observed in the plasma membrane ( Fig 5E and 5F ) be analyzed on a local scale as opposed to the global scale described above ? To address this , we expanded QuASIMoDOH analysis to assess local distribution differences . For a local analysis , the tile area distribution analysis ( see above ‘5 . Tile area distribution analysis’ ) is applied to a subset of tiles in the images . This subset of tiles is selected by a circle that moves from the center of one tile to the next ( Fig 6A and 6B ) . A color is assigned to the tile in the center based on the distribution detected using the selected tiles . Magenta represents a random distribution , green represents a distribution in clusters ( for simplicity , the distinction between clusters of different sizes is omitted ) , and red represents polar distributions ( for simplicity , the distinction between polar and polar clusters is omitted ) . When switching to a local analysis , the fitting quality ( see S11 Fig ) can be affected . To limit incorrect distribution assignment , we set 0 . 45 as a minimum r2 and a cut off for the outliers that , despite a high enough r² , are too far away from any reference point ( distance > 1 , for widefield images ) , resulting in tiles without color . To test the local analysis , we first created in silico images where the upper part contained a random distribution and the lower part consisted of a clustered distribution ( see example in Fig 6A ) . These test images were simulations of WF images with a size of 512 x 512 pixels and 1600 points , on average . To maximize the number of tiles with detected distributions , the diameter of the circle must be in the range of 4 to 10 μm ( Fig 6C , S1 Text and S12 Fig ) . As expected , from the local analysis we obtained a random distribution in the upper third of the image , a clustered distribution in the lowest third , and a polarized distribution in the center due to the neighborhood abrupt change from one part of the image to the other in this transition region ( Fig 6D ) . We next applied the local analysis to a fixed cell image from our EGFR internalization assay ( treated for 10 min with high dose of EGF , Fig 6E ) . In Fig 6F , we applied the local analysis by setting the circle diameter in the range of 4 to 15 μm ( similar to the simulation , the maximum diameter is about half of the image ) . A clear clustered organization of the receptor becomes apparent at the periphery of the cell following the analysis . A random distribution , however , covers the center of the cell and a gradient of random and clustered receptors mark the transition between these regions . Finally , an image of EGFR on the surface of a live cell , stimulated by 20 ng/mL of EGF , was used to further investigate the successful application of this local analysis approach . We were able to observe the local variation of receptor distributions following the time dependence of stimulation ( see S1 Movie ) . These results indicate that QuASIMoDOH can be used to assess both the global and local changes in the distribution of fluorescent patterns at the plasma membrane .
Here we present QuASIMoDOH as a new approach to measure the inhomogeneity of a spatial distribution as a deviation from random towards clustered and polarized patterns ( S13 Fig ) . Different from methods such as PC-PALM [20] , Ripley’s K-function [14] , nearest neighbor approaches [13] , and DBSCAN [22 , 23] , QuASIMoDOH is compatible with polarized distributions ( see Caveolin-1 in Fig 3 ) . It can detect and measure polarized distributions independent from their orientation , while other tools must acquire information on cell morphology or other features to assess the spatial phenotype of polarized molecules [53] . Compared to grid-based algorithms , like the Hoshen-Kopelman algorithm [54] that divides space into a grid and identifies clusters as continuously occupied areas , QuASIMoDOH can correct for unresolved points by complementing the tile area dataset with the information on tile intensity and is thus applicable beyond single molecule imaging techniques . Where SpIDA [55] measures protein interactions and aggregation by multiple fitting of pixel intensity histograms , QuASIMoDOH analyzes the fit from tile intensity histograms to extract a measure of plasma membrane inhomogeneity . Comparable to Number and Brightness ( N&B ) approaches [31] , which analyze temporal fluctuations , in QuASIMoDOH the tile intensity IT depends on the number of molecules ( p ) in the tile and their brightness ( f ) : IT = pf . After calibration , incorporating background intensity , N&B can be mapped to absolute values with good spatial resolution . For ease of use in QuASIMoDOH , we decided to estimate the correction factor from the image by an analysis of the distribution of tile intensities . Determining the correction factor , however , leaves the possibility of misinterpretation due to background , non-uniform illumination , or the presence of artifacts . Pre-processing steps to address these conditions are provided in the QuASIMoDOH documentation ( S1 File ) . S14 Fig offers information on how to determine if an image is suitable for QuASIMoDOH analysis . We applied QuASIMoDOH to PALM , SIM , WF , and TIRF images ( Figs 3–5 , S9 Fig ) . However , in principle , this tool can also be applied to other microscopy modalities , including confocal microscopy and ( d ) STORM imaging . We have demonstrated that results obtained for the cluster analysis of GPI-anchored proteins are in reasonable agreement across microscopy techniques with different resolution and with previously published results obtained by PC-PALM analysis [42] . Additionally , QuASIMoDOH correctly detected the differential distribution of Na+/K+ ATPase , TfR , and Cav-1 proteins . Notably , QuASIMoDOH does not require the presence of a reference molecule in the sample with a known distribution . As a result , comparisons between protein/lipid organization in different cell types or within the same cell and under different experimental conditions are possible , e . g . , evaluating changes in a lipid distribution following drug treatment . We specifically demonstrated this by analyzing the distribution of sphingomyelin ( Fig 4 ) . The observed increase in homogeneity for the lipid probe Lysenin in cells treated with U18666A compared to untreated cells emphasizes the potential of our analysis tool to readily test a range of cell perturbations . Additionally , the fact that affinity probes , fusion proteins , and lipid probes can be detected highlights the versatility of our analysis method . We further demonstrated the ability of this approach to reveal the dynamic nature of EGFR organization at the plasma membrane upon stimulation , using both fixed and live cells . A list of advantages and limitations of QuASIMoDOH is provided in Table 1 . An important feature of QuASIMoDOH analysis is its direct application to microscopy data with no detailed prior knowledge of the sample . Overall QuASIMoDOH serves as a quick , straightforward , and automatable method to measure distribution patterns of proteins and lipids on the cell surface . It can be used to study events at the cell surface related to cell signaling or remodeling as these appear , for example , in the reprogramming of cancer cells or neuronal differentiation .
For QuASIMoDOH development and testing , a Dell Optiplex7010 computer was used ( Intel CITM ) i7-3770 CPU @ 3 . 40GHz processor and 4 . 00 GB RAM , running Windows 7 Professional ) . Images were run in batch mode , each 256 x 256 pixels in size . In total , approximately 25 , 000 points were identified , and using a standard four year old personal computer , we were able to analyze the entire dataset of 50 images in 22 seconds . Pixel-by-pixel image processing of both simulated and fluorescent images was carried out using custom-made routines in MATLAB and ImageJ/Fiji [26] according to the schemes described in the main text . For plasma sheet samples , background correction was applied ( where necessary ) by subtracting a background value either using the Dip Image function “backgroundoffset” or manually . Background subtraction on TIRF images of intact cells was carried out using the ImageJ/Fiji function “Rolling Ball” [26] . The WF , SIM , and TIRF images are initially filtered by the ImageJ/Fiji filter ‘Sigma Filter Plus’ and then smoothed . For the separation of signal and background in our simulated images , we used the default threshold in ImageJ/Fiji [57] , which is based on Isodata . For the widefield images of Na+/K+ ATPase and Caveolin-1 , the threshold Li [58] was used . GPI , TfR , and Lysenin widefield images , as well as GPI SIM and EGFR TIRF images , were thresholded using Mean [59] . Apparently , the best-suited threshold for an image can depend on the imaging modality and was chosen upfront based on the obtained images ( see S1 Text ) . For skeletonization , the function implemented in ImageJ/Fiji version 1 . 47 was used . PALM super-resolution images were generated by analyzing datasets obtained from Peak Selector software ( Research Systems , Inc . ) . Analysis of paGFP-GPI images was performed on 16 square regions of 7–18 μm2 obtained from 8 cells , with an average of 75 ± 5 localized peaks/μm2 and average localization precision of 15 nm . PC-PALM analysis was performed similarly to the previously described method [20] . For QuASIMoDOH analysis , localized peaks were grouped using a group radius of 3 x maximum localization precision and a maximum dark time of 5 s using Peak Selector . The maximum dark time was obtained experimentally using sparse paGFP . Similar to the method previously reported by Annibale et al . [60] , a best fit of the observed fluorophore counts as a function of the dark time was used to determine the effective number of molecules present in the sample . Grouped peak coordinates ( in pixels ) were subsequently fed into ImageJ/Fiji for QuASIMoDOH analysis . Peaks were plotted following a conversion to nanometers by a user-defined pixel size . The image width and height was calculated from the coordinates as the difference between the maximum and minimum values of X and Y coordinates , respectively . The generated image was then scaled to a 2 . 5 nm pixel size , and subsequently analyzed as described before . All graphing and statistics were prepared using MATLAB , GraphPad Prism ( GraphPad , La Jolla , USA ) , and Excel ( Microsoft , Redmond , USA ) . For the analysis of distribution patterns of proteins and lipids , images of cells from three different preparations/coverslips were analyzed . Images were pooled for further QuASIMoDOH analysis . Mouse Embryonic Fibroblast ( MEF ) cells were maintained at 37°C and 5% CO2 in DMEM/F12 ( +L-glutamine + 15 mM HEPES ) supplemented with 10% of fetal bovine serum ( FBS ) . MDA-MB-468 cells were cultured in DMEM supplemented with 10% FBS . Transient transfection of MDA-MB-468 cells was performed using Jetprime ( PolyPlus , following manufacturer instructions ) with 2 μg of paGFP-GPI similarly as described in detail elsewhere [42] . Transient transfection of MDA-MB-468 cells was performed using FuGENE ( Promega , following manufacturer instructions ) with 2 μg of GFP-GPI . HeLa cells were grown in DMEM/F12 ( +L-glutamine + 15 mM HEPES ) supplemented with 10% FBS . One day after plating cells , transfection was performed using FuGENE6 with 0 . 5 μM of plasmid pGFP encoding for EGFR-GFP . Plasma membrane sheets were prepared as previously described [36] . In short , MEF cells were cultured in DMEM/F12 ( +L-glutamine + 15 mM HEPES ) supplemented with 10% of fetal bovine serum . Cells were grown on coverslips to approximately 60% confluence . At 4°C , cells were washed with PBS+/+ and subsequently with coating buffer ( 20 mM MES , 135 mM NaCl , 0 . 5 mM CaCl2 , 1 mM MgCl2 , pH5 . 5 ) . Next , they were incubated in coating buffer with 1% of silica beads for 30 min . They were rinsed with deionized water ( 10 min ) followed by three washing steps with PBS+/+ . To prepare plasma membrane sheets , shear force was applied to the coverslip using a syringe held at a 30° angle ( on the coverslip ) . As the upper surface of adherent cells is made rigid by the silica coating , shear forces break off membranes at the edges releasing all soluble contents and retaining only the basal plasma membrane adherent to the coverslip . These remaining sheets were then fixed ( 4% paraformaldehyde in PBS , 15–20 min at RT ) ( see schematic in S8 Fig ) . Fixed plasma membrane sheets were rinsed with PBS+/+ and blocked ( 2% FCS , 2% BSA , 0 . 2% gelatin , 5% goat serum in PBS-/- ) for 1 h . For immunofluorescence , the following antibodies were used: mab to murine Cav-1 ( BD Biosciences; San Jose , USA ) ( 1:200 ) ; mab against Transferrin receptor ( Zymed/Life Technologies ) ( 1:100 ) ; mab against Na+/K+ ATPase alpha ( Novus Biologicals , Littleton USA ) ( 1:100 ) . Alexa488/555/568 conjugated secondary antibodies were used ( Life Technologies ) ( 1:1000 ) . Lysenin ( 1:40 ) was purchased from Peptide Institute ( Osaka , Japan ) and immunolocalized using anti-Lysenin pab ( 1:100 ) followed by goat anti-rabbit Alexa-555 ( Life Technologies ) secondary antibody . U18666A was purchased from Sigma . DiI and DiO ( Life Technologies ) ( 1:100 ) were used to stain lipid bilayers . Staining was performed according to manufacturer recommendations . Twenty-four hours after transfection , HeLa cells were serum starved overnight . For the preparation of the fixed samples , after starvation , the cells were incubated at 37°C with either 2 ng/mL or 20 ng/mL EGF for different time intervals ( 2 , 5 , 7 , 10 , and 15 minutes ) , then fixed with 4% paraformaldehyde in PBS , for 15–20 minutes at room temperature . The fixed samples were then imaged by TIRF . Additionally , living cells were imaged by TIRF upon stimulation with 20 ng/mL of EGF ( S1 Movie ) . Widefield and structured illumination images were acquired with a structured illumination microscope ( Elyra S1 ( Carl Zeiss , Jena , Germany ) equipped with a 63x oil objective lens with a numerical aperture ( NA ) of 1 . 4 and an Andor iXon 885 EM-CCD camera ) . PALM imaging was performed using a Nikon Instruments Ti Eclipse inverted microscope with a 100x/1 . 49 NA TIRF objective ( Apo ) and a 488 nm laser ( Agilent , MLC-MBP-ND laser launch ) with an EM-CCD camera ( Andor Technology , iXon DU897-Ultra ) . The microscope was equipped with a Perfect Focus Motor to minimize axial drift over the duration of imaging . paGFP was simultaneously activated and excited with the 488 nm laser at an intensity set to 1 . 45–1 . 9 mW ( as measured at the optical fiber ) . Exposure time was set at 100 ms . Imaging was performed until paGFP was completely exhausted , typically after 20 , 000 frames . TetraSpeck beads ( Life Technologies ) were used as fiducial markers for drift-correction during image acquisition . TIRF images were acquired by a Nikon Ti Eclipse inverted microscope , equipped with a 100x/1 . 49 NA oil objective lens and a Hamamatsu Orca D2 camera , or Hamamatsu Orca Flash 4 Lite , for living cell imaging . Confocal images were acquired using the same microscope equipped with Nikon A1R using a 60x 1 . 4 NA oil objective . Co-localization analysis was carried out using the Fiji plugin JACoP [61] . | Plasma membrane organization is fundamental to cellular signaling , transport of molecules , and cell adhesion . To achieve this , plasma membrane proteins and lipids are spatially organized: they form clusters , aggregate in signaling platforms , distribute into gradients on polarized cells , or randomly distribute across the membrane . It is also clear that these organizations can be affected in various contexts . For example , in aging or neurodegenerative diseases , the composition of the plasma membrane is altered and , consequently , the protein and lipid distributions in the membrane fluctuate . In addition , cancer progression is characterized by changes in cellular polarity , lipid content , and the redistribution of cell surface receptors and adhesion molecules . Here we have developed a method to quantify such alterations that , unlike current tools , is compatible with diverse types of cellular organization , including polarity . Our tool can be employed to screen for changes in a straightforward manner and to elucidate distributions of cell surface components in different disciplines , ranging from neurobiology to cancer research . | [
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] | 2016 | Inhomogeneity Based Characterization of Distribution Patterns on the Plasma Membrane |
Zika virus ( ZIKV ) has extended its known geographic distribution to the New World and is now responsible for severe clinical complications in a subset of patients . While substantial genetic and vector susceptibility data exist for ZIKV , less is known for the closest related flavivirus , Spondweni virus ( SPONV ) . Both ZIKV and SPONV have been known to circulate in Africa since the mid-1900s , but neither has been genetically characterized by gene and compared in parallel . Furthermore , the susceptibility of peridomestic mosquito species incriminated or suspected in the transmission of ZIKV to SPONV was unknown . In this study , two geographically distinct strains of SPONV were genetically characterized and compared to nine genetically and geographically distinct ZIKV strains . Additionally , the susceptibility of both SPONV strains was determined in three mosquito species . The open reading frame ( ORF ) of the SPONV 1952 Nigerian Chuku strain , exhibited a nucleotide and amino acid identity of 97 . 8% and 99 . 2% , respectively , when compared to the SPONV 1954 prototype South African SA Ar 94 strain . The ORF of the SPONV Chuku strain exhibited a nucleotide and amino acid identity that ranged from 68 . 3% to 69 . 0% and 74 . 6% to 75 . 0% , respectively , when compared to nine geographically and genetically distinct strains of ZIKV . The ORF of the nine African and Asian lineage ZIKV strains exhibited limited nucleotide divergence . Aedes aegypti , Ae . albopictus and Culex quinquefasciatus susceptibility and dissemination was low or non-existent following artificial infectious blood feeding of moderate doses of both SPONV strains . SPONV and ZIKV nucleotide and amino acid divergence coupled with differences in geographic distribution , ecology and vector species support previous reports that these viruses are separate species . Furthermore , the low degree of SPONV infection or dissemination in Ae . albopictus , Ae . aegypti and Cx . quinquefasciatus following exposure to two geographically and genetically distinct virus strains suggest a low potential for these species to serve as vectors .
The Spondweni serogroup , genus Flavivirus ( Flaviviridae ) , includes two species–Zika virus ( ZIKV ) and Spondweni virus ( SPONV ) [1] . Both ZIKV and SPONV are associated with human illness [2 , 3] . SPONV can cause a self-limiting febrile illness characterized by headache , myalgia , nausea and arthralgia [3–7]; signs and symptoms similar to most reported symptomatic ZIKV infections [8–16] , making diagnosis challenging in those regions of Africa with virus co-circulation . Although SPONV is not typically associated with serious disease , a subset of patients report signs and symptoms suggestive of vascular leakage and/or neurological involvement [3] . In 1952 , the Chuku strain of SPONV was isolated from the blood of a febrile patient in Nigeria [6] . This strain was initially misclassified as ZIKV [3 , 17] , leading to the 1955 South African SA Ar 94 Mansonia uniformis mosquito isolate being classified as the prototype SPONV strain [18] . Since its initial isolation , SPONV activity has been reported throughout sub-Saharan Africa ( Table 1 ) . In nature , the maintenance cycle is unclear [2 , 17 , 19] , but may be similar to the non-human primate/mosquito cycle that is utilized by ZIKV [11 , 13] . Although SPONV has been isolated from several mosquito genera ( Table 1 ) , the vast majority of isolations have been made in the sylvatic mosquito , Aedes circumluteolus [7 , 20 , 21] . Like other flaviviruses SPONV has a positive-sense single stranded RNA genome of approximately 11 kilobases in length [22] . The genome contains 5′ and 3′ untranslated regions flanking a single open reading frame ( ORF ) that encodes a polyprotein that is cleaved into three structural proteins: the capsid ( C ) , premembrane/membrane ( prM ) , and envelope ( E ) , and seven non-structural proteins ( NS1 , NS2A , NS2B , NS3 , NS4A , 2K , NS4B , and NS5 ) [22] . Herein , we genetically characterize two SPONV strains and investigate their potential for urban emergence as seen with ZIKV , as well as with other flaviviruses , including yellow fever and dengue viruses [23 , 24] . We determined the genetic relationship between the Nigerian Chuku and South African SA Ar 94 strains of SPONV and compared their sequence data to nine geographically and genetically distinct strains of ZIKV . We also determined the susceptibility of Ae . aegypti , Ae . albopictus and Culex quinquefasciatus mosquitoes to both strains of SPONV–mosquito species incriminated or suspected in transmitting ZIKV [25–28] .
Virus strains were obtained from the World Reference Center of Emerging Viruses and Arboviruses Collection at the University of Texas Medical Branch in Galveston , Texas . Both the Nigerian Chuku and South African SA Ar 94 strains prior passage histories were unknown and therefore these viruses could exhibit passage-associated mutations . Both viruses were passaged once in Ae . albopictus cells ( C6/36; ATCC #CCL-1660 ) for sequencing , and subsequently passaged once in African green monkey kidney cells ( Vero; ATCC #CCL-81 ) for vector susceptibility experiments . Vero cells were maintained at 37°C in a total volume of 20 ml of media containing Dulbecco's Modified Eagle Medium ( DMEM ) ( Gibco , Carlsbad , CA , USA ) supplemented with 2% ( vol/vol ) fetal bovine serum ( FBS ) , 100 U/ml of penicillin , 100 μg/mL of streptomycin , and 0 . 5 mg/ml amphotericin B ( Sigma-Aldrich , St . Louis , MO , USA ) . C6/36 cells were maintained at 29°C in culture media that was also supplemented with 0 . 1 mM non-essential amino acids , 1 . 0 mM sodium pyruvate , and 1% tryptose phosphate broth ( vol/vol ) ( Sigma-Aldrich , St . Louis , MO , USA ) . Following virus harvest , virus stocks were aliquoted and frozen at -80°C . Viral RNA was extracted from cell culture supernatant using the QIAamp Viral RNA Kit ( Qiagen , Valencia , CA , USA ) . Overlapping primer pairs ( S1 Table ) were used to amplify the entire open reading frame ( ORF ) using the Titan OneStep RT-PCR kit ( Roche , Mannheim , Germany ) and purified amplicons were directly sequenced using the Applied Biosystems BigDye Terminator version 3 . 1 Cycle Sequencing Kit ( Foster City , CA , USA ) and the Applied Biosystems 3100 Genetic Analyzer ( Foster City , CA , USA ) . Nucleotide sequences derived from both SPONV strains were assembled in Vector NTI Suite ( Invitrogen , Carlsbad , CA , USA ) , aligned in SeaView [29] using MUSCLE [30] , and edited in MacVector ( Apex , NC , USA ) . These consensus sequences were deposited in GenBank , SPONV Chuku strain accession no . KX227369 and SPONV SA Ar 94 strain accession no . KX227370 . ZIKV strains currently fall into either the African or Asian lineages [11 , 15]; as such nine geographically and genetically distinct sequences ( i . e . strains ) were used as representative members of these lineages for nucleotide and amino acid comparisons with both SPONV strains . The selected strains were isolated in West Africa ( n = 1 ) , Central Africa ( n = 1 ) , East Africa ( n = 1 ) , Southeast Asia ( n = 2 ) , the Pacific Islands ( n = 2 ) , and the New World ( n = 2 ) . These strains include the prototype strain MR 766 ( Uganda 1947 ) GenBank accession no . AY632535 [22]; ArB 13565 ( Central African Republic 1976 ) GenBank accession no . KF268948 . 1 [31]; ArD 41519 ( Senegal 1984 ) GenBank accession no . HQ234501 . 1 [11]; P6-740 ( Malaysia 1968 ) GenBank accession no . HQ234499 [11]; CPC-0740 ( Philippines 2010 ) GenBank accession no . KM851038 . 1; EC Yap ( Yap Island 2007 ) GenBank accession no . EU545988 . 1 [32]; H/FP/2013 ( French Polynesia 2013 ) GenBank accession no . KJ776791 . 1 [33]; Z1106033 ( Suriname 2015 ) GenBank accession no . KU312312 . 1 [34]; and PRVABC59 ( Puerto Rico 2015 ) GenBank accession no . KU501215 . 1 [35] . The MR 766 sequence used in these analyses exhibited a deletion in the potential glycosylation site that has been previously noted [11 , 22] . Three laboratory colonized , geographically distinct strains of both Ae . albopictus and Ae . aegypti , and one strain of Cx . quinquefasciatus were used to determine mosquito susceptibility to both SPONV strains ( Table 2 ) . Mosquitoes were reared and maintained during experiments using a 12:12 hour light/dark photoperiod in approximately 80% relative humidity at 28°C , and adult mosquitoes were provided a 10% sucrose solution via a cotton ball . Four- to seven-day-old female mosquitoes were sugar starved for 24 hours prior to infectious blood meal feeding , with Ae . albopictus and Cx . quinquefasciatus having access to deionized water up to 12 hours prior to feeding to reduce physiological stress . Mosquito infections were performed in an Arthropod Containment Level-3 ( ACL3 ) laboratory following the guidelines set forth under the Biosafety in Microbiological and Biomedical Laboratories ( BMBL ) 5th Edition Appendix E ( Arthropod Containment Guidelines ) . Groups of 100 mosquitoes were allowed to feed from artificial membrane feeders ( Discovery Workshops , Lancashire , UK ) covered by rat skins and containing a suspension of one part defibrinated sheep blood ( Colorado Serum Company , Denver , CO , USA ) and one part thawed virus . Virus titration was performed by plaque assay on infectious blood meals at 37°C using 90% confluent Vero cells in six-well plates with media containing Modified Eagle Medium ( MEM ) ( Gibco , Carlsbad , CA , USA ) supplemented with 10% FBS ( vol/vol ) , 100 μg/mL of penicillin , 100 μg/ml of streptomycin , 0 . 1 mM non-essential amino acids , and 2 mM glutamine ( Sigma-Aldrich , St . Louis , MO , USA ) . Serial 10-fold dilutions of infectious blood meals were inoculated onto the cell monolayer rinsed with phosphate-buffered saline ( Gibco , Carlsbad , CA , USA ) . Virus was allowed to absorb for 30 min at room temperature after which the monolayer was overlaid with 4 mL of a 1:1 solution of 2% agar–2× MEM . 96 hours after the first overlay , 2 mL of a 1:1 solution of 2% agar–2× MEM containing 2% neutral red ( Sigma-Aldrich , St . Louis , MO , USA ) was added to each well . Plaques were counted at 120 hours , and infectivity titers were expressed as PFU/mL . Blood meal titers were 5 . 1 ( Chuku strain ) and 5 . 3 ( SA Ar 94 strain ) log10 PFU/mL . Post feeding , mosquitoes were sorted on ice and individuals meeting the criteria for stages 4 to 5 engorgement were retained [36] . On day 14 post-feeding , individual mosquitoes were chilled to immobilize , then dissected and homogenized ( legs/wings and body separately ) in tubes containing a steel BB and 500μl of media [DMEM supplemented with 20% ( vol/vol ) FBS , 100 U/mL of penicillin , 100 μg/mL of streptomycin , and 0 . 5 mg/mL amphotericin B ( Sigma Aldrich , St . Louis , MO , USA ) ] and frozen at -80°C . Homogenate was assayed on C6/36 cells for the presence of SPONV antigen by an indirect fluorescent antibody ( IFA ) test using hyperimmune mouse ascitic fluid ( HMAF ) directed against the SPONV Chuku strain and a commercial fluorescein isothiocyanate-conjugated goat antimouse immunoglobulin G ( Sigma Aldrich , St . Louis , MO , USA ) [37 , 38] . For each mosquito species and virus strain , the Wilson-Brown method [39] implemented in GraphPad Prism 7 ( GraphPad Software , Inc , La Jolla , CA , USA ) was used to calculate 95% confidence intervals for the percentage of mosquitoes with detectable infection and percentage of mosquitoes with disseminated infection .
The ORF of SPONV Chuku and SA Ar 94 strains displayed >98% nucleotide and amino acid identity to each other , whereas they displayed ~68% and ~75% percent nucleotide and amino acid identity to ZIKV ( Fig 1 ) . Next we compared nucleotide and amino acid identity in the individual genes of SPONV and ZIKV . The lengths of individual genes were determined by utilizing putative cleavage sites of ZIKV genes . The individual SPONV gene sizes were similar to ZIKV genes: C prM , NS1 , NS4A , and NS5 were identical , whereas the E ( 505 vs . 504 amino acid ) , NS2A ( 226 vs . 217 amino acid ) , NS2B ( 130 vs . 122 amino acid ) , NS3 ( 619 vs . 617 amino acid ) and NS4B ( 255 vs . 251 amino acid ) were larger than ZIKV . The individual structural gene comparison of SPONV and ZIKV showed nucleotide and amino acid identity ranging from 61% to 68% and 64% to 72% , respectively , with the E gene displaying greater sequence identity ( 68% nucleotide and 72% amino acid ) ( S1 Fig ) . The nonstructural gene comparison displayed nucleotide and amino acid identity ranging from 59% to 73% and 58% to 82% , respectively . The NS4B and NS3 genes displayed the greater identity , 70% to 72% nucleotide and 81 to 82% amino acid ( S2 and S3 Figs ) . The NS2A gene was the most divergent gene with 59% to 60% nucleotide and 58% to 59% amino acid identity between SPONV and ZIKV ( S3 Fig ) . Exposure to the SPONV Chuku strain by artificial infectious blood meal ( 5 . 1 log10 PFU/mL ) did not result in any detectable infection or dissemination in any of the three mosquito species ( Table 2 ) . Exposure to the SPONV SA Ar 94 strain by artificial infectious blood meal ( 5 . 3 log10 PFU/mL ) resulted in detectable infection in 8 . 3% of Ae . aegypti ( Galveston ) and 12 . 5% Ae . aegypti ( Thailand ) , while only Ae . aegypti ( Galveston ) developed detectable disseminated infection ( 8 . 3% ) . Poor feeding rates ( stages 1 to 3 engorgement ) and high mortality experienced in Ae . aegypti ( Thailand ) and Ae . albopictus ( Venezuela ) mosquitoes exposed to the SPONV Chuku strain resulted in low numbers of experimentally exposed mosquitoes .
Prior to this study , only one SPONV strain had been sequenced , but its geographic origin and passage history were not reported [40] . Our analyses demonstrated that both SPONV strains sequenced in this study ( Chuku and SA Ar 94 ) are genetically similar , but exhibit a high degree of nucleotide and amino acid divergence when compared to ZIKV strains from West Africa , East Africa , Southeast Asia , the Pacific Islands and the New World ( Fig 1 ) . The similarity between the two SPONV strains isolated in different geographic regions approximately 2 . 5 years apart indicates the possibility of continuous enzootic transmission and maintenance between Nigeria and South Africa , although interpretation is limited due to the lack of spatial and temporally spaced sequences ( i . e . multiple isolates ) . ZIKV strains within each lineage , African and Asian , also exhibited a low degree of nucleotide divergence when compared to one another , as seen in previous work [11] . With the exception of the MR 766 ZIKV strain , neither SPONV strain nor any of the other eight ZIKV strains used in this study exhibited a deletion in the potential N-linked glycosylation site as reported in some ZIKV strains that had prior passage histories in mouse brains [11 , 22 , 31 , 41] . The susceptibility and dissemination to moderate doses of both SPONV strains in all three species was low or non-existent ( Table 2 ) . The Chuku strain caused no detectable infection or dissemination in any of species , while the SA Ar 94 strain was only observed to cause disseminated infection in Ae . albopictus Galveston ( 8 . 3% ) . Work by Bearcroft also failed to show transmission of the Chuku strain by Ae . aegypti [4] . Unlike SPONV , Ae . aegypti and Ae . albopictus have been incriminated as primary urban vectors of ZIKV [25–28 , 42–48] . Early work demonstrated that Ae . aegypti was a competent vector of ZIKV following feeding on an artificial infectious blood meal containing the MR 766 prototype strain , with three mosquitoes transmitting ZIKV to a single rhesus monkey 72 days post-exposure [27] . Since that time , numerous studies have shown that various geographically distinct strains of Ae . aegypti or Ae . albopictus mosquitoes exposed to ZIKV strains from either the African and Asian lineages exhibit a wide range of susceptibility and/or vector competence in these two mosquito species [25 , 26 , 28 , 43–48] . Although there have been reports that Cx . quinquefasciatus could be a potential vector of ZIKV [49 , 50] , multiple vector susceptibility and/or competence studies using laboratory or field strains of Cx . quinquefasciatus or Cx . pipiens indicate that many geographically distinct populations are refractory to virus transmission [46 , 47 , 51–54] . These results are similar to our findings in both SPONV strains , where infection and dissemination was not detected in Cx . quinquefasciatus . While there is little information on the potential sylvatic amplification and maintenance hosts of SPONV [2] , intensive field studies carried out in areas of high SPONV transmission were able to narrow down or exclude potential host species [7 , 18] . Virus isolations and antibodies were not detected in any rodent or bird collected in Ndumu , South Africa in 1958 [7] . These findings led the authors to speculate that it was unlikely these species were involved in the amplification and maintenance of the SPONV . Later experimental work supported this hypothesis , when six African rodent species in different genera failed to develop viremia following experimental inoculation with SPONV [55] . During the course of these early field studies antibodies were detected in domestic livestock [7 , 56] , however the ability of these species to develop viremia remains unknown . Similar to ZIKV , experimental work demonstrated that SPONV can infect non-human primates [18 , 57] . While little is known in regards to the species of non-human primates SPONV may infect in nature , considerable information exists for ZIKV . In Africa , ZIKV has been isolated and/or a serological response to prior infection has been observed in numerous non-human primate species including members of the genera Cercocebus , Cercopithecus , Colobus , and Erythrocebus [11 , 13 , 17] . Based on historic reports and its close genetic relationship with ZIKV , SPONV may be maintained and transmitted in a sylvatic cycle involving non-human primates and mosquitoes . Unlike ZIKV , which has a broad geographic distribution [11 , 13–15] , SPONV isolations and seroprevalence have thus far been confined to Africa ( Table 1 ) . While it is possible that the differences in the geographic distribution of ZIKV and SPONV are a result of prior infection either virus resulting in a refractory status among amplification hosts , another explanation is that different sylvatic vector species are involved in the transmission of these two viruses . To date , the majority of SPONV isolations have been made in Ae . circumluteolus mosquitoes collected in Southern Africa [7 , 20 , 21] , with experimental work demonstrating this species is capable of virus transmission up to 84 days following exposure to 7 . 1 log10 PFU/mL of virus [20 , 58] . Isolations made from other sylvatic mosquito species are considerably less common [7 , 18 , 20 , 21] , which may be a result of sampling bias [20] . In contrast , the commonly incriminated sylvatic vectors of ZIKV in sub-Saharan Africa are Ae . africanus , Ae . furcifer , Ae . opok , Ae . vittatus , and Ae . luteocephalus [11 , 13] . Mosquito collections and subsequent virus isolation attempts over a number of years by laboratories in sub-Saharan Africa yielded isolations of SPONV from eight species of mosquitoes in the genera Aedes , Culex , Eretmapodites , and Mansonia ( Table 1 ) , while ZIKV has been isolated in 20 species in the genera Aedes , Anopheles , Eretmapodites , and Mansonia [11] . Although many of these species are found in the same regions where both SPONV and ZIKV have been isolated , both viruses have only been isolated in two species , Ae . fowleri and Ma . uniformis . Ultimately , further studies are needed to determine the potential for sylvatic mosquito species to transmit both ZIKV and SPONV . Our study has some limitations . We only had access to two SPONV strains whose prior passage histories are obscure , as such , passage associated mutations could be present . Based on ZIKV non-human primate and human viremia data [59 , 60] , we choose a virus dose that would provide approximately 100 virus particles per 0 . 1 ul of infectious blood to experimental mosquitoes–what we concluded would constitute a moderate virus dose . As such , a higher virus dose may result in infection and dissemination in these species . While the presence of infectious virus was demonstrated by plaque titrating infectious blood meals , ( 5 . 1 log10 PFU/mL Chuku strain and 5 . 3 log10 PFU/mL SA Ar 94 strain ) , some studies have shown a decrease in infection and subsequent transmission among flaviviruses using infectious blood meals that have utilized freeze-thawed virus [61 , 62] . Finally , it is important to note that caution should be exercised regarding the over interpretation of the results of vector susceptibility/competence studies , as variation in virus strains and/or vector competence between geographically distinct mosquito populations has been reported in other arboviruses [63 , 64] . Previous to the Ninth Report of the International Committee on the Taxonomy of Viruses ( ICTV ) [65] , SPONV was considered a species of the genus Flavivirus , family Flaviviridae , and both SPONV and ZIKV were considered members of the Spondweni serogroup [2] . According to the current report , SPONV has now been categorized as a member of the genus Flavivirus that has not been approved as a species . SPONV clearly exhibits a greater nucleotide ( ~ 32% ) and amino acid ( ~25% ) divergence from ZIKV as has been previously reported ( Fig 1 ) [31] . This is particularly evident when comparing individual proteins rather than the entire ORF ( S1 , S2 and S3 Figs ) . Comprehensive historic work using neutralization , hemagglutination-inhibition , complement fixation and antibody absorption tests also differentiate SPONV and ZIKV as distinct viruses based on limited cross-reactivity [2 , 3 , 17 , 66 , 67] . Furthermore , both viruses exhibit differences in vector associations , ecology , and geographic distribution . These data suggest that although both SPONV and ZIKV are related , they are separate species . In conclusion , this study determined the genetic relationship between two SPONV strains , as well as their relationship to nine representative African and Asian lineage ZIKV strains . Aedes aegypti , Ae . albopictus and Cx . quinquefasciatus mosquitoes exhibited poor infection and virus dissemination rates following exposure to moderate oral infectious doses of both SPONV Chuku and SA Ar 94 strains , indicating a low potential for these species to serve as vectors . Based on these results , SPONV probably has limited potential for emergence into urban cycles that are characteristic of other flaviviruses such as Zika , yellow fever and dengue viruses . Nucleotide and amino acid divergence coupled with differences in geographic distribution , ecology and vector species support previous reports that SPONV and ZIKV are separate species . | Spondweni virus ( SPONV ) is a mosquito-transmitted flavivirus reported in Africa . Human infection with SPONV may result in a febrile illness similar to symptomatic Zika virus ( ZIKV ) infection , as well as many other tropical infections . Previously , little was known about the genetic relationships between SPONV and ZIKV . Additionally , the ability of SPONV to infect peridomestic mosquito species suspected or incriminated in the transmission of ZIKV was unknown . Both SPONV strains exhibited a high degree of nucleotide and amino acid identity to each other , but considerable nucleotide and amino acid divergence to ZIKV . The open reading frame ( ORF ) of the nine African and Asian lineage ZIKV strains originally isolated in West Africa , Central Africa , East Africa , Southeast Asia , the Pacific Islands and the New World all exhibited limited nucleotide divergence . Both strains of SPONV exhibited a low degree of infection and/or dissemination in Aedes albopictus , Ae . aegypti and Culex quinquefasciatus mosquitoes suggesting that these species have a low potential to serve as vectors . These results coupled with differences in geographic distribution , ecology and vector species indicate that SPONV and ZIKV are similar but separate species . | [
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] | 2016 | Genetic Characterization of Spondweni and Zika Viruses and Susceptibility of Geographically Distinct Strains of Aedes aegypti, Aedes albopictus and Culex quinquefasciatus (Diptera: Culicidae) to Spondweni Virus |
Motivated by viral persistence in HIV+ patients on long-term anti-retroviral treatment ( ART ) , we present a stochastic model of HIV viral dynamics in the blood stream . We consider the hypothesis that the residual viremia in patients on ART can be explained principally by the activation of cells latently infected by HIV before the initiation of ART and that viral blips ( clinically-observed short periods of detectable viral load ) represent large deviations from the mean . We model the system as a continuous-time , multi-type branching process . Deriving equations for the probability generating function we use a novel numerical approach to extract the probability distributions for latent reservoir sizes and viral loads . We find that latent reservoir extinction-time distributions underscore the importance of considering reservoir dynamics beyond simply the half-life . We calculate blip amplitudes and frequencies by computing complete viral load probability distributions , and study the duration of viral blips via direct numerical simulation . We find that our model qualitatively reproduces short small-amplitude blips detected in clinical studies of treated HIV infection . Stochastic models of this type provide insight into treatment-outcome variability that cannot be found from deterministic models .
There are many locations in the body from which viruses could re-emerge during drug treatment; for a review , see [6] . Here , we will focus on the important possibility that viruses may emerge from a reservoir of latently infected cells . Usually when HIV infects target cells ( such as CD4+ T cells and macrophages ) the result is rapid virus production and cell death . However , a fraction of infected cells are known to enter a state of latent infection [12] where virus has integrated into the host cell DNA , but there is little , if any , viral gene expression . While cells are in this state , they are unaffected by ART and viral cytopathicity , and are effectively invisible to the host immune response [13] . However , upon re-activation , latent cells begin the normal processes of viral replication and production , and become immune targets [14] . A large fraction of the latent reservoir consists of resting memory CD4+ cells [15] and therefore , reactivation could occur as part of the normal immune response to a secondary pathogen [16] . However , we do not completely understand the reasons for activation of latently infected cells and it is likely that there is a pathogen-independent component as well . Indeed , the mechanisms for latency are generally poorly understood; there are differing opinions , but no consensus to date [12] , [17]–[19] . The population of latently infected cells is established as early as 10 days after symptoms of seroconversion , within a few weeks of initial infection [14] . Estimates of reservoir size differ but consistently show that latently infected cells constitute only a small fraction of the total number of T-cells [12] , [20] . Unfortunately , and in spite of its small relative size , the decline of this population is slow and it is estimated that it can persist for up to 70 years [21] . This long lifetime probably arises from the intrinsic stability of resting memory CD4+ cells which is an important part of immune memory [22] . Recent evidence also indicates that latently infected cells can undergo cell division [15] , potentially increasing the lifetime of the reservoir . These factors , in combination with long lifetime of the reservoir , indicate that latently infected cells are an important factor that must be addressed in the search for therapies to eradicate HIV infection [23] . While on successful anti-retroviral treatment ( ART ) for HIV , an infected individual's viral load remains non-zero [3] , though it is very low and usually undetectable using standard assays that have a detection limit of 50 copies/mL in plasma . Occasionally , however , regular blood tests show viral blips: periods of detectable viral load , preceded and followed by undetectable loads . At one time there was a concern that blips might signal imminent drug failure [24] , including the emergence of new , drug-resistant variants of virus [25] . However , there is a substantial body of evidence dating from the early 2000s , indicating that most blips are not associated with virological failure [26]–[28] . With that said , a recent large-scale study of 3530 Canadian patients refined these results by showing a two-fold increase in the risk of drug failure following viral blips that exceeded 500 copies/mL , but importantly , smaller blips were not associated with drug failure [29] . In this study , blips were detected at a frequency of about 0 . 1/patient/year . This rate is compatible with data taken from the UK during 2006–2007 [30] and is lower than the rate estimated from earlier data [31] . The reduction in blip frequency over the last decade is likely a result of improved drug efficacy . The underlying cause of viral blips remains controversial . There is some evidence that immune activation , through secondary infection or vaccination , may be correlated with viral blips , [32] , [33] . However , there have been observations of blips not associated with clinical or demographic variables . In an intensive 90-day study of 10 patients , Nettles et al . found that blips were fairly common , smaller in amplitude ( mean 79 copies/mL ) and short in duration ( median less than 3 days ) , and that blip frequency was unrelated to illness , vaccination , or drug concentrations [34] . Finally , we must acknowledge that accurate detection of small-amplitude blips is bedevilled by assay variability and sensitivity [30] , [34] . There has been extensive modeling work done to characterize viral load and pathogen-immune system interaction in HIV-infected individuals . However , standard viral dynamics models do not capture residual viremia in treated patients and various modeling approaches have been applied . Residual viremia can be captured by adding a latent cell reservoir to the standard model . Perelson et al . ( 1997 ) proposed the first model that included latent cell activation , in order to better understand decay characteristics of HIV-1-infected compartments during combination therapy [35] . This model was expanded to include a varying decay rate in the latent reservoir , and bystander proliferation in the latent reservoir , with the finding that a constant long-term activation rate for the latent reservoir , maintained through cell division , could explain residual viremia in treated patients [36] . We will use these elements in the development of our model of the latent reservoir . Careful modelling of viral blips has been fruitful in analyzing different mechanisms of blip generation . The focus of previous models has been on blips associated with immune system activation due secondary infection or vaccination . One successful approach has been to consider short periods of sustained viral replication driven by stochastic activation of CD4+ and CD8+ T cells [37]–[39] . A further series of models including T cell expansion due to vaccination and secondary infection showed episodes of detectable viremia of long duration ( 2–3 months ) , with amplitudes in the range of several hundred copies/mL [40] , [41] . Viral blips can also result from production of virus following immune activation and clonal expansion of latently infected cells [33]; latent cell activation caused by sporadic immune activation has also been modeled as a source of viral blip generation . Indeed , antigen-induced latent cell activation has been modeled and shown to be a plausible source of viral blips [42] . Most recently , Rong and Perelson ( 2009 ) proposed a model with antigen-induced asymmetric activation and division of latently infected cells [43] . Blips produced by this model are of short duration , directly related to the length of stimulation , and of variable amplitude , consistent with observations . These models produce blips of larger ( 100 copies/mL ) amplitude , with variable durations , and frequency depending directly on user-controlled periods of immune system activation in the model . The base mechanism in these models of the production of blips is immune system activation . As noted above , there have been observations of small-amplitude blips not associated with clinical or demographic variables [34] . Such blips can be imagined as random biological or statistical variation around a mean undetectable viral load . In order to capture this kind of stochastic effect , continuous ( differential equation based ) models are inadequate . Here , we propose a continuous-time branching process model of within-host viral dynamics for a patient undergoing successful treatment . We use this formulation to derive probability distribution functions for viral load as a function of time and examine the contribution of varying latent cell activation and proliferation to viral load . Using this methodology we first consider extinction times for the latent reservoir and examine the role of limited ongoing viral replication in replenishing the reservoir . We then examine the hypothesis that stochastic activation of latently infected cells can maintain low-level plasma viremia and generate small intermittent viral blips . Finally , via Gillespie simulation of the branching process , we calculate viral blip durations .
We consider a simple model of latent cell reactivation , presented schematically in Figure 1 . Our model has three compartments: the number of latently infected cells , which can replicate at rate die at rate and activate at rate to become productively infected cells; the number of productively infected cells which die at rate ; and the number of virions , produced by productively infected cells at rate , which can die at a rate . We allow for infection of new cells at rate , of which a fraction become latently infected . The efficacy of ART is given by . We will assume that this efficacy is very high , and that therefore the number of uninfected T-cells remains approximately constant and equal to . Clinical findings on viral blips show differing amplitudes [34] , [44] , [45]; though the small-amplitude blips were shown to be unassociated with clinical variables [34] , it is possible that the larger-amplitude blips may be due to an immune response , increasing the activation rate for a period of time . We will initially restrict ourselves to constant activation rate , but we consider variable in a later section . The mean behaviour of the system shown in Figure 1 is given by the linear system of ordinary differential equations ( 1 ) where , and represent the mean numbers of latently infected cells , productively infected cells , and virions respectively . Our goal is to obtain probability distributions for the viral load and the size of the latent reservoir at time . We assume that the events in the model can be described by a multi-type continuous time branching process with the rates given in Figure 1 . Importantly , the model does not scale up and so any computations we perform must be over the total number of and in the patient . The transition probabilities for each process in the model are stationary in time . We therefore know that our desired probability distributions depend only on the time since the initial state , and considerthe conditional probability that there are latently infected cells at time , productively infected cells at time , and virions at time given that there were initially , and of each species respectively . Then , given a joint initial distribution on these species , we can compute the joint probability distribution on each of theseNote that , as the latent reservoir must be of finite size , and as . By considering each process in Figure 1 in turn , we can derive the backwards Chapman-Kolmogorov differential equation for [46]: ( 2 ) with initial condition ( is the Kronecker delta function ) . Multiplying through by and re-indexing yields an infinite-dimensional system of nonlinear ordinary differential equations for the conditional probability generating functionWe reduce the infinite dimensional system to a system of three equations by exploiting the branching property [46] , with initial conditions and . To our knowledge we cannot solve this nonlinear system analytically . Therefore to calculate , we solve numerically using a standard differential equation integrator . Once and are calculated we can compute the full probability generating function , accounting for the initial distributionsOur goal is to recover the probability distributions of latently infected cells , productively infected cells , and virions at times . These can be recovered from the probability generating function by taking derivatives . For example , the probability that there are virions at time is given byBecause the distributions do not scale , we must perform computations over the total number of virions . Given a mean viral load of 25 copies/ml ( henceforth abbreviated as 5 c/mL ) within 5L of total blood volume , we must compute 125000 derivatives to get ! Direct numerical differentiation would be difficult , so we exploit the Cauchy-Euler formula:where is a closed curve in complex space which contains , and is analytic on a simply connected domain containing . The probability generating function is a polynomial in , and and therefore satisfies the analyticity requirement . We want to evaluate integrals at so our contour must contain the origin and it is simplest to use the unit circle , , Thenwhere we have used the fact that , where indicates complex conjugate . By this method we can calculate our probabilities via straightforward and reliable numerical integration . The same approach can be used to compute joint probability distributions . We can also use this formulation to directly calculate cumulative probabilities . As , we can writeby interchanging the order of integration and summation . This final formula will be useful in calculating blip probabilities at a time , . To our knowledge this is a novel method for computing probability distributions from single- or multi-type continuous time branching processes . We thoroughly tested our method and its implementation; see Figure S1 for comparisons with Gillespie simulations . We also wish to calculate the distribution of times to extinction for the latent reservoir . We choose parameters so that the probability of extinction of the latent reservoir is 1 as . However , as clearing the latent reservoir is considered a major hurdle in clearing HIV , the distribution of times to this inevitable extinction is of interest . We obtain the cumulative probability of latent reservoir extinction directly from the probability generating function . Since Note that the marginal probability . We then find the probability distribution of extinction times by differentiating , If we assume that no newly infected cells become latently infected ( ) or that treatment is completely effective ( ) , we can obtain the extinction probability analytically . In this case , the latent cell dynamics decouples from the rest of the model and can be represented as a pure birth-and-death process with master equation ( 3 ) where is the probability that at time there are latently infected cells . This probability has the conditional probability generating functionwhere is the initial reservoir size . The cumulative distribution is and thus we obtain ( 4 ) Then given the initial distribution on the latent reservoir we have can compute the extinction probability . We use the analytic expression to compute latent reservoir extinction time distributions for or . Otherwise , we work numerically . The parameters used for simulation results presented below are given in Table 1 . In our simulations the parameters , and are chosen based on estimates from [36] and based on estimates from [47] . The decay rate of the latent reservoir is chosen so that its half-life is , as measured in patients exhibiting viral blips [21] . For , the death rate of productively infected cells , and the virion clearance rate we set to estimates from [48] ( ) and [49] ( ) , respectively . The in vivo viral production rate is not well established and therefore we will consider a range for this parameter . The fraction of new viral infections that result in latency is also hard to estimate , but given the small size of the latent reservoir , it is likely to be rather small . For simplicity , we choose a baseline value of . We choose the initial mean latent reservoir size consistent with the experimental estimates [21] , [50] . The activation rate and replication rate of latently infected cells remain unknown . We calculate values from the mean behaviour equations ( 1 ) , taking . Since the dynamics of the productively infected cells and virus are more rapid than those of the latent reservoir , we can make a quasi-steady approximation to find . Then for an initial latent reservoir size of , . To calculate , we choose the mean decay rate so that the half-life of the latent reservoir is . Thus we can set . The resulting ( ) values for each production rate and mean viral loads of or are given in Tables 2 and 3 respectively . As noted above , we must perform calculations over the entire blood volume , which we take to be . When presenting results below we report viral loads in copies per mL , as this is the standard measurement , but they are always obtained by re-scaling the axes for results over the entire blood volume . In order to correctly simulate viral blips and latent reservoir extinction in patients with established treated infection , we should carefully choose the initial joint distribution so that it is close to the ( moving ) equilibrium of the ongoing dynamics . Otherwise , transient effects will pollute our results . In the mean , the dynamics of the latent reservoir are very slow compared to those of the productively infected cells or virions . We therefore focus on getting the initial latent reservoir distribution correct since errors in the other two compartments will resolve themselves quickly . Indeed , for a constant latent reservoir size , and our parameters , the distributions on and converge to stationary distributions in less than a month ( results not shown ) . In order to calculate a reasonable initial latent reservoir distribution we isolate its dynamics and consider the marginal probability distribution only , as in equation ( 3 ) . We choose the marginal latent reservoir probability distribution at time such that the variance is maximized . We reason that transient dynamics on the latent reservoir are dominated by the spreading of the distribution about the decaying mean , and that at maximum variance the probabilities are sufficiently spread for our purposes . For birth-and-death processes maximum variance occurs at the half life . Therefore , in order to create the initial distribution on the latent reservoir , we solve ( 3 ) out to , starting with latently infected cells , where is the desired mean latent reservoir size . The resulting distributions for different parameter sets are shown in Figure 2 . Notice that results based on a virus production rate have larger standard deviation . This is because lower production rates are associated with higher activation rates ( cf . Tables 2 and 3 ) . The higher activation rate speeds the dynamics of the latent reservoir , increasing the spread of its probability distribution function . Finally , we combine the computed initial latent cell distribution with single initial numbers of productively infected cells and virus , to obtain the whole initial joint probability distribution:
The reservoir of latently infected cells is considered a major obstacle to clearing HIV infection [19] . Within our model , when the reservoir goes extinct , viral load quickly goes to zero , since ongoing viral replication is too small to sustain the virus population . We are therefore interested in examining the reservoir lifetime after the onset of ART . To do this , we extend our approach to find the probability of reservoir extinction over time . We examine the reservoir lifetime using baseline parameters ( ) , and then allowing for the possibility of latent reservoir replenishment ( ) . Furthermore , since anti-retroviral treatments have improved substantially over the last 15 years , we also examine how the reservoir lifetime behaves as drug efficacy improves ( ) . We now focus on the time evolution of viral load and the likelihood of small-amplitude viral blips . We interpret viral load above the threshold of detection of 50 c/mL as a viral blip . Note that unless otherwise specified , the following calculations and computations assume the fraction of newly infected cells that become latently infected is 0 . In Figures 6 and 7 , we plot full viral load distributions over time , assuming initial mean viral loads of 25 c/mL and 35 c/ml . As time advances , the mean viral load decreases as expected in all cases but the viral load distributions widen more significantly when is smaller ( e . g . distributions in Figure 6A are widest , those in Figure 6C are narrowest ) . This is because the lower values of are associated with higher values of and , and the resulting dynamics on the latent reservoir cause the latent reservoir size probability distribution ( not shown ) to be wider . As a consequence the associated viral load distributions are wider , and this also causes higher blip amplitudes . This effect is more clearly understood by examining the insets in Figures 6 and 7 , which represents a magnified view of the given probability distribution curves above the blip threshold ( 50 c/mL ) , using a log scale to more clearly distinguish the curves . We observe that viral blips occur with very small probability regardless of the production rate . The blip amplitudes vary between parameter sets but remain approximately within the range of blips unassociated with clinical variables shown in [34] , i . e . 50–100 c/ml . Over three years , the range of reasonably likely detectable viral loads decays slowly , but small blips remain possible throughout that time ( Figure 6 ) . In this section we consider the following question: given a blip , defined as a detectable viral load measurement , how long should we expect the viral load to remain above the threshold of detection ? This question is of clinical interest , since a repeat measurement following a measurable viral load should be performed after enough time that a second positive result might have clinical significance , such as suggesting drug failure . Different from the previous sections , all results in this section are computed via 10000 direct simulations of the branching process using the Gillespie algorithm , beginning with an initial “blip” condition . The initial conditions are chosen as follows: we set the latent reservoir size and viral load . Since dynamics on the viral load are so much faster than on the productively infected cells ( cf . Table 1 ) , we then use a quasi-steady approximation to set the initial number of productively infected cells .
Clinical results on latent reservoir decay ( e . g . [21] ) make predictions on latent reservoir lifetimes that are based on purely exponential decay . Our model results showed that , for the same mean decay rate , the time distribution - and the mean time to extinction - is sensitive to dynamics on the latent reservoir . Further , assuming some reservoir replenishment due to latency in newly infected cells , our model predicted only limited lifetime reduction associated with improving drug efficacy . Eradication of the latent reservoir is considered a major hurdle in eradicating HIV infection [19] , [23] , and these results demonstrate the importance of understanding the underlying dynamics on the latent reservoir: the reservoir half-life is only a small part of the equation . From a clinical point of view our results on the latent reservoir lifetime are quite depressing - in our model we essentially study perfectly drug-adherent patients , and even with perfect drugs , decades of drug treatment are needed to clear the latent cell reservoir . Our model predicts that drug treatments that increase the activation rate of latently infected cells should reduce the lifetime of the reservoir . This approach has been tried several times but so far without real success ( reviews in [1] , [53] ) . A candidate drug would need to work on the whole heterogeneous population of cells seeded during initial infection , and in particular on the longest-lived subpopulation . We would predict that early treatment with such a drug , along with aggressive ART , would be most likely to reduce the size of the latent reservoir . This is in line with current research in treating HIV infection earlier , to enhance survival on an individual level [54] and limit transmission on the population level [55] . Of concern with earlier treatment is the possibility of emergent drug resistance ( DR ) . In future work we plan to expand our stochastic model to examine the likelihood of different mechanisms of acquired DR in patients on treatment , such as mutation during ongoing viral replication and activation of a cell latently infected with a DR strain [56] . We also examined the evolution of viral load over time , finding that as time progresses , the viral load distributions become more asymmetric , with a long tail towards higher viral loads . This can be explained by viewing our model as an extended subcritical birth-and-death process . Such processes produce asymmetric distributions ( see Figures 6 and 7 ) . The asymmetry is more pronounced for smaller production rates ( vs ) , associated with larger activation rates , and for larger initial viral loads ( vs ) . When examining blip probability we found that our model predicts that these probabilities decay exponentially over time . This decay is more dramatic for larger production rates , associated with smaller activation ( ‘birth’ ) rates , and smaller initial mean viral loads . In only one case is the decay so slow ( with mean initial viral load ) that our model predictions are broadly consistent with previous observations that blip probabilities don't decay over time [45] . We also observed that blip probabilities show great sensitivity to model parameters , varying by orders of magnitude . Given sufficient high-quality data on blips ( which does not currently exist ) , these would be the ideal results to compare with data for the purpose of parameter fitting , in order to gain some insight into latent reservoir dynamics - into the activation rate , for example . With improving drug efficacy ( ) our model predicts a significant decrease in baseline viral load , down to , in accordance with recent viral load observations [10] , [11] . From this low baseline , we found blip probabilities smaller than we can calculate . However , we found that by increasing the activation rate ( roughly simulating immune system activation due to secondary infection ) viral loads exceeding the threshold of detection were attainable . Therefore , our model supports the hypothesis that , for patients adhering to modern ART , viral blips signal an underlying secondary condition . We examined the duration of viral blips through direct ( Gillespie ) simulation of the model . We sought to answer the question “Given a patient measurement of X c/mL , how long can we expect the viral load to remain detectable ? ” We found , unsurprisingly , that blips of larger initial amplitude have longer mean duration and larger standard deviation in duration . Perhaps more interestingly , we found very strong dependence on the production rate . Given an initial blip of amplitude 90 c/mL , doubling the production rate from to , and changing other parameters accordingly , more than triples the predicted mean blip duration ( for parameters as in Figure 9 ) . We also considered blip duration as a function of the latent reservoir size , anticipating longer durations for larger reservoir sizes , since associated with these is a higher quasi-steady mean viral load . Our expectations were confirmed by simulation results ( see Figure 11 ) . The sensitivity to production rate and associated parameters was also recovered . Repeat-blip measurements in patients are , predictably , rather rare , since blips are already quite unusual events . Across all the parameter sets we examined , we found that detectable viremia should be expected to vanish within 8–10 days at most . This result is in general agreement with previous reports [34] , [45] and indicates that repeat low-level detectable viremia within 8–10 days could be due to a statistical fluctuation rather than drug resistance or other pathology . Over the last 15 years , enormous numbers of differential-equation models have been generated to study different aspects of various viral infections . We believe that stochastic models of the kind described here have an important role to play in certain situations where viral or cell populations are small enough that random effects still play a role . The obvious settings are during the first few days of any new infection ( see also [57] and , very recently , [58] ) , during drug treatment of a chronic infection , and during the extinction phase of an acute infection . One issue with stochastic modeling of rare events ( such as viral blips in our model ) is that simulation-driven studies can require enormous numbers of simulations to reliably sample the rare events . The method we describe here is an alternative to simulation ( or methods to capture rare-events ) and provides a direct and relatively straightforward way to calculate probability distribution functions . We hope to adapt this method to other situations in viral dynamics in future work . | While on successful drug treatment , routine testing does not usually detect virus in the blood of an HIV patient . However , more sensitive techniques can detect extremely low levels of virus . Occasionally , routine blood tests show “viral blips”: short periods of elevated , detectable viral load . We explore the hypothesis that residual low-level viral load can be largely explained by re-activation of cells that were infected before the initiation of treatment , and that viral blips can be viewed as occasional statistical events . To do this , we propose a mathematical model of latently-infected cells , activated cells , and virus . The model captures random fluctuations of the system as well as the mean behaviour . We estimate the time it takes for all the latently-infected cells to be eradicated . Eradication of these cells is considered a major hurdle in eliminating infection . We predict a wide range of eradication times , highlighting the importance of studying latently-infected cells . We also estimate the frequency and duration of viral blips , and find qualitative agreement with clinical studies . By refining our models , we hope to find guidelines that can be used in practise to distinguish between clinically insignificant statistical blips , and instances of drug failure . | [
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] | 2011 | A Stochastic Model of Latently Infected Cell Reactivation and Viral Blip Generation in Treated HIV Patients |
Diseases of humans and wildlife are typically tracked and studied through incidence , the number of new infections per time unit . Estimating incidence is not without difficulties , as asymptomatic infections , low sampling intervals and low sample sizes can introduce large estimation errors . After infection , biomarkers such as antibodies or pathogens often change predictably over time , and this temporal pattern can contain information about the time since infection that could improve incidence estimation . Antibody level and avidity have been used to estimate time since infection and to recreate incidence , but the errors on these estimates using currently existing methods are generally large . Using a semi-parametric model in a Bayesian framework , we introduce a method that allows the use of multiple sources of information ( such as antibody level , pathogen presence in different organs , individual age , season ) for estimating individual time since infection . When sufficient background data are available , this method can greatly improve incidence estimation , which we show using arenavirus infection in multimammate mice as a test case . The method performs well , especially compared to the situation in which seroconversion events between sampling sessions are the main data source . The possibility to implement several sources of information allows the use of data that are in many cases already available , which means that existing incidence data can be improved without the need for additional sampling efforts or laboratory assays .
Infection incidence ( the number of new infections per time unit ) is a basic epidemiological measure that describes the transmission of an infection through time . Because the exact time at which an individual acquired an infection is difficult to assess , time of symptom onset is often used as a proxy ( e . g . [1] ) . When the time between the moment of infection and symptom onset ( the incubation period ) is predictable , this proxy will not bias results , but incidence estimation does become problematic with asymptomatic infection or when incubation periods vary unpredictably [2] . Another common problem for measuring incidence is the time resolution of data , as the temporal precision of incidence is directly related to that of data “sampling” . Ideally , each new infection is detected and recorded immediately , but in reality this is rarely possible and new cases are often recorded at irregular intervals and a low number of time points , resulting in suboptimal resolution incidence data [3 , 4] . Even more importantly , when sampling intervals are larger than the duration of symptoms , a proportion of cases will be missed . This problem is especially common in the case of wildlife diseases , as natural populations are often sampled incompletely and at relatively large intervals [5] . In such cases , indirect measures of incidence that rely on evidence of past infection are needed . The presence of specific antibodies indicates whether an individual has previously been infected , and the distribution of different antibody ( Ab ) types ( e . g . IgG , IgM , IgA ) can give a rough indication of how recently the individual was infected [6–9] . If individuals in a population are sampled repeatedly , a seroconversion event in between two sampling events provides further information about the time since infection . Aside from being present or not , Abs vary over time in quantity ( titer ) and quality ( avidity ) . On the condition that this temporal variation is sufficiently constant and predictable within and between individuals , these antibody dynamic properties can be used for a more accurate estimation of the time since infection . Avidity ( Ab-antigen bond strength ) tends to increase with time since infection , which means that it can in some cases be used to back-calculate the time since infection . But although this method is used routinely , e . g . for human cytomegalovirus [10 , 11] , its sensitivity is low , and it can only differentiate between “recent” or “old” ( e . g . less or more than 90 days since infection for cytomegalovirus ) infection events [6 , 12] . Temporal dynamics of Ab levels can be another source of information about time since infection . In such cases a model must be created that describes the course of Ab levels ( titers ) over time since infection using known serological response data . This model is then used to back-calculate , given an Ab titer , the time since infection , which in turn can be used for incidence estimation . This has been done for pertussis [13 , 14] , HIV [15 , 16] and Salmonella [17 , 18] . While this method is promising , significant improvements are still possible in two main ways . A common , important limitation for developing good time since infection models is the lack of detailed information about individual Ab dynamics , which limits the explanatory power of such models as they must in that case be estimated using cross-sectional instead of individual data ( e . g . [18] ) . Experimental challenge studies , in which the exact time since infection is known , would be needed to describe and model the within-individual Ab dynamics needed to calculate time since infection , but these are notoriously difficult to conduct [19] . A perhaps more feasible approach to improving time since infection models would be to make optimal use of all available sources of information on the course of infection . While changes in Ab presence/titer over time can contain much information on time since infection and are the most obvious input data , additional information is contained in parameters such as the presence/quantity of the pathogen ( or of other immune response markers ) , individual age ( e . g . for typical childhood infections , young individuals are more likely to have been infected recently than older ones ) or season ( e . g . for seasonal infections , individuals are more likely to have been infected recently during or short after the peak transmission season ) . Here , we present a novel method that allows the integration of multiple serological biomarkers ( Ab presence/absence/titer , pathogen presence/absence ) as well as additional prior knowledge ( e . g . age , season , capture probability ) to inform a semi-parametric mixed model that back-calculates the time since infection of each individual , in a Bayesian framework . The integration of multiple sources of information ensures the optimal use of data that are often already available but not yet taken into account . We apply this method to estimate the incidence of Morogoro virus ( MORV ) infection in Natal multimammate mice ( Mastomys natalensis ) . This model system is used because the epidemiological and demographic parameters necessary for testing this method are well known for this infection . MORV is a member of the arenaviruses , a family of zoonotic viruses that includes viruses able to cause hemorrhagic fever in humans after acquiring infection from wild rodents ( e . g . Lassa virus ( LASV ) , Junin virus , Machupo virus ) [20] . It is restricted to East-Africa , and while it does not seem to cause disease in humans it is closely related to Lassa virus which causes Lassa hemorrhagic fever in West-Africa , and with which it shares the same host species . Because both the population ecology of the rodent host M . natalensis and the infection ecology of MORV have been studied thoroughly ( driven by the host’s status as an agricultural pest species and the virus’ close resemblance to LASV ) [21 , 22] , MORV infection provides a good model system for testing the current method . As is the case for other time since infection methods , two types of datasets are needed to estimate incidence . A first dataset , consisting of any type of data that contains information on the temporal course of infection ( e . g . Ab titer dynamics in an infected individual ) , is used once in order to create an integrated model of individual time since infection . Once created , this model can be used to estimate incidence from cross-sectional sampling data that ideally ( but not necessarily ) includes repeated measures of individuals . We use a wildlife disease model system to develop and test the method because detailed individual-level infection/antibody dynamics are available , but also to show that the method is applicable to both human and wildlife infections . Because it is usually difficult to monitor infections at a high time-resolution , this method can provide a way to improve the quality of longitudinal data without having to increase sampling efforts .
The estimation of the time of infection θ can be based on different dimensions of the immune response that each require a slightly different approach . In the following we consider two different sources of information . Because an individual can of course only have been infected when it was alive and present in the population , the estimation of θ can be improved by incorporating prior information about the probability of an individual being alive/present , i . e . by modeling P ( θ|T ) . Here , we show how to implement information on mortality rate and maximum life span , age at the time of sampling , and encounter probability , but note that any source of information can be used in a similar way as long as it results in a realistic prior distribution . Knowledge about the maximum life span can be informative because it sets an upper boundary to the possible time since infection , and is especially useful in situations where the maximum life span is short relative to the possible time since infection . If an individual was last tested at time tn and the maximum life span is known , then the prior distribution P ( θ|T ) can be reduced to P ( θ | T ) ∼ 1 life span θ > ( t n - life span ) θ < t n , with [ . < . ] is a boolean operator that returns 1 or 0 when the equality is true or false , as shown in Fig 3a . Similarly , one could make use of the mortality rate , as this is directly associated with the possible age of an individual . If an individual was first encountered at time t1 and we assume a mortality rate γ as inferred from data , we arrive at prior distribution P ( θ | T ) ∼ max exp ( γ ( θ - t 1 ) ) , 1 θ < t n , as shown in Fig 3b . This figure clearly shows that , due to mortality , it becomes increasingly unlikely for individuals to have been alive , and therefore infected , further in the past . When more precise information exists on the age of an individual focus individual ( which is trivial for humans , while for wild animals this can be based on physiological or morphological features such as weight ) , this can be taken into account explicitly by including P ( θ | T ) ∼ θ > ( t 1 - age ( t 1 ) ) θ < t n , if the individual was first encountered at time t1 , see Fig 3c . More applicable to wildlife infections is the use of encounter probability ( typically termed trapping or capture probability , but for consistency and human application we will here refer to it as encounter probability ) . In a typical capture-mark-recapture study , only a proportion of individual is captured during each session , and well-developed methods exist for estimating encounter probability [27 , 28] . This encounter probability can be used to estimate the likelihood of an individual being alive at a certain point in time , assuming a closed population during that time ( no migration ) . If an individual is first encountered at time t1 , the probability of it being born at time θ decreases with t1 − θ , as it becomes increasingly unlikely that it was not encountered during ( t1 − θ ) / Δt trapping sessions . If we estimate encounter probability penc for every trapping session , this information can be used to further improve the prior time distribution: P ( θ|T ) ∼ max[ ( 1−penc ) ( t1−θ ) /Δt , 1 ][ θ<tn ] ≈ max{ exp[ penc ( θ−t1 ) Δt ] , 1 }[ θ<tn ] , where Δt is the sampling or trapping interval time , and with the latter approximation valid only when penc < <1 . This approach only holds if one can assume a closed population where every individual was in the population during its lifetime and the effects of migration are negligible . One could also use seasonal information or cross-sectional data to inform the prior P ( θ|T ) , or in fact any other data source that contains any type of information about the time since infection . Given the resulting posterior probability P ( θ|X , T ) , the observer still has to use a decision criterion to decide which time of infection θ is most likely . Probably the most obvious decision criterion is the mean squared error ( MSE ) of the time since infection by selecting the θ ^ i for which M S E = 1 N i n d ∑ 1 = 1 N i n d θ ^ ( i ) - θ ( i ) 2 , with i running over a population of Nind individuals , is minimal . It can be shown that this is the case for θ ^ = ∫ d θ P ( θ | T , X ) θ [29] . In order to assess the quality of the estimates , the remaining uncertainty on the time since infection can be inspected conditional on the observed data ( X , T ) , which can be quantified using the conditional entropy E ( θ|X , T ) [29] , i . e . , E ( θ | X , T ) = ∫ θ ′ d θ ′ p ( θ ′ | X , T ) log 2 p ( θ ′ | X , T ) , where θ′ runs over all possible time since infection values . Conditional entropy is a commonly used measure in information theory that quantifies ( in bits ) the remaining amount of uncertainty about the actual value of the quantity of interest ( here: time since infection ) . The highest entropy is attained for a uniform posterior probability distribution ( maximum uncertainty ) , whereas the minimum ( zero ) entropy is obtained when there is no uncertainty left about the actual value [29] . In an epidemiological context , the entropy value can be used to improve the reliability of estimated incidence ( see next paragraph ) by removing all estimates of θ for which the entropy value is larger than a threshold value . The choice of this threshold value will mostly depend on the trade-off between sample size and estimation error: a low threshold value will generally result in a higher quality of the remaining θ estimates , but at the cost of reducing the final size of the dataset , and will therefore be dataset-specific . One of the main purposes of knowing the time of infection of an individual is to analyse and model infection incidence on a population level . To this end , we need to estimate the time of infection θi for all sampled individuals i in the population and count the number of newly infecteds on a regular ( usually daily ) basis . Because in most situations only a proportion of individuals will be encountered and sampled , the “real” proportion of new infections needs to be estimated . This can be done by dividing the number of infecteds by an estimate of the proportion of encountered individuals . Given a certain sampling interval Δt and an encounter probability at each session ( penc ) , this proportion can be approximated by proportion encountered = γ ∫ d t exp ( - γ t ) 1 - ( 1 - p e n c ) t / Δ t , where the integral runs over all the survival times t following an exponential distribution with 1/γ ( the average lifespan of an individual in our simulation ) , t / Δt is the approximate number of sampling sessions during lifetime t , and ( 1 − penc ) t/ Δt is the approximate probability that an individual is never encountered during these sessions . Next , in order to test the back-calculation scheme , we need a dataset of individuals in a population , with full knowledge of their infectious status at each moment . Also , to test the efficacy of the method as a function of sample size ( with regard to intervals between sampling sessions as well as the sampling effort ) , we need datasets collected under different trapping regimes . We therefore simulate MORV transmission in a population of multimammate mice , “sampled” in different trapping sessions , with each individual given simulated infection attribute data based on the experimentally-derived [23] course of Ab levels and probability of virus presence in blood and excretions . These simulated data are equivalent to epidemiological data obtained through surveys with repeated sampling , but now of course with the difference that our simulated data are completely known for testing purposes . All simulated data , as well as the Matlab code used to apply the time of infection estimation method , can be found in S1 Data . As input for the model , we use simulated data from an existing individual-based spatially-explicit SEIR model , which models the population dynamics and the transmission of Morogoro virus in M . natalensis [30] . In this model , individuals are born in the susceptible ( S ) state and can become infected through contact with infectious ( I—infectious state ) individuals . When infected , they enter a latent stage ( E—exposed state ) during which they cannot transmit the virus , until they become infectious ( I ) after around 6 days . After around 45 days they stop being infectious , recover from the infection ( R—recovered state ) and remain immune against re-infection for the remainder of their life . Latent and infectious periods were simulated assuming an exponential distribution . The simulation is run over a total area of 10ha , but in order to recreate a realistic situation in which individuals can move freely in and out of the study site , only the individuals that are encountered within a central 5ha area were available for “trapping” . Realistic population densities and fluctuations are used , ranging between around 10 and 150 per ha . After a simulation burn-in period , two years of data are considered ( from day 1000 until 1730 ) . Throughout the simulation we keep track of each individual’s age , time since infection t , and we simulate trapping sessions with a time interval Δt , in which every individual present in the 5ha area has a probability ptrap to be trapped . Whether an individual is trapped or not is determined using pseudo random numbers . This way , for every individual we can generate an artificial set of measurements ( T , Xk ) that we can then use to estimate the time of infection θ ^ . Xab are random realisations according to the multivariate distribution shown in Eq 8 at times T . Xvb and Xve are random draws with respective probabilities pvb and pve at times T . We vary the time intervals between capture sessions using Δt = 1 , 7 , 14 , 28 , 56 days , as well as the probability for each of the individuals to be captured using ptrap ∈ ( 0 , 1 ) . We implement a maximum life span of M . natalensis of 450 days based on [31] . The average mortality rate ( averaged across the year ) is calculated from the simulation data , and estimated to be μ = 0 . 008537 mice/day ( average life span of 117 days ) . Both maximum and average life span are used as prior information for all time of infection estimates .
The estimation of the time since infection is much improved by the use of Ab levels , as opposed to when only using Ab presence/absence data ( Figs 4 and 5 ) . The use of Ab levels also results in a much better reconstruction of incidence dynamics , even without including additional information such as virus presence or individual age ( Fig 6 ) . When using Ab presence/absence data , incidence can only be estimated with a low temporal resolution , the main consequence being that the peaks and troughs of the incidence dynamics were estimated badly ( Fig 6 ) . Although the incidence peaks are estimated quite well when using Ab levels , the periods of low incidence are still often over-estimated ( Fig 6 ) . The inclusion of additional information ( Vb , Ve , individual age ) greatly improves the estimation of time since infection and incidence ( Figs 4–6 ) . Interestingly , this effect is more pronounced when using Ab presence/absence than when using Ab levels . The combined use of Ab levels and other available information results in the highest quality reconstruction of incidence dynamics , where the inclusion of additional information mainly reduces the previously observed over-estimation of low incidence levels between peaks . Nevertheless , even when using Ab presence/absence instead of Ab level data , incidence can be reconstructed well when including Vb , Ve and individual age . This is encouraging , given the fact that many datasets , especially for wildlife infections , already contain some or all of this information; it means that by applying the back-calculation method , many existing incidence estimations can be improved significantly without additional laboratory or sampling efforts . The quality of the estimates strongly depends on sampling frequency ( or trapping interval ) and the proportion of individuals that is encountered ( or trapped ) and sampled . While more additional prior information always results in a better estimation of the time since infection , we see that , at low ( realistic ) encounter probabilities , this effect is strongest ( Fig 4 ) . We also observe that a higher sampling frequency results in better estimates ( Fig 5 ) , and this is largely an effect of increased sample sizes: when adjusting the trapping probability to equalise sample sizes of different sampling frequencies , this effect mostly disappears ( S1 Fig ) . This means that , in theory , similar results can be reached for any sampling frequency or trapping interval , but only if the sampling effort is increased so that a sufficient number of individuals can be sampled . Nevertheless , we observe that long sampling intervals ( 28–56 days ) generally result in lower quality estimates ( S1 Fig ) , indicating that a shorter interval would still be preferred . In the model , we introduce the use of entropy ( which is inversely related to information ) as an indicator of the amount of uncertainty contained by an estimate . Fig 7 shows how estimates of the time since infection with a higher deviation from the real time since infection generally also contain less information ( i . e . have a higher entropy ) . Similarly , we observe a strongly positive correlation between the MSE of the estimated time of infection and the entropy level ( S2 Fig ) . Therefore , by removing estimates above a critical entropy value , the MSE can be lowered , albeit at the cost of a lower sample size . Because of this trade-off it is not possible to suggest an optimal critical entropy cut-off value , which should rather be chosen depending on the specific situation , sample size and quality of available information . Although the model performs well and seems promising for a wide range of situations , there are a number of important assumptions and prerequisites that must be met before it is possible to apply the model to data . First , of course , empirical data on the dynamics of biomarkers ( e . g . antibodies , viral RNA , etc ) within individuals must be available . These can be relatively straightforward data such as knowledge about when after infection individuals seroconvert and how long antibodies remain detectable , or more elaborate information such as the temporal variation of antibody and virus levels after infection . Then , these data can only be used if they are sufficiently consistent across individuals . If there is too much inter-individual variation in the shape of biomarker dynamics , it will not be possible to predict individual patterns . This does not however mean that there can not be individual variation in the magnitude of the response , as this would in fact be easy to implement into the model . Further care must be taken if biomarker data have been obtained through laboratory experiments . Because laboratory conditions are often controlled and limited , natural variation in factors such as individual differences in immune response , stress , secondary infection , initial dose , boosting , etc . may result in different biomarker dynamics that could invalidate a time since infection model if they can not be incorporated into the model [32] . Ideally this is tested through a comparative study between laboratory and field patterns , but if such a study has not been done we must assume that the patterns observed in laboratory conditions apply to the natural situation . Other factors that could render the use of a time since infection model difficult are the existence of maternal antibodies and the simultaneous presence of chronically and acutely infected individuals , as these factors would be difficult ( but not necessarily impossible ) to disentangle and take into account . On the other hand , under certain conditions these factors may even improve the model , as they provide additional information; for example , if maternal antibodies only occur for a certain period in newborn individuals , and if maternal antibodies can be distinguished from other antibodies ( e . g . because of lower levels or using a different assay ) , this information can likely improve the estimation of the time since infection when incorporated into the model . Under the conditions described here , the model is a significant improvement on existing models ( e . g . [14 , 17 , 18 , 33] ) . It provides a relatively simple probabilistic framework for the incorporation of any data source that can inform the estimation of time since infection , such as biomarker level/presence , age , season , sex , weight , etc . , and thus allows for the use of individual-level data to interpret cross-sectional survey data and estimate population-level incidence . An important strength of the method is that it does not assume a certain form for the underlying models , which makes it possible to use a general spline method but also a more specific ordinary differential equation ( ODE ) method when a good ODE can be found ( e . g . [17] ) . More specifically for wildlife infections , the method has the potential to enhance existing long-term data . Often , large logistical efforts are necessary to collect longitudinal data on wildlife infections , and even the best datasets have a relatively low temporal resolution , typically consisting of monthly ( but often less frequent ) capture sessions [5 , 34–37] . Prevalence or incidence patterns resulting from such data are usually also limited to this capture frequency , and to our knowledge the only efforts for improving these data have been the rough estimation of seroconversion events between two capture sessions ( e . g . [38 , 39] ) . We have shown however that by integrating multiple sources of information ( that have often already been collected or analysed ) , the quality of incidence data can be greatly improved , especially ( but not uniquely ) when predictable antibody level dynamics are available . Due to its flexibility , the model presented here allows the integration of multiple sources of information , thus making optimal use of all available data for estimating individual times of infection and population incidence . It provides a conceptually simple , flexible framework for estimating the time since infection and incidence of human as well as wildlife infections , and can potentially be used to significantly improve incidence estimation based on already existing data . | Human and wildlife diseases can be tracked by looking at incidence , which is the number of new infections per time unit ( typically day , week or month ) . While theoretically this would only be a matter of counting the number of newly infected individuals , in reality these data are difficult to acquire due to limited sampling possibilities and undetectable cases . This means that a method must be used to estimate the real incidence using a limited amount of data . For many infections , the concentration and quality of antibodies changes predictably over time , which means that one could use the antibody level at any point in time to back-calculate how much time passed since the infection entered the body . Other information , such as the age of the individual , or the presence of the pathogen , can also help to estimate when an individual became infected . Improving on existing methods , we developed a method that allows the use of a wide range of information sources for estimating individual time since infection . Using arenavirus infection in mice , we show that this method works well when sufficient background data are available , and that it can greatly improve the estimation of incidence patterns . | [
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] | 2016 | Estimating Time of Infection Using Prior Serological and Individual Information Can Greatly Improve Incidence Estimation of Human and Wildlife Infections |
Bacteria encode a single-stranded DNA ( ssDNA ) binding protein ( SSB ) crucial for genome maintenance . In Bacillus subtilis and Streptococcus pneumoniae , an alternative SSB , SsbB , is expressed uniquely during competence for genetic transformation , but its precise role has been disappointingly obscure . Here , we report our investigations involving comparison of a null mutant ( ssbB− ) and a C-ter truncation ( ssbBΔ7 ) of SsbB of S . pneumoniae , the latter constructed because SSBs' acidic tail has emerged as a key site for interactions with partner proteins . We provide evidence that SsbB directly protects internalized ssDNA . We show that SsbB is highly abundant , potentially allowing the binding of ∼1 . 15 Mb ssDNA ( half a genome equivalent ) ; that it participates in the processing of ssDNA into recombinants; and that , at high DNA concentration , it is of crucial importance for chromosomal transformation whilst antagonizing plasmid transformation . While the latter observation explains a long-standing observation that plasmid transformation is very inefficient in S . pneumoniae ( compared to chromosomal transformation ) , the former supports our previous suggestion that SsbB creates a reservoir of ssDNA , allowing successive recombination cycles . SsbBΔ7 fulfils the reservoir function , suggesting that SsbB C-ter is not necessary for processing protein ( s ) to access stored ssDNA . We propose that the evolutionary raison d'être of SsbB and its abundance is maintenance of this reservoir , which contributes to the genetic plasticity of S . pneumoniae by increasing the likelihood of multiple transformation events in the same cell .
Natural genetic transformation can compensate for the absence of sexual reproduction in bacteria and allows genetic diversification by frequent recombination . Two recent studies illustrated the remarkable ability of the transformable species Streptococcus pneumoniae , a Gram positive human nasopharyngeal commensal and respiratory pathogen , to evolve rapidly [1] , [2] . The former study revealed that the genome of a strain that colonized a single child received an estimated 23 recombinational replacements over 7 months , resulting in the substitution of 7 . 8% of the genome [1] . In the latter study , 615 recombination events varying in size from 3 bp to 72 , 038 bp , with a mean of 6 . 3 kb , were detected through sequencing of 240 pneumococcal isolates of the same lineage [2] . These events occurred over about 40 years . Pneumococcal transformation requires the development of competence , which relies on the transient expression of a specific set of genes encoding proteins necessary for binding of exogenous double-stranded DNA ( dsDNA ) , for internalization of single-stranded DNA ( ssDNA ) fragments extracted from donor dsDNA [3] , and for homologous integration of ssDNA into the chromosome [4] , [5] . Expression of these genes is induced in response to an unmodified extracellular heptadecapeptide [6] , called CSP ( for Competence Stimulating Peptide ) , and is under the tight control of a complex regulatory circuit [7] , [8] . Despite maintenance of intact genetic information after internalization into competent cells , ssDNA fragments reextracted from transformed cells soon after uptake have much less transforming activity than the parental dsDNA and are therefore described as being ‘in eclipse’ [9] . This transient loss of activity is accounted for by a reduced uptake of ssDNA compared to dsDNA [10] . Donor genetic information emerges from eclipse as integration of transforming ssDNA into recipient dsDNA proceeds . While in eclipse , ssDNA is recoverable from transformed cell lysates as a nuclease-resistant nucleoprotein complex [11] . ssDNA in eclipse complex ( EC ) is distinguishable from nucleotides and from naked ss or dsDNA by hydroxylapatite ( HAP ) chromatography [12] . EC was reported to contain a single competence-induced protein with an apparent molecular mass of 15 . 7–19 . 5 kDa [13] , [14] , identified using Western blotting as SsbB [15] . This 131 residue protein is , with SsbA ( 156 residues ) , one of two paralogous ssDNA-binding proteins present in S . pneumoniae [16] . Whilst the essential ssbA gene [17] is constitutively expressed , ssbB is specifically induced during competence [4] , [5] . Bacillus subtilis also possesses two SSBs , but both are induced in competent cells [16] . Apart from its induction at competence and its cofractionation with transforming ssDNA in S . pneumoniae , little is known regarding the role ( s ) of SsbBs in transformation . As EC ssDNA was shown to be resistant to nucleases [11] , it was tempting to attribute a protective role to SsbB [15] . However , reduction in chromosomal transformation due to ssbB inactivation appeared variable in both species . In S . pneumoniae a 10- to 30-fold reduction was initially reported [18] , but subsequent studies only found a 3- to 5-fold reduction [15] , [19] , [20] . Similarly , in B . subtilis the transformation defect resulting from ssbB ( previously called ywpH ) inactivation varied from 5–10 [21] , [22] to up to 50-fold [23] . To characterize the role of SsbB in DNA transformation , we investigated the impact of ssbB mutations on chromosomal and plasmid transformation . We show that SsbB plays a direct role in the stabilization of internalized ssDNA and that its cellular concentration is adjusted so as to handle very large quantities of ssDNA . ssDNA stabilization is of particular importance when high concentrations of exogenous DNA are available , as revealed by the diminution in the absolute number of transformants at the highest concentration used . In contrast , our results indicate quite surprisingly that the absence of SsbB facilitates plasmid transformation at high DNA concentration . In view of these findings , we propose that the evolutionary raison d'être of pneumococcal SsbB is to maintain a reservoir of internalized ssDNA to permit successive rounds of chromosomal transformation allowing the formation of new combinations of mutations , thereby directly contributing to the genetic plasticity of this species .
We first wished to establish the cellular amount of both SSBs in competent pneumococcal cells . Because SsbA and SsbB share a similar , though not identical , mode of binding [24] , this information is relevant to indicate whether these proteins are likely to play a significant role in the processing of internalized ssDNA . Western-blotting with crude extracts of competent cells was used to quantify SsbB and SsbA through comparison with known quantities of purified SsbA and SsbB ( Figure 1A and Figure S1 ) . The estimated number of SsbA and SsbB molecules per cell is ∼3 , 500 and ∼70 , 000 , respectively . While barely detectable in non-competent cells , SsbB thus appears to be ∼20-fold more abundant than SsbA during competence . Assuming binding in the 65 mode [24] , SsbB could potentially cover ∼1 . 15 Mb ssDNA . It is of note that the abundance of SsbB is fully consistent with a previous estimate that the competence-induced EC-bound protein represented as much as 6 . 4% of total proteins synthesized in competent cells [14] . To assess the importance of SsbB for chromosomal transformation , the ssbB gene was inactivated by mariner transposon insertion mutagenesis ( generating mutation ssbB::spc2C hereafter called ssbB−; Figure 1B ) and mutants of SsbB harbouring a 7 or 27-residue C-ter deletion were constructed as described in Materials and Methods . SsbB C-ter truncations were constructed because of the documented importance of the acidic tail , DDD ( I/L ) PF , of Escherichia coli and B . subtilis SSBs for specific interactions with protein partners involved in DNA metabolism [25] , [26] and because SsbB also harbours an acidic tail , EEEELPF [16] . Western-blotting confirmed the truncation of SsbB in the two mutants ( Figure 1B ) . We then compared chromosomal transformation frequencies , scoring transformants resistant to streptomycin ( SmR; rpsL41 point mutation ) with R304 donor DNA ( 0 . 5–2 µg ml−1 ) , in wild type , ssbB− , ssbBΔ7 and ssbBΔ27 cells ( Table 1 ) . In a series of experiments with two different strains carrying the same minitransposon insertion , inactivation of ssbB reduced transformation 4 . 8 to 13 . 5 fold . The reason for this unusual variability will become clear in the last section of Results . In parallel , a 2 . 2 to 3 . 6 fold reduction in SmR frequency was observed in ssbBΔ7 cells compared to wildtype cells ( Table 1 ) . Compared to ssbB− cells , transformation frequencies appeared significantly higher in ssbBΔ7 cells suggesting that SsbBΔ7 exhibits residual activity . On the other hand , SmR frequency appeared similar in ssbBΔ27 and ssbB− cells , suggesting that SsbBΔ27 is inactive ( Table 1 , experiment IV ) . The latter mutant protein was not further characterized . The physical fate of transforming DNA was then examined in competent wildtype , ssbBΔ7 and ssbB− cells following exposure for 3 min to a 7771-bp homologous DNA fragment , uniformly labeled with 32P ( Materials and Methods ) . Analysis of total DNA extracted after incubation at 25°C or 30°C of transformed cells for 1 to 30 min was carried out using agarose gel electrophoresis . This allows discrimination between label still associated with internalized ssDNA fragments and chromosome-associated label resulting from both homology-dependent integration and/or reincorporation via replication of ssDNA degradation products [27] , [19] ( Figure S2A and S2C ) . To evaluate the impact of ssbB mutations on the stability of internalized ssDNA , we choose to conduct pairwise comparison experiments involving a mutant , ssbB− or ssbBΔ7 , and its wildtype parent . While similar uptake was achieved in each pair of strains ( Figure S2B and S2D ) , a significant acceleration in the decay of internalized ssDNA was observed in ssbB− cells compared to wild type at both 25°C and 30°C ( Figure 2A , upper part ) . In contrast , ssDNA appeared stabilized in ssbBΔ7 cells at both temperatures ( Figure 2A , lower part ) . As expected , ssDNA decay was accelerated in all cases at 30°C compared to 25°C . The overview of pairwise experiments conducted to compare internalized ssDNA decay in ssbB mutants and in the wild type established the existence of systematic and opposite biases for the ssbB− and ssbBΔ7 mutants ( Table S2 ) . The amount of ssDNA recovered was lower in ssbB− than in wild type in 100% of 21 measured ratios ( including the 7 values from Figure 2 ) ; and it was higher in ssbBΔ7 than in wild type in 100% of 22 measured ratios ( including the 8 values from Figure 2 ) . Together with the previous demonstration that SsbB is bound to ssDNA in transformed cell extracts [15] , these findings strongly suggested that SsbB protects internalized ssDNA and that the truncated SsbBΔ7 protein is still able to fulfill this role . While the latter observation was not unexpected in view of the location of the predicted ssDNA-binding site of SsbB in the N-terminal moiety [16] , it indicated that the C-ter tail is not required for SsbB to readily access incoming ssDNA . Agarose gel electrophoresis of transformed cell extracts also allowed the measurement in the same experiment of the incorporation/integration of internalized 32P donor label in the chromosome ( Figure S2A and S2C ) . Our pairwise comparison approach revealed a differential behaviour of the ssbB− and ssbBΔ7 mutants . Compared to wild type , the two mutants exhibited respectively a faster and a slower rate of incorporation/integration of donor label in the chromosome ( Figure 2B ) . These findings were fully consistent with the observation of an accelerated rate of ssDNA degradation in ssbB− cells , on one hand , and of an increased protection of internalized ssDNA in ssbBΔ7 cells , on the other hand . Extracts from transformed wild type and ssbB− cells were then compared using HAP chromatography to document possible changes in EC resulting from ssbB inactivation . Approximately half of the internalized 32P label was recovered as dsDNA , i . e . was eluted with 0 . 25–0 . 28 M sodium phosphate buffer ( PB ) [12] , in both wildtype and ssbB− cell extracts ( Figure 3 ) . This material , which co-eluted with 3H-labeled recipient chromosomal DNA ( Figure 3A ) , is known to originate from both homology-dependent integration of internalized ssDNA fragments and reincorporation of ssDNA degradation products through replication [27] , [19] . The remaining part of internalized label , while eluting at the position of EC in wildtype extracts ( i . e . at 0 . 10–0 . 13 M PB [12] ) , behaved completely differently in extracts from ssbB− cells . No label appeared at the position of nude ssDNA ( i . e . at 0 . 17–0 . 18 M PB [12] ) and most eluted with PB concentrations well below 0 . 1 M ( Figure 3 ) . As short DNA chains are known to elute at 0 . 07 M PB [12] , this result suggested that internalized ssDNA extracted from ssbB− cells was in the form of short fragments . This would be consistent with its behaviour during gel exclusion chromatography on Sephacryl S400HR , which discriminates fragments by size ( data not shown ) . This result confirmed the conclusion that ssbB inactivation significantly decreased the stability of internalized ssDNA , which could be the primary reason for the reduction in transformation frequency in ssbB− cells . In support of the idea that SsbB plays a direct role in protection of ssDNA from endogenous nuclease ( s ) , HAP chromatography analysis of extracts of transformed ssbBΔ7 cells provided evidence for a direct interaction of SsbB with internalized ssDNA . Two types of changes were detected , a ∼2-fold increase in the amount of 32P label recovered as EC-like material combined with a shift in position of EC which eluted at a lower PB concentration than in wildtype extracts ( Figure 3 ) . The increased amount of 32P label in EC , and therefore of ssDNA , confirmed that internalized ssDNA is stabilized in ssbBΔ7 cells . Assuming that SsbBΔ7 exhibits a mode of binding similar to that of full-size SsbB , an interpretation of this result is that more SsbBΔ7 was bound to ssDNA . Increased binding could result from a modification of the off-rate constant , k ( off ) , of SsbBΔ7 . Alternatively , deletion of the acidic C-ter might prevent interactions with processing protein ( s ) that are normally required to displace SsbB from ssDNA , thus slowing down ssDNA processing . As concerns the shift in EC position , we tentatively attributed it to a drastic change in the isoelectric point of the truncated protein ( 9 . 39 and 5 . 90 for SsbBΔ7 and SsbB , respectively ) resulting from the removal of acidic residues . Whatever the explanation , we take the shift in EC position as an additional strong argument in favor of a direct interaction of SsbB with internalized ssDNA . We then sought to establish whether SsbB would be required for transformation with a unique short fragment , i . e . when SsbB is in very large excess over the number of nucleotides to be protected and processed . We transformed ssbB− , ssbBΔ7 and wildtype cells with a short ( 888-bp or 903-bp ) SmR fragment ( Materials and Methods ) , using DNA concentrations low enough to ensure the uptake of a unique fragment per cell . Transformation with the 888-bp fragment was reduced 3 . 3±0 . 6 fold in both ssbB− and ssbBΔ7 cells compared to wild type ( Figure 4A ) . A similar reduction was observed with both mutant strains with the 903-bp fragment ( 3 . 0±0 . 7 fold reduction ) . Because the ssbB− and ssbBΔ7 mutations had opposite effects on ssDNA stability ( Figure 2 and Figure 3 ) , we concluded that during processing of a single short molecule , SsbB is not required for protection of the internalized fragment but participates directly to the formation of homologous recombinants ( transformants ) . We also concluded that the C-ter acidic tail of SsbB is crucial for this participation . To further characterize the role of SsbB , plasmid transformation was investigated . While in the same experiment the ssbB− and ssbBΔ7 mutations had a similar impact on transformation of a unique short chromosomal fragment ( Figure 4A ) , they had a clearly different effect on plasmid pLS1 transformation ( Figure 4B ) . Dose-response curves showed that ssbBΔ7 cells behaved similarly to wild type , whilst a 6 . 0±0 . 7 fold reduction in plasmid transformation was observed with ssbB− cells at the lowest plasmid concentration . We suggest that this reduction is a direct consequence of the destabilization of internalized ssDNA observed in ssbB− cells ( Figure 2 ) , whereas SsbBΔ7 efficiently protects internalized DNA ( Figure 2 ) , thus allowing plasmid installation at normal ( i . e . wildtype ) frequency . These data suggest that SsbB plays no active role in plasmid installation , apart from its protective action on internalized ssDNA . To confirm these conclusions , dose-response curves for plasmid transformation were established in parallel for wildtype , ssbB− and ssbBΔ7 cells over a wider range of DNA concentrations . Whatever the concentration of plasmid DNA , ssbBΔ7 and wildtype cells behaved similarly; a possible trend toward a ∼2-fold higher transformation rate in ssbBΔ7 than in wildtype cells at high plasmid DNA concentrations was tentatively attributed to the increased protection of ssDNA in the former cells ( Figure 5 ) . Surprisingly , plasmid transformation frequency in ssbB− cells appeared to vary over a 100-fold range , from 10-fold lower than wild-type frequency at the lowest DNA concentrations , to 10 fold higher , at the highest concentration ( Figure 5 , lower panel ) . In the latter case , the number of plasmid transformants approached the number of chromosomal transformants for a point mutation . Such an absolute frequency was unprecedented in S . pneumoniae , a species in which dimeric covalently closed molecules , the most active donors for plasmid transformation , have been shown to be very inefficient [28] . We conclude from this observation that , in wildtype pneumococci , SsbB antagonizes the reconstitution of a plasmid replicon . A molecular explanation for this antagonistic role of SsbB is proposed in the Discussion . The finding that in competent pneumococcal cells SsbB can protect up to ∼1 . 15 Mb ssDNA led us to hypothesize that this protein may be very important in allowing cells to cope with high concentrations of exogenous DNA . This hypothesis was tested by investigating dose-response curves for chromosomal transformation . This analysis revealed a clear difference between ssbB− and ssbBΔ7 cells ( Figure 6 ) . At donor DNA concentrations in the range 1–4 µg ml−1 , SmR single and RifR SmR double transformation frequencies in ssbBΔ7 cells paralleled those in wildtype cells ( Figure 6 , lower panel; 2 . 3±0 . 3 and 5 . 0±1 . 5 fold reduction for single and double transformants , respectively ) . In contrast , in ssbB− cells absolute SmR transformation frequencies progressively diminished with increasing DNA concentration and SmR frequency relative to wild type was reduced up to 11 . 6-fold at the highest DNA concentration ( Figure 6 , lower panel ) . The defect in ssbB− cells was confirmed through selection for RifR SmR double transformants , where a ∼170-fold reduction relative to wild type was observed ( Figure 6 , lower panel ) . It is of note that the frequencies of double RifR and SmR transformants are in all cases close to the square of the frequencies for an individual marker at the same DNA concentrations , which is what is expected for independent transformation events . The same reasoning leads to the prediction that the occurrence of a triple transformant would be 2000 to 3000-fold more frequent in the presence than in the absence of SsbB . These data thus establish that SsbB is of particular importance at high concentration of exogenous DNA and directly impacts on the likelihood of observing multiple recombination events in the same cell . Furthermore , these data provide an explanation of the unusually large fluctuations in transformation frequency of ssbB− cells compared to wild type in this ( Table 1 ) and other studies [15] , [18]–[20] . These were likely due to differences in transforming DNA concentration . Finally , while SsbB clearly boosted chromosomal transformation at high DNA concentration , we wished to establish whether this protein also played a specific role during the processing of heterologous regions . SsbB could , for example , be required to protect from endogenous nucleases or , more generally , hide long ssDNA segments during or after heteroduplex formation . A mariner SpcR cassette was inserted in cps2E , a gene in the capsule locus of strain D39; transformation of the D39 cps2E::spc7C mutation in R6 derivatives relies on integration of 8 , 653-nt long heterologous region including the mariner cassette ( Materials and Methods ) . In wildtype cells , the heterologous region transformed with a 10-fold reduced frequency compared to the SmR point mutation ( Figure 7 ) . A similar reduction was observed in ssbB− cells; irrespective of DNA concentration , the SpcR/SmR ratio appeared constant ( Figure 7 ) . Therefore , SsbB plays no specific role in processing of long heterologies . On the other hand , these data confirmed a reduction not only in the relative frequency of SmR ( 11 . 3-fold; Figure S4C ) or SpcR ( 15 . 0-fold; Figure S4D ) transformants in ssbB− cells compared to wild type , but also in the absolute number of SmR or SpcR transformants ( Figure S4A and Figure S4B ) at high DNA concentration .
As ssbB− and ssbBΔ7 cells exhibit a similar ∼3 . 3-fold deficit in transformation compared to wild-type cells when a unique 888-bp fragment is internalized ( Figure 4A ) , whilst the ssbB− and ssbBΔ7 mutations had opposite effects on ssDNA stability ( Figure 2 and Figure 3 ) , we first concluded that SsbB was not required for protection of the short internalized fragment but stimulates ssDNA processing into recombinants ( Figure 8 ) . It is of note that the absence of protective role of SsbB under these conditions could be accounted for by the direct loading of DprA and RecA on ‘nascent’ ssDNA , i . e . on ssDNA just emerging from the transmembrane channel [16] . We further concluded that the C-ter acidic tail of SsbB is crucial for its participation to ssDNA processing . In view of the documented key role of the acidic C-ter of B . subtilis SsbA [26] and E . coli SSB [25] , it is tempting to speculate that SsbB C-ter is involved in specific interactions with other processing protein ( s ) . It is known that short dsDNA is cut upon binding at the cell surface [29] , halving the average size of internalized ssDNA . The observed deficit thus indicates that SsbA cannot substitute for SsbB in the processing of a ∼450 nucleotide fragment , despite the fact that occluding the entire fragment would require only ∼28 or 50 SSB molecules , respectively , in the 65 or 35 mode of binding [24] . Intriguingly , a ∼2 . 3-fold deficit is also observed at low concentration of chromosomal DNA ( Figure 6 ) . Because nonspecific cutting at the cell surface occurs on large DNA at mean spacings that are greater than 7 kb [29] , fragments taken up from chromosomal DNA are presumably >15-fold larger than with an 888-bp donor . This suggests the deficit is essentially independent of the length of ssDNA to be processed , and therefore probably of the number of SsbB molecules bound . Thus , the type of event SsbB could be involved in during processing may occur only once per fragment . SsbB could for example be required to target a processing protein to the displaced recipient strand , to trigger its specific cleavage and/or degradation . Inactivation of ssbB negatively impacts the stability of internalized ssDNA , as revealed by analyzing the fate of 32P-labeled donor DNA using agarose gel electrophoresis ( Figure 2 ) . HAP chromatography of extracts of transformed ssbB− cells provided an independent confirmation of this destabilization , as essentially no donor label was eluted at the position of EC , the nucleoprotein complex normally found in wildtype cells ( Figure 3 ) . Thus , while some SsbA was detected at the position of EC [15] , which raised the question as to whether SsbA is also involved in processing of incoming ssDNA [16] , the present finding suggests that no other protein , including SsbA , can substitute for SsbB . The acidic tail of SsbB does not appear to be required for protection of internalized ssDNA since the ssbBΔ7 mutation increased the half-life of internalized ssDNA ( Figure 2 ) and resulted in more ssDNA present within EC ( Figure 3 ) . Interestingly , SsbBΔ7 EC eluted earlier , a shift we attribute to the deletion of acidic residues resulting in a severe change in the isoelectric point of the protein . We consider this result as formal proof that SsbBΔ7 is bound to internalized ssDNA . Altogether , these findings are consistent with a protective role of SsbB against endogenous nuclease ( s ) . We propose that the deficit of chromosomal transformants in ssbB− cells at high DNA concentration ( Figure 6 and Figure S4 ) , as well as of plasmid transformants at low DNA concentration ( Figure 4B and Figure 5 ) , results directly from the absence of protection of internalized ssDNA by SsbB . While arguing in favor of a direct protective role of SsbB , the present data appear puzzling in view of a previous finding that ssDNA is almost immediately degraded when internalized in dprA or recA mutant cells [19] . The latter observation necessarily implies that in the absence of DprA ( or RecA ) , SsbB cannot access to and protect ssDNA . Possible explanations to this paradox have been proposed [16] , [20] but none has received support yet . Because SsbB stabilizes internalized ssDNA , is highly abundant during competence and , at high DNA concentration , is of crucial importance for chromosomal transformation whilst antagonizing plasmid transformation , we propose that the evolutionary raison d'être of pneumococcal SsbB and its abundance is the maintenance of a reservoir of internalized ssDNA . Thereby SsbB increases the likelihood of occurrence of multiple chromosomal transformations in the same cell by allowing subsequent processing of internalized molecules by DprA and RecA ( Figure 8 ) , after completion of a first round of recombination . SsbB could thus permit the rescue of rare beneficial mutations present in the population and/or the creation of new combinations of mutations , directly contributing to the generation of diversity and to the genetic plasticity of S . pneumoniae . In B . subtilis , SsbB abundance in competent cells [41] and the scatter in transformation defect in ssbB mutants [21]–[23] are reminiscent of the situation in S . pneumoniae . We conclude that the main role of SsbB is to optimize chromosomal transformation in S . pneumoniae . Whether this conclusion also applies to B . subtilis and more generally to other naturally transformable species remains to be established .
S . pneumoniae strains , plasmids and primers used in this study are described in Table S3 . The ssbBΔ7 and ssbBΔ27 mutations were constructed by designing primers , respectively ssbB18 and ssbB17 , designed so as to introduce a premature stop codon . PCR fragments generated with the ssbB14-ssbB18 and ssbB14-ssbB17 primer pairs were cloned into a ColE1 plasmid derivative , pR326 , and the resulting plasmids ( pR469 and pR470 ) were used as donors in transformation of strain R1501 . Homology-dependent integration of plasmid pR469 and pR470 at ssbB created the ssbBΔ7 and ssbBΔ27 mutations , respectively ( strains R2081 and R2082; Table S3 ) . Western-blot characterization of the SsbBΔ7 and SsbBΔ27 mutant proteins were performed as described below; C-ter truncations of SsbB did not appear to reduce significantly the amount of mutant protein per cell ( Figure 1B ) . Insertions of an SpcR cassette ( spc gene ) in cps2E , ssbB and thyA was obtained by in vitro mariner mutagenesis as previously described [42] . Plasmid pR412 was used as a source for the spc minitransposon ( Table S3 ) . Briefly , plasmid DNA ( ∼1 µg ) was incubated with a target PCR fragment in the presence of purified Himar1 transposase , leading to random insertion of the minitransposon within the fragment . Gaps in transposition products were repaired as described [42] and the resulting in vitro-generated transposon insertion library was used to transform an S . pneumoniae strain . Location and orientation of minitransposon insertions were determined as previously described [42] through PCR reactions using primers MP127 or MP128 in combination with either one of the primers used to generate the target PCR fragment . PCR fragments to target the cps2E , ssbB and thyA genes were generated using the cps2C1-cps2F1 , ssb1-ssb2 and thyA1-thyA2 primer pairs ( Table S3 ) , respectively . Cassette-chromosome junctions were sequenced and the cps2E::spc7C ( cassette at position +949 with respect to cps2E start codon ) , ssbB::spc2C ( cassette at position +158 with respect to ssbB start codon ) and thyA::spc5A ( cassette at position +139 with respect to thyA start codon ) insertions were retained ( strains TD153 , R1192 and R2512 , respectively; Table S3 ) . Inactivation of cps2E , the fifth gene in the cps operon in strain D39 , is known to abolish synthesis of the polysaccharide capsule [43] . As the D39 derivative R6 ( Table S3 ) contains a 7 , 505-bp deletion of the cps2A to cps2H genes , corresponding to bp 314 , 740–322 , 244 of D39 NCTC 7466 [44] , transformation of the D39 cps2E::spc7C cassette ( see above ) in R6 derivatives relies on integration of 8 , 653-nt long heterologous region including the spc minitransposon . Stock cultures were routinely grown at 37°C in Casamino Acid Tryptone ( CAT ) medium to OD550 = 0 . 4; after addition of 15% glycerol , stocks were kept frozen at −70°C . CSP-induced transformation [6] was performed in C+Y medium as described previously [45] , using precompetent cells treated at 37°C for 10 min with synthetic CSP-1 ( 100 ng ml−1 ) . After addition of transforming DNA and unless otherwise indicated , cells were incubated for 20 minutes at 30°C . Transformants were selected by plating on CAT-agar supplemented with 4% horse blood , followed by selection using a 10 ml overlay containing chloramphenicol ( Cm; 4 . 5 µg ml−1 ) , erythromycin ( Ery; 0 . 05 µg ml−1 ) , kanamycin ( Kan; 250 µg ml−1 ) , spectinomycin ( Spc; 100 µg ml−1 ) , streptomycin ( Sm; 200 µg ml−1 ) or tetracyclin ( Tc; 1 µg ml−1 ) , after phenotypic expression for 120 min at 37°C . An 888-bp or a 903-bp fragment carrying the rpsL41 allele , which confers resistance to Sm , used as transforming DNA were amplified from R304 chromosomal DNA using respectively the IM51-IM52 or the rpsL_7-IM52 primer pairs ( Table S3 ) . 20-ml culture ( ∼108 cfu ml−1 ) was pre-incubated at 37°C for 3 min and treated with CSP-1 for 12 min at 37°C . Culture was then divided in two parts ( 9 ml each ) that were further processed in parallel at 25°C or 30°C as follows . After 3 min of incubation , competent cells were exposed for 3 min to 40 µl of a 7 . 771-bp S . pneumoniae fragment , uniformly labeled with 32P ( ∼500 ng; ∼2 106 cpm ) . This fragment was generated by PCR-amplification in the presence of [α32P]-dATP , using as template R800 chromosomal DNA and the BM37–AM15 primer pair ( Table S3 ) as previously described [46] but with the Phusion polymerase ( Ozyme ) . Uptake was terminated with DNase I ( 50 µg ml−1; 100 Kunitz Units ml−1 ) and incubation was continued . After 1 , 5 , 15 and 30 min incubation , 2 ml samples were taken and cooled down by addition of 8 ml cold CAT medium . Cultures were then centrifuged for 10 min at 10 , 000 g to harvest cells . The pellet was resuspended in 200 µl SEDS containing RNAse A ( 20 µg ml−1 ) and cells were lysed as described [46] . Two phenol extractions followed by ethanol precipitation were used to recover total DNA which was resuspended in 50 µl Tris buffer ( 10 mM , pH 8 . 5 ) . DNA was subjected to overnight electrophoresis ( 28 V cm−1 ) on 15-cm-long 1% agarose gel in Tris-acetate/EDTA buffer . Gels were dried for 2 hr at 53°C before exposure to a Phosphorimager screen ( Fuji Photo Film ) . Electrophoregrams were analyzed using the MultiGauge software ( Fujifilm ) . A thymine-requiring strain was used as recipient to allow labeling of recipient DNA through incorporation of 3H-thymidine during growth of precompetent cells in C+Y medium containing 150 µg ml−1 cold thymidine + 75 ng ml−1 3H-thymidine ( PerkinElmer NET355; Sp . Act . 20 Ci mmol−1 ) . A 400-ml culture of thyA mutant strains R2512 , R2582 and R2583 ( Table S3 ) at an OD550 = 0 . 136 in C+Y medium was treated with 40 µg of CSP-1 at 30°C for 15 min and exposed to 978 µl of a mixture containing 72 µl of a uniformly 32P-labeled 10 , 380-bp fragment ( 93 µg ml−1 ) prepared as described above but with BM37-BM112 primer pair ( Table S3 ) and 906 µl of cold chromosomal DNA ( 555 µg ml−1 ) ; the latter was added to ensure uptake and processing of significant amounts of DNA into each cell in the transformed culture; the specific activity of the mix used for transformation of wildtype , ssbB− and ssbBΔ7 cells in Figure 3A was respectively 88 , 097 , 70 , 078 and 62 , 134 cpm µl−1 . After 5 min exposure to transforming DNA , DNase I was added ( 5 µg ml−1+10 mM MgCl2; final concentrations ) for 1 min and the culture was then rapidly cooled before centrifugation at 5 , 000 g for 15 min . The pellet was washed with 30 ml cold SSC ( SSC is 0 . 15 M NaCl , 0 . 015 M sodium citrate ) and resuspended in 4 ml lysis buffer containing 0 . 1 M NaCl , 0 . 05 M Tris-HCl ( pH 7 . 5 ) , 0 . 01 M EDTA , 0 . 5% Sarkosyl , 0 . 1% Triton X-100 , 1 mM phenylmethylsulfonyl fluoride , 50 µg ml−1 RNase A , and 10% glycerol . The clear lysate obtained after 10 min at 37°C was passed 20 times through a 5-cm 21G hypodermic needle to reduce viscosity . 4-ml sheared lysate ( corresponding to 400 ml transformed culture ) were loaded on a 5-ml HAP column ( ∼4-ml flowthrough collected ) followed by elution with PB as previously described [15] , [12] . Briefly , columns were washed with 20 ml 0 . 04 M PB ( 5 fractions collected ) and developed with a 32-ml linear gradient of 0 . 04 M to 0 . 4 M PB ( 2 2-ml , followed by 10 1-ml and 9 2-ml fractions collected ) , then washed with 24 ml 0 . 4 ml 0 . 065 M PB + 2 M NaCl ( 6 fractions collected ) . 1/50th of each fraction was mixed with 3 ml Ultima GOLD ( Perkin Elmer ) to measure 3H counts . 3H channel counts were corrected for overflow from the 32P channel . For quantification of SsbA and SsbB , cells from 10 ml culture were collected by centrifugation; pellets from the CSP-induced and control cultures were resuspended in 300 µl 1× Tris ( 10 mM , pH 8 . 0 ) -EDTA ( 1 mM ) buffer and lysed for 10 min at 37°C after addition of 8 µl DOC ( 0 . 25% ) -SDS ( 0 . 5% ) . 100 µl loading buffer were added and the suspension was incubated for 5 min at 85°C before loading onto a 15% acrylamide-SDS gel . Western blotting were classically performed as described in reference [15] , using rabbit polyclonal antibodies raised against purified S . pneumoniae SsbB protein ( 2 hr [Figure 1A] or overnight [Figure 1B] incubation at room temperature with a 1/5000th dilution ) . The ECL ( Figure 1A ) or ECL Plus ( Figure 1B ) Western Blotting Detection System ( GE Healthcare ) and a BioImager were used for signal detection ( 2 min exposure ) . | Natural genetic transformation can compensate for the absence of sexual reproduction in bacteria , allowing genetic diversification by frequent recombination . In many species , transformability is a transient property relying on a specialized membrane-associated machinery for binding exogenous double-stranded DNA and internalization of single-stranded DNA ( ssDNA ) fragments extracted from exogenous DNA . Subsequent physical integration of internalized ssDNA into the recipient chromosome by homologous recombination requires dedicated cytosolic ssDNA–processing proteins . Here , we document the roles in the model transformable species Streptococcus pneumoniae of one of these processing proteins , SsbB , a paralogue of SsbA the ssDNA–binding protein essential for genome maintenance in bacteria , which is expressed uniquely in cells competent for genetic transformation . We show that SsbB is highly abundant , potentially allowing the binding of ∼1 . 15 Mb ssDNA ( half a genome equivalent ) ; that it participates in the processing of ssDNA into recombinants; that it protects and stabilizes internalized ssDNA; and that , at high DNA concentration , it is of crucial importance for chromosomal transformation whilst antagonizing plasmid transformation . We conclude that SsbB creates a reservoir of ssDNA , presumably allowing multiple transformations in the same cell , and that S . pneumoniae has evolved SsbB to optimize chromosomal transformation , thereby contributing to its remarkable genetic plasticity . | [
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] | 2011 | Role of the Single-Stranded DNA–Binding Protein SsbB in Pneumococcal Transformation: Maintenance of a Reservoir for Genetic Plasticity |
Appropriate displays of aggression rely on the ability to recognize potential competitors . As in most species , Drosophila males fight with other males and do not attack females . In insects , sex recognition is strongly dependent on chemosensory communication , mediated by cuticular hydrocarbons acting as pheromones . While the roles of chemical and other sensory cues in stimulating male to female courtship have been well characterized in Drosophila , the signals that elicit aggression remain unclear . Here we show that when female pheromones or behavior are masculinized , males recognize females as competitors and switch from courtship to aggression . To masculinize female pheromones , a transgene carrying dsRNA for the sex determination factor transformer ( traIR ) was targeted to the pheromone producing cells , the oenocytes . Shortly after copulation males attacked these females , indicating that pheromonal cues can override other sensory cues . Surprisingly , masculinization of female behavior by targeting traIR to the nervous system in an otherwise normal female also was sufficient to trigger male aggression . Simultaneous masculinization of both pheromones and behavior induced a complete switch in the normal male response to a female . Control males now fought rather than copulated with these females . In a reciprocal experiment , feminization of the oenocytes and nervous system in males by expression of transformer ( traF ) elicited high levels of courtship and little or no aggression from control males . Finally , when confronted with flies devoid of pheromones , control males attacked male but not female opponents , suggesting that aggression is not a default behavior in the absence of pheromonal cues . Thus , our results show that masculinization of either pheromones or behavior in females is sufficient to trigger male-to-female aggression . Moreover , by manipulating both the pheromonal profile and the fighting patterns displayed by the opponent , male behavioral responses towards males and females can be completely reversed . Therefore , both pheromonal and behavioral cues are used by Drosophila males in recognizing a conspecific as a competitor .
Aggression is a complex , innate behavior that likely evolved in the context of obtaining or defending resources [1]–[3] . Appropriate displays of aggression rely on the correct identification of potential competitors . In Drosophila as in most species , males fight with other males [4]–[7] and do not attack females . A wide variety of sexually dimorphic cues might be used by a male in directing agonistic rather than reproductive behavior towards another fly . As in other insect species , sex recognition in flies is strongly dependent on chemical communication , mediated by surface cuticular hydrocarbons that serve as pheromones [8]–[13] . Drosophila cuticular hydrocarbons ( CH ) are sexually dimorphic; female surfaces are characterized by dienes like ( Z , Z ) -7 , 11 heptacosadiene and ( Z , Z ) -7 , 11 nonacosadiene that act as aphrodisiacs [12] , [14] , [15] , while male surfaces include ( Z ) -7 tricosene [11] , [16] , [17] and 11-cis-vaccenyl acetate ( cVA ) [18]–[20] act as anti-aphrodisiacs to other males . While the effects of CH ( called “pheromones” in what follows ) in courtship have been described in detail ( reviewed in [8] ) , little is known about the roles of these substances in aggression . Pheromones that promote aggressive behavior have been identified in vertebrate and other invertebrate species [21]–[25] , and cVA has been reported to modulate male aggressiveness in flies [26] . However , to what extent pheromonal or other cues are sufficient to trigger aggression in Drosophila remains largely unknown . Although complex interactions between genes , environmental signals , and hormones ultimately influence the development and manifestation of social behaviors like aggression [27]–[30] , the core circuitry involved appears to be pre-wired in the nervous system , as animals with no previous social experience can engage in normal agonistic encounters . Both males and females display aggression , but the specific behavioral patterns displayed are sexually dimorphic [31]–[33]: of greatest importance to the present work is that males “lunge , ” in which they rise high on their hind legs and snap down hard on an opponent with their fore legs , while females display “head butt” and “shove” behaviors in which they do not rise above the horizontal . Rarer high-intensity patterns of behavior displayed by males include “boxing” and “tussling . ” Finally , males establish hierarchical relationships , while females do not [33] . Recently , it has been shown that male and female patterns of aggression can be switched by manipulation of male and female splice variants of the fruitless ( fru ) gene [34] . Manipulations of transformer ( tra ) , a splicing factor required for female development [35] , also have been shown to switch male and female patterns of aggression [36]: inhibiting tra expression in the female nervous system leads to the display of male-like fighting patterns , while ectopic expression of tra in the male nervous system leads to the display of female-like fighting patterns in males . tra , in conjunction with a second gene , tra-2 , mediates sexual differentiation by altering the splicing of doublesex and fru , which code for transcription factors responsible for regulating the morphological and behavioral aspects of sexual development [34] , [35] , [37]–[43] . In this work , we aimed to identify the cues used by males in identifying a conspecific as an opponent . Our strategy was to interfere with the expression of transformer by targeting a transgene carrying a dsRNA for tra ( traIR ) to different female tissues using the Gal4/UAS system . These masculinized females were paired with wild type Canton-S males in order to search for male aggressive responses . In parallel experiments , we asked whether it was possible to prevent aggression from a wild type male against another male by reciprocal manipulations in male flies . Our results show that by manipulating the pheromonal profiles and fighting patterns displayed by an opponent , male behavioral responses towards females and males can be completely reversed: wild type males fight rather than court when both pheromones and behavior are masculinized in females and court rather than fight when they are feminized in males . We propose that both pheromonal and behavioral cues can serve as key elements that allow Drosophila males to recognize a conspecific as a competitor .
Given the importance of pheromonal cues for sex recognition , we began by masculinizing the female oenocytes , specialized pheromone-producing cells [8] , [11] . A transgene carrying a dsRNA for tra ( traIR ) was targeted to the oenocytes using an oenocyte-specific Gal4 line [11] . These females were paired with wild type Canton-S in aggression assays . Surprisingly , pairings between wild type Canton-S males and oeno-gal4/UAS-traIR ( oetraIR ) females revealed that masculinization of the pheromone profile elicits male aggression towards females ( Figure 1A–B , E ) . For scoring , we quantified male lunging , as this is the most characteristic male aggressive response . Males never attacked wild type females ( Figure 1A ) , even after copulation , when females display rejection behavior and have acquired some male CHs on their surfaces [44] , [45] . In contrast , lunging behavior was observed in close to 60% of the experimental pairings , always performed by males ( Figure 1B ) since oetraIR females do not display lunging behavior ( Figure S1A ) . The number of lunges directed towards oetraIR females was comparable to the number targeted at Canton-S males ( Figure 1E ) . Male-to female aggression was never observed in fights between Canton-S males and any of the heterozygote parental control females either ( oeno-Gal4/+ and uas-traIR/+ females; Figure S5B ) . Analysis by mass spectrometry ( MS ) of the CHs profile from both intact animals ( Figure S2 , see Methods ) [45] and extract revealed that oetraIR females show a predominantly male profile , although small amounts of female CHs also are detected ( Figure 1F , Table 1 , and Figure S2 ) . As expected , male-characteristic sex pheromones that are not produced by the oenocytes , namely cVA and the recently identified 3-O-acetyl-1 , 3-dihydroxy-octacosa-11 , 19-diene ( CH503 ) [45] , were not detected in females ( Figure S2 ) . These results demonstrate that partial masculinization of the female pheromonal profile is sufficient to trigger male-to-female aggression . Males consistently court decapitated wild type females , but they do not attack decapitated or immobilized males , suggesting that male pheromones can elicit aggression only in the context of a moving fly . This observation raised the question of whether behavior of another animal could also contribute to the triggering of aggression . We hypothesized that the display of male patterns of behavior by the opponent might stimulate aggressive responses from a male . To test this , we masculinized the female nervous system , by using the pan-neuronal driver elav-Gal4 . This strategy has been shown to induce expression of FruM in the female CNS [36] . Moreover , it induced male-like patterns of fighting behavior in females; pairs of elav-gal4;UAS-traIR ( elavtraIR ) females are highly aggressive and lunge at each other [36] . We paired Canton-S males with behaviorally masculinized elavtraIR females and found that 85% of these pairs showed lunging ( Figure 1A ) . In this case , females lunged intensely at the males and initiated most of the fights ( Figure S1 ) . However , a smaller but substantial fraction of the males lunged at the females ( Figure 1B ) , with a 3-fold reduction in the number of lunges compared to that performed towards oetraIR ( Figure 1E ) . The fact that females usually dominate these fights ( Figure S1B–C ) is likely to be due to the fact that males persistently court the females despite being lunged at by them . The considerable difference in size between females and males also might contribute to giving the females an advantage [46] , [47] . Male aggression towards females was not observed in fights between Canton-S males and any of the heterozygote parental control females ( oeno-Gal4/+ and uas-traIR/+ females; Figure S5B ) . Since the pheromone profile of elavtraIR is unaffected ( Figure 1F , Table 1 , and Figure S2 ) , these females are as attractive as control females and males vigorously court them before transitioning to aggression . Nonetheless , because elavtraIR females display aggressiveness towards the males , only 42% of these pairings resulted in successful copulation ( Figure 1D ) . Courtship experiments towards headless targets confirm that in the absence of behavioral cues males cannot distinguish between elavtraIR and Canton-S females ( Figure 1C ) . Thus , males are willing to attack an opponent that exhibits male fighting behavior , even if that opponent is morphologically female and has a normal female pheromone profile . In order to analyze male responses towards further masculinized females , we simultaneously changed the sex of the female oenocytes and nervous system . When males were paired with elav-gal4;oeno-gal4/UAS-traIR females ( elav+oetraIR ) , lunging was observed in 94% of the fights ( Figure 2A–B ) . Like elavtraIR females , elav+oetraIR females initiated and dominated most fights ( Figure S3A–C ) . Remarkably , 92% of the males who lunged at these females did so prior to or without ever copulating ( Figure 2C ) . Since females do not make cVA , and this compound is only present on females after copulation , these results in which males attack females with masculinized hydrocarbon profiles but lacking cVA directly demonstrate that cVA is not necessary to trigger aggression . This is consistent with what was previously reported by Wang et al . [26] , showing that cVA promotes aggression but it is not required to initiate it [26] . The male latency to lunge at elav+oetraIR females was similar to that of pairs of Canton-S males ( Figure 2D ) . Moreover , successful copulation was observed in fewer than 25% of these pairings ( Figure 2E ) and the latency to achieve copulation was 6-fold higher compared to Canton-S females ( Figure 2F ) . Thus , wild type males respond to elav+oetraIR females as potential competitors rather than as potential mates . As further confirmation of these observations , we expressed traIR under control of a 1407-gal4 , a line that drives expression both in the oenocytes [12] , [48] and in the nervous system [48]–[52] . Expression of uas-traIR in females under the control of 1407-Gal4 has been previously shown by our laboratory to induce expression of FruM in the CNS [36] , and pairs of 1407-gal4/UAS-traIR ( 1407traIR ) females frequently lunge , although they show a mixture of male and female fighting patterns [36] . When paired with Canton-S males , 1407traIR females were as aggressive as elav+oetraIR ( Figure S3D–E ) , and the male response towards these two genotypes of females was indistinguishable ( Figure 2A–B , E ) . All the observed pairs of Canton-S males with 1407traIR females showed lunging ( Figure 2A ) , and only 25% of them copulated throughout 1 h ( Figure 2E ) . Analysis by MS of the CHs profile revealed that both elav+oetraIR and 1407traIR females show a predominantly male profile ( Figure 2G ) . Taken together , these results demonstrate that the display of both male pheromones and male patterns of behavior in a female reverses the normal dynamics between males and females . We next asked whether it was possible to inhibit male aggression towards other males . We employed a symmetric strategy , feminizing the same tissues in males by expressing an active form of transformer ( traF ) . Since males attack females that exhibit male pheromonal profiles but wild type female behavior ( oenotraIR; Figure 1B , E ) , suppression of male behavioral patterns by expressing traF in the nervous system should not prevent aggression from wild type males . Indeed , Canton-S males showed high intensity aggression towards elav-gal4;UAS-traF ( elavtraF ) males ( Figure 3A–B , E ) . There was a substantial increase in the number of lunges that CS males directed to elavtraF males compared to that directed towards both other Canton-S males ( Figure 3E ) , despite the fact that elavtraF males do not exhibit male patterns of aggression . Reciprocally , since the masculinization of the female nervous system triggers male aggression , the display of feminized pheromonal profiles in males should not completely suppress aggression from Canton-S males . Previous studies have shown that feminization of male pheromones elicits vigorous courtship behavior from wild type males [12] . Despite persistent courtship and frequent copulation attempts towards oeno-gal4/UAS-traF ( oetraF ) males ( Figure 3D ) , Canton-S males eventually transitioned to aggression . Canton-S males display normal aggression and courtship responses towards males from all the parental control lines ( elav-Gal4/+ , oeno-Gal4/+ , and uas-traF/+ males; Figure S5 ) . Courtship assays using headless target males confirm that oetraF males are highly attractive for CS males , since courtship index towards these males is significantly higher compared to courtship towards CS ( Figure 3C ) . Mass spectrometric analyses revealed that oetraF males show reduced levels of ( z ) -7-tricosene and intense signals from diene hydrocarbons that are characteristic of females ( Figure 3F , Table 2 , and Figure S4 ) . As expected , both control and experimental males still express cVA and CH503 ( Figure S4 ) . We next asked whether simultaneous feminization of oenocytes and the nervous system in males was sufficient to prevent aggression from wild type males . Indeed , males expressing traF driven by both elav-gal4 and oeno-gal4 trigger responses in males that are opposite to those anticipated in normal male-male interactions . Analysis by MS of the CH profile revealed that elav+oetraF males show a predominantly female profile ( Figure S6 ) . Aggression towards these males was greatly reduced , since in only 6 out of the 47 pairs analyzed did Canton-S males attack them ( Figure 3A ) . The fact that some elav+oetraF males were still attacked is likely due to the presence of residual male pheromones ( Figure S6 ) . Remarkably , 96% of the Canton-S males persistently courted and attempted copulation with elav+oetraF males ( Figure 3D ) . These effects were significantly different from those obtained with oetraF males and resembled the normal responses of males towards females . Previous experiments using oenocyte-less ( oe− ) flies showed that males court both males and females that are devoid of CHs [11] , suggesting that courtship is a “default” behavior in the absence of pheromonal cues . If aggression is also a default behavior , which is normally suppressed by female pheromones , wild type males should attack both oe− male and oe− female opponents . If instead aggression has to be triggered actively either via pheromonal or behavioral cues , males should not attack oe− flies that do not display male behavior . Indeed , aggression assays showed that Canton-S males did not display aggressive behavior towards oe− females ( Figure 4 ) . In contrast , they did attack oe− males ( Figure 4A , B ) , although at a reduced intensity compared to controls ( Figure 4B , C ) . Reduced aggressiveness directed towards oe− males indicates that pheromones missing from these males are required for normal intensity levels of fighting . It should be noted that oe− males still have normal levels of cVA [11] , which could also contribute to the aggressiveness displayed towards them by Canton-S males . Like oetraIR females , oe− females show wild type behavior and copulate with males . Nevertheless , males did not attack oe− females , even when they had previously mated with other males ( unpublished data ) . Future experiments will attempt to identify the male pheromonal cues that are sufficient to trigger male aggression against opponents who show no aggression towards them . Results presented here demonstrate that intense male aggression is evoked when females display masculinized pheromonal profiles . They show further that cVA is not required to trigger aggression . Our results indicate that surface pheromonal cues eventually triumph over other sensory cues , since males ordinarily do not fight females . Surprisingly , males also attack any opponent , male or female , displaying male behavior . The fact that males do not attack oe− females but do attack oetraIR and elavtraIR females suggests that , unlike courtship , aggression is not a default behavior and has to be actively triggered . The stimuli may be either chemical cues , which would be perceived through chemosensory input pathways , or cues derived from the display of male behavioral patterns , probably perceived via multimodal input pathways . The male willingness to attack elavtraIR females , which exhibit normal female pheromone profiles , is an unexpected result that could be accounted for by different scenarios . Males could be responding to a specific cue that triggers lunging behavior as a stimulus-response effect ( like a visual threat ) . However , this seems unlikely since we did not observe any specific behavioral pattern in females preceding attacks from Canton-S males . Alternatively , multiple cues emerging from the behavior of these aggressive females could be perceived by the males , converging on central neural pathways that ultimately determine the male switch from sexual to aggressive responses . Our results support the notion that whereas courtship is a default behavior , the escalation to aggressive interactions is a complex behavioral response that requires integration of different sensory modalities by higher order processing centers in the male brain . In this work , we show that masculinization of either pheromones or behavior in females is sufficient to trigger male-to-female aggression . In support of this , feminization of only one of these factors in males is not sufficient to prevent aggression from Canton-S males . However , males display little or no aggression against males in which the pheromone profiles and fighting patterns were simultaneously feminized . Remarkably , genetically inverting male and female fighting patterns and pheromone profiles of an opponent is sufficient to completely switch the behavioral response of a male . Taken together , our results indicate that Drosophila males use pheromonal and behavioral cues to recognize a conspecific as a potential competitor .
All fly strains were reared on standard fly food ( medium containing agar , glucose , sucrose , yeast , cornmeal , wheat germ , soya flour , molasses , propionic acid , and Tegosept ) . Flies were grown in temperature- and humidity-controlled incubators ( 25 °C , 50% humidity ) on a 12-h light/dark cycle , except for the oenocyte-less flies . Male or female pupae were isolated approximately 24 h prior to eclosion and housed in individual vials with food medium for 6 d prior to use in experiments . In male-male fights , a small dot of a water-based acrylic paint was applied to the dorsal thorax so that individuals could be easily identified . This procedure was performed under CO2 , at least 1 d before fighting . Wild-type Canton-S and elavC155-Gal4 lines were obtained from the Bloomington Stock Center . uas-traIR line was obtained from Barry Dickson ( Vienna Drosophila RNAi Center , No . 2560 ) and uas-TraF line was obtained from Bloomington Stock Center ( No . 4590 ) . The line oeno-Gal4 ( PromE ( 800 , ) line 2M ) was generated by J-C . B . [11] . We crossed either elavC155-Gal4 or oeno-Gal4 virgin females to males from the respective uas lines to generate the feminized or masculinized experimental lines . All the transgenes employed in each case were tested in heterozygosis ( hemizygosis for males containing elav-Gal4 ) . Adults lacking oenocytes were obtained as previously described [11] . For behavioral assays , all target flies generated in these cases had w+ background . We also used 1407-Gal4 ( Bloomington No . 8751 ) to generate masculinized females as described in previous studies [36] . For each genotype , five flies were placed in 100 ml of hexane containing 10 mg/ml of synthetic hydrocarbon ( hexacosane; Sigma-Aldrich ) for 30 min at room temperature . Five replicate samples were prepared for each genotype . The extract was removed , placed in a clean glass vial , and the solvent evaporated under vacuum . The extracts were re-dissolved in 30 ml of heptane prior to GC-MS analysis . GC-MS analysis was performed with a Quattromicro-GC ( Waters , Manchester , UK ) equipped with a HP-5 ( 5%-Phenyl-methylpolysiloxane column; 30 m length , 0 . 32 mm ID , 0 . 25 µm film thickness; Agilent ) . Ionization was achieved by electron ionization ( EI ) at 70 eV . One ml of the sample was injected using a splitless injector . The helium flow was set at 1 . 3 ml/min . The column temperature program started at 50 °C for 2 min , then increased to 300 °C at a rate of 15 °C/min . The quadrupole mass spectrometer was set to unit mass resolution and 3 scans/min , from m/z 37 to 700 . Chromatograms and mass spectra from GC-MS analysis were analyzed using MassLynx ( Waters , Manchester , UK ) . Compounds were identified on the basis of retention time and EI mass spectra . To determine the signal intensity for each hydrocarbon species , the area of its chromatographic peak from the total ion chromatogram was calculated and normalized to the area of the signal corresponding to the synthetic standard . Statistical analysis was performed using analysis of variance ( ANOVA ) followed by post hoc analysis with a Tukey-Kramer honestly significant difference ( HSD ) test ( http://faculty . vassar . edu/lowry/VassarStats . html ) . Aggression and courtship ( male-female or male-male ) assays were performed in individual chambers of 12-well polystyrene plates ( each chamber dimension is 10 mm diameter × 5 mm depth ) containing a food cup made of the cap of a 1 . 5 ml Eppendorf tube . Flies were transferred in pairs to assay chambers by aspiration . Experiments were started at Zeitgeber time 1 at 25 °C in a humidity controlled room ( 50% ) . For quantification of courtship towards decapitated targets , headless flies were placed in the center of the food cup prior to the transfer of the courting CS males . The courtship index is the fraction of a 10-min observation period spent by the male exhibiting courtship steps such as tapping , wing extension , licking , and attempting copulation , starting from the onset of courtship . The same chambers and conditions were used for courtship and aggression experiments to allow comparisons between experiments , since differences in chamber size lead to variations in behavior . Fights and courtship assays were videotaped and tapes were scored blindly . Courtship assays were recorded for 20 min while aggression assays were videotaped for 90 min and scored for 60 min after the time when both flies were introduced to the chamber . Latency to court , attempted copulation , and mating with intact targets were determined from recordings of the aggression assays . The time between when flies were loaded and the onset of copulation was defined as the mating latency . Similar criteria were used for determining courtship latency and attempted copulation latency . Attempted copulation is scored when courting males bend their abdomens towards the courtship object . For aggression assays , pairs of a Canton-S male and either a male or a female opponent were placed in each chamber . Lunging behavior was determined as previously described [6] . The time between when flies were loaded into chambers and the first lunge displayed by CS males was defined as the latency to lunge . Statistical analyses were performed with the Prism software ( version 5 . 0b , SPSS Inc . ) . p values were determined either via two-tailed Student's t test when comparing two groups or via ANOVA followed by the post hoc Bonferroni test when comparing multiple groups . For data that did not follow a parametric distribution , Mann-Whitney test was used for comparing two groups . | As in other species , the fruit fly Drosophila melanogaster uses chemical signals in the form of pheromones to recognize the species and sex of another individual . Males typically fight with other males and do not attack females . While the roles of pheromonal and other sensory cues in stimulating courtship towards females have been extensively studied , the signals that elicit aggression towards other males remain unclear . In this work , we use genetic tools to show that masculinization of female pheromones is sufficient to trigger aggression from wild type males towards females . Surprisingly , males also attacked females that displayed male patterns of aggression , even if they show normal female pheromonal profiles , indicating that pheromones are not the only cues important for identifying another animal as an opponent . By simultaneously manipulating pheromones and behavioral patterns of opponents , we can completely switch the behavioral response of males towards females and males . These results demonstrate that not only pheromonal but also behavioral cues can serve as triggers of aggression , underlining the importance of behavioral feedback in the manifestation of social behaviors . | [
"Abstract",
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"neuroscience/sensory",
"systems"
] | 2010 | Pheromonal and Behavioral Cues Trigger Male-to-Female Aggression in Drosophila |
How social traits such as altruism and spite evolve remains an open question in evolutionary biology . One factor thought to be potentially important is demographic stochasticity . Here we provide a general theoretical analysis of the role of demographic stochasticity in social evolution . We show that the evolutionary impact of stochasticity depends on how the social action alters the recipient’s life cycle . If the action alters the recipient’s death rate , then demographic stochasticity always favours altruism and disfavours spite . On the other hand , if the action alters the recipient’s birth rate , then stochasticity can either favour or disfavour both altruism and spite depending on the ratio of the rate of population turnover to the population size . Finally , we also show that this ratio is critical to determining if demographic stochasticity can reverse the direction of selection upon social traits . Our analysis thus provides a general understanding of the role of demographic stochasticity in social evolution .
The evolution of social traits remains a very active area of investigation in evolutionary biology [1–4] . Research has predominately focused upon how different mechanisms such as population structure [5–8] , kin discrimination [9–11] or greenbeard effects [3 , 12 , 13] create heterogeneity in interactions among individuals of different types , leading to the evolution of social traits . Recent evolutionary theory , however , has considered whether or not in the absence of interaction heterogeneity , demographic stochasticity alone can promote the evolution of altruism ( e . g . , [14–16]; see also [17] for when interaction heterogeneity and demographic stochasticity work in combination ) . These studies concluded that since altruism increases population size , it confers a stochastic advantage that can reverse ( weak ) selection against altruism . Counterexamples to this prediction have been found however ( e . g . , see below ) leading one to wonder whether unambiguous conclusions can be drawn . To address this question we develop a general theoretical analysis of the role of demographic stochasticity in social evolution for well-mixed populations . We start with a detailed description of birth and death events at the individual level [18] and then derive a very simple theory that makes a set of clear , general , predictions . Whether a social trait is favoured or disfavoured by demographic stochasticity is determined by how the social action alters the recipient’s life cycle . When the action alters the recipient’s death rate , altruism is stochastically favoured , and spite is stochastically disfavoured . When the action alters the recipient’s birth rate both altruism and spite can be either stochastically favoured or disfavoured , with the outcome depending upon the ratio of the rate of population turnover to the population size . These results provide a general understanding of the role of demographic stochasticity in the evolution of social traits . They also explain previous models and counterexamples , and illustrate how previous results are special cases of a simple general principle .
Consider a well-mixed population of size Ωn ( t ) , where Ω is the habitat size and n ( t ) is the population density at time t . The population consists of two types of individuals: type 1 individuals , who are social actors capable of altering the birth or death rate of other individuals in the population , and type 2 individuals who are not . The social action may occur through direct contact between individuals or by the production and uptake of an external compound ( e . g . , the release of siderophores or toxins by bacteria [19–21] ) . Each individual in the population is equally likely to be the recipient of the social action , and the effect of the social action upon the recipient is identical among types . We distinguish between two possible social traits: altruism , which we define to be an action that enhances the vital rates of other individuals ( e . g . , by increasing birth rates or decreasing mortality rates ) and spite , which we define to be an action that inhibits the vital rates of other individuals ( e . g . , by decreasing birth rates or increasing mortality rates ) . These are standard definitions if the social trait comes at a cost to the actor [22] . Thus at demographic equilibrium the population size n will increase as the frequency of altruism increases whereas it will decrease as the frequency of spite increases . Denote the per-capita birth and mortality rates as b and m , respectively , and let the per-capita cost of the social trait be ϵc , where ϵ is a parameter controlling the magnitude of the costs ( b , m , and c may depend upon population densities and/or the state of the environment ) . Thus the per-capita growth rate of social actors ( type 1 individuals ) and non-actors ( type 2 individuals ) is b − m − ϵc and b − m , respectively . As a consequence , whenever ϵ > 0 , non-actors have a selective advantage , and so in the absence of mutations and stochasticity they will ultimately take over the population . If ϵ = 0 , then the social trait is cost-free and so neither type of individual is selectively favoured . Finally , we suppose that mutation between the two types occurs at a per-capita rate μ . We will further assume that in the absence of selection and mutation , there is an asymptotically stable curve of ecological equilibria given by b = m . This is a curve rather than a fixed point because in the absence of selection and mutation , both types have identical per-capita growth rates . If selection is weak , mutations rare , and habitat size large , then the system dynamics occur on two timescales: a fast timescale corresponding to demographic processes ( birth and death events ) and a slow timescale corresponding to evolutionary change in population composition . As our primary interest is the evolution of the population , our focus is on the slow timescale . On this slow timescale , let p be the fraction of social actors ( type 1 individuals ) ; then we can rewrite total population density as a function of p ( t ) alone , that is , n ( t ) = n ( p ( t ) ) = n ( p ) ( see S1 Appendix ) . Then let b ( p ) , m ( p ) , and c ( p ) be the per-capita birth , death , and costs on the slow timescale . If ϵ is small then T ( p ) ≡ b ( p ) + m ( p ) is approximately the total rate at which demographic events are occurring and so is a measure of the rate of population turnover . Formally , it is also the variance in per-capita growth rate at selective neutrality . Using a diffusion approximation of the full , individual-based , stochastic process [23 , 24] ( see S1 Appendix ) and eliminating the fast timescale dynamics [25–28] , the evolutionary change in frequency of social actors in the population is described by the stochastic differential equation ( SDE ) d p = α ( p ) d t + σ 2 ( p ) d W t ( 1 ) where α ( p ) ≡ μ ( 1 − 2p ) − ϵc ( p ) p ( 1 − p ) , σ2 ( p ) ≡ p ( 1 − p ) T ( p ) /[Ωn ( p ) ] , Wt is a Wiener process and we have neglected terms of order ϵ/Ω and μ/Ω ( see S1 Appendix ) . Eq 1 is associated with a one-dimensional diffusion process with infinitesimal mean and variance α ( p ) and σ2 ( p ) [29 , 30]; when written as an SDE , the expression α ( p ) dt is often referred to as the “drift term” . If mutation rate is sufficiently large , the diffusion process admits a stationary distribution , which we will denote by π ( p ) . Note that in contrast to previous work ( e . g . , [16] ) , here our focus is the frequency of the social trait , p , rather than the density of social actors , n ( p ) p . As a consequence , there are no noise-induced effects in the drift term of Eq 1 , whereas there are often noise-induced effects in the drift term of the SDE describing the change in density of social actors ( see S1 Appendix , and also [16] ) . We opt to focus upon the frequency SDE rather then the density SDE because we are concerned with evolutionary processes , and evolution is a change in frequency not density .
We wish to use Eq 1 to determine if stochasticity favours one type over another . Since α ( p ) dt represents the expected change in frequency of the social actors , while σ 2 ( p ) d W t represents stochastic noise around this mean change , one is tempted to simply examine the sign of α ( p ) . With this approach , if α ( p ) < 0 then the social actor ( type 1 ) is disfavoured , which is the same conclusion as the deterministic model ( Ω → ∞ ) , and so this approach fails to take into account the role played by stochasticity . A second approach would be to suppose that whenever a mutation arises , it is either lost or sweeps to fixation before another mutation occurs , and so evolution proceeds according to a mutation-fixation process [31 , 32] . With this approach , assessing if a trait is favoured or not is often done by comparing the probability a trait i mutant sweeps to fixation in a population monomorphic for trait j to the role-reversed situation ( a comparison of invasion probabilities ) . If the costs of the social behaviour due to selection are sufficiently weak , ϵ ≈ 0 , then from Eq 1 the invasion probability of a single social actor in a population of non-social individuals is 1/[Ωn ( 0 ) ] , whereas the invasion probability of a single non-social individual in a population of social actors is 1/[Ωn ( 1 ) ] . Hence a comparison of invasion probabilities favours the social actor whenever the social trait increases population size ( altruism ) [14–16] . The problem with comparing invasion probabilities alone is doing so fails to consider the full evolutionary process . Because in a mutation-fixation process the population transitions from monomorphic state to monomorphic state , we can construct a Markov chain on the space of possible traits by letting Ni be the size of a trait-i population and μij be the per-capita rate at which trait i mutates to trait j . Then the population will transition from a trait i state to a trait j state at a rate μijNi × ( 1/Ni ) = μij . Thus in the absence of any biases in per-capita mutation rate , the population is equally likely to be observed in any monomorphic state , irrespective of the effect the trait has upon population size [32–34] . What both of these approaches have failed to take into account is the speed at which the change in population composition ( and hence the evolutionary process ) occurs . In particular , although the stochastic noise does not induce an average directionality to the change in p , the amount of stochasticity nevertheless is typically different for different values of p , and this will effect the speed at which the population composition changes , affecting the likelihood of observing the process in a particular state . As an analogy , a biased random walk whose step-size and time between steps depends upon the position of the walker will tend to spend more time in regions with smaller step-sizes and less frequent steps , independent of any bias in the directionality of the walk . Thus we will say that the social actor is favoured if , in the long-term , we are more likely to observe the system in a state in which the social actor is at greater frequency than the non-social actor ( see S1 Appendix ) . For example , in the case where a stationary distribution π ( p ) exists , the social actor is favoured if ∫ 1 / 2 1 π ( p ) d p > 1 / 2 . To understand how this applies to the stochastic process defined by Eq 1 , first suppose the social trait is cost-free ( ϵ = 0 ) . Then the behaviour of Eq 1 is determined by two factors: the magnitude of the mutation rate μ and the ratio T ( p ) /n ( p ) . Mutation does not directly favour one type over the other and therefore the ratio T ( p ) /n ( p ) should play a critical role in determining the values of p at which the system spends the most time . The following derivative tells us how this ratio changes with p: d d p [ T ( p ) n ( p ) ] = T ( p ) n ( p ) ( − d n / d p n ( p ) ︸ ( i ) + d T / d p T ( p ) ︸ ( ii ) ) . ( 2 ) There are two components to Eq 2 , each with a simple biological interpretation: ( i ) is the effect the social trait has upon population size , n ( p ) , and ( ii ) is the effect the social trait has upon population turnover , T ( p ) . In terms of our random walk analogy , as the population size increases , the step size of the random walk ( in terms of frequency p ) decreases , meaning that the process will tend to spend more time at values of p corresponding to large population sizes . Put another way , larger populations are more buffered against demographic stochasticity and thus effect ( i ) shows how the type resulting in the greatest population size tends to be favoured [14–16] . Likewise , the rate of population turnover ( as measured by the neutral variance in per-capita growth rate , T ( p ) ) can be thought of as controlling the frequency of steps taken by the random walker . Thus the process will tend to spend more time at values of p that correspond to less frequent steps , and so effect ( ii ) shows how the type minimizing T ( p ) tends to be favoured . Taken together these two effects therefore favour the type minimizing the amount of demographic stochasticity , as given by the ratio T ( p ) /n ( p ) . We can now examine how the different social traits influence effects ( i ) and ( ii ) . If the social trait is altruism , then as explained earlier the population size will increase as its frequency increases ( i . e . , dn/dp > 0; this process was the focus of previous work on the role of demographic stochasticity [14–16] ) . On the other hand , if the trait is spite then the population size will decrease as its frequency increases ( i . e . , dn/dp < 0 ) . Thus effect ( i ) always favours altruism and disfavours spite . The role played by effect ( ii ) is more complex . To see why , observe that on the slow timescale the demographic processes are in quasi-equilibrium and so T ( p ) = 2b ( p ) = 2m ( p ) . Therefore if either b ( p ) or m ( p ) are constant with respect to p then dT/dp = 0 . In this case only term ( i ) plays a role and so altruism is always favoured and spite disfavoured . Otherwise , to understand how the social action affects T ( p ) , we need to consider two cases: ( a ) the social action affects the death rate , or ( b ) the social action affects the birth rate . Consider the case where the social action affects the death rate . If the the social action is altruism then by definition it must decrease the death rate ( dm/dp < 0 ) and so we have dT/dp < 0 . Conversely , if the social action is spite then by definition it must increase the death rate ( dm/dp > 0 ) and so dT/dp > 0 . In both cases effect ( ii ) works in concert with effect ( i ) to always favour altruism and disfavour spite . Indeed the ratio T ( p ) /n ( p ) is monotonic is p , being minimized at p = 1 in the case of altruism and at p = 0 in the case of spite . Next consider the case where the social action affects the birth rate . If the social action is altruism then by definition it must increase the birth rate ( db/dp > 0 ) and so we have dT/dp > 0 . On the other hand , if the social action is spite then by definition it must decrease the birth rate ( db/dp < 0 ) and so we have dT/dp < 0 . Hence effect ( ii ) opposes effect ( i ) . As a result , altruism or spite can each be favoured or not depending upon the magnitude of effect ( i ) relative to the magnitude of effect ( ii ) . Moreover , the ratio T ( p ) /n ( p ) can be non-monotonic , meaning that it can be minimized by a polymorphic population . To illustrate these phenomena more concretely , we apply our analysis to several specific models ( see S1 Appendix for details ) . Throughout we use xi to denote the density of type i . Simulation results suggest that when more than two types of individuals are included in the population , the above results hold . For example , Fig 3a shows that when the social trait acts on death rate and there are several different types of individuals in the population , ranging from very altruistic to very spiteful , it is the most altruistic type that is favoured . Furthermore , Fig 3b and 3c shows that when the social trait acts on birth rate and there are multiple types of individuals in the population , it can be an intermediate level of altruism or spite that is favoured ( analogous to Fig 2b and 2d ) . Up until this point we have assumed the social trait is cost-free , ϵ = 0 . Suppose instead the social action has a cost , ϵ > 0 , which creates a directional bias disfavouring the social trait . We may then ask if/when the effect of stochastic noise can overcome this directional bias , and so reverse the direction of selection [14–16] . We will focus upon situations in which a stationary distribution , π ( p ) , exists . Since by construction the social actor ( type 1 ) is at a selective disadvantage ( ϵ > 0 ) , if ∫ 1 / 2 1 π ( p ) d p > 1 / 2 , then we may argue demographic stochasticity reverses the direction of selection . We illustrate this phenomenon with two examples . First , consider a population where the social actor is an altruist capable of altering birth rate such that b ≡ r + νx1 , m ≡ κ ( x1 + x2 ) , and c ≡ r , where r > 0 , κ > ν > 0 . Models based on these specific assumptions have been explored by previous authors , where it was argued that demographic stochasticity favours altruism and thus a selective reversal is possible [14–16] . This argument was based upon two main points . First , the authors observed that the drift term of the SDE associated with the density of social actors , pn ( p ) , could be either positive or negative due to the magnitude of noise-induced effects relative to selection . Second , the authors showed that whichever phenotype can grow to a larger population size in isolation is favoured ( altruists ) by applying a pairwise comparison of invasion probabilities . Each of these points has an interpretative issue . First , although noise-induced effects often appear in the drift term of the SDE describing the change in density of social actors ( the ecological process ) , these tend to disappear after the density SDE is converted to the SDE tracking the frequency of the social trait ( the evolutionary process ) , and this is indeed the case here ( see Eq 1 ) . It is these noise-induced effects that lead to the incorrect conclusion about when social traits are favoured . To see why , consider the above model when there are no mutations , μ = 0 , and no selection , ϵ = 0 . Then the social trait ( altruism ) is neutral . Suppose the population is initially at a state in which half the individuals are social actors , p = 1/2 . Then since the fixation probability of the social actor in a neutral population is equal to its proportion in the population , 50% of the time the social actor will sweep to fixation in the population . Unsurprisingly , the drift term for the frequency equation , α ( p ) , in Eq 1 is zero , that is , the expected change in frequency is zero . However , the drift term of the SDE for the density of social actors will be positive . This is because the population size goes up when the altruists fix more than it goes down when non-altruists fix . But altruism is neutral , and therefore the sign of the drift term of the density equation cannot be used as a measure of evolutionary ‘success’ . Second , although comparison of invasion probabilities does favour whichever phenotype grows to a larger population size in isolation , as we pointed out previously , if we place the invasion probabilities within the context of the full mutation-fixation evolutionary process the effect of population size disappears ( see also [32–34] ) . Indeed , these issues can be made readily apparent by considering the stationary distribution associated with the model ( this assumes mutations are explicitly included , which deviates from the model in [16] ) . In particular , the stationary distribution is π ( p ) ∝ p μ Ω κ − 1 ( 1 − p ) μ Ω κ − 1 e − ϵ r Ω κ p , ( 3 ) ( see S1 Appendix ) . If μΩ/κ > 1 , then mutations push the distribution towards p = 1/2 and so π ( p ) has a ( skewed ) bell-shape , whereas if μΩ/κ < 1 , the distribution accumulates at p = 0 and p = 1 and so π ( p ) has a ( skewed ) U-shape . At selective neutrality , π ( p ) is symmetric about p = 1/2 and so altruism is completely neutral . If altruism comes at a cost , ϵ > 0 , then π ( p ) is shifted in favour of the non-actor and so stochasticity can never reverse the direction of selection ( Fig 4 , Model 1—red ) . This conclusion can also be reached by noting the ratio T ( p ) /n ( p ) in this particular model is a constant , independent of p ( S1 Appendix ) . This is because any increase in population size ( which reduces the step size of the random walk in p ) is exactly compensated for by an increase in the rate of population turnover . Interestingly , it is possible to construct a model in which a selective reversal occurs by making only a slight modification of the above assumptions . Suppose b ≡ β + νx1 , m ≡ d + κ ( x1 + x2 ) , and c ≡ r , with r = β − d . This model has the same per-capita growth rate as the previous model but now the rate of population turnover ( i . e . , the variance in per-capita growth ) is larger . As a result , the ratio T ( p ) /n ( p ) is linearly decreasing in p . The stationary distribution is then π ( p ) ∝ p μ Ω r β κ − 1 ( 1 − p ) μ Ω r β κ − d ν − 1 ( β κ − d ν p ) r 2 Ω ϵ d ν − μ Ω r β κ − μ Ω r β κ − d ν − 1 . ( 4 ) In this modified model altruism is now stochastically favoured ( Fig 4 , Model 2—black ) and so stochasticity can reverse the direction of selection . Notice from Eq 4 the role played by mutation rate in shaping the stationary distribution . In the first model , mutation rate only controlled whether the distribution was normalizable or not . Now , however , mutation rate can alter whether or not a selective reversal is possible . It is important to stress that the difference in outcome between these two models is driven exclusively by demographic stochasticity . The deterministic components of these two models are the same . Put another way , the expected change in the frequency of the altruists is identical in the two models despite the second model predicting the evolution of costly altruism while the first model not doing so . In the first model selection pushes the distribution in favour of the non-altruists and demographic stochasticity has no biasing effect . In the second model , again selection pushes the distribution in favour of the non-altruists , but now demographic stochasticity is biased such that it decreases as the altruists become more common . The predicted population composition ( i . e . , the stationary distribution ) thus arises from a balance between selection favouring non-altruists and the demographic noise being smaller when the frequency of altruists is high . These effects only become apparent from consideration of the ratio T ( p ) /n ( p ) . Thus determining whether stochasticity can reverse selection requires analysis of this ratio , and we cannot exclusively focus upon how the social trait alters population size [14–16] .
Recent work has explored how stochasticity can alter social trait evolution by deriving a stochastic version of Hamilton’s rule [17] . Our work differs from this in a couple of important ways . First , those authors focused upon the expected evolutionary change alone , which is equivalent to considering the sign of α ( p ) of Eq 1 , whereas our focus is upon how social traits influence the evolutionary noise , and how this works in conjunction with the expected evolutionary change . Our results demonstrate that examining the expected evolutionary change alone may often be insufficient to determine whether a social trait subject to stochasticity is more or less likely to be observed . Instead one may need to account for both the expected change in the population composition as well as any change in ( unbiased ) demographic noise that occurs during evolution ( i . e . , the ratio T ( p ) /n ( p ) ) . Second , we have focused on indiscriminate social behaviours and as such , in well-mixed populations these traits are always either neutral ( if they are cost-free ) or selected against ( if they entail a cost ) . In contrast , Kennedy et al . [17] focuses upon cooperation preferentially directed towards kin . Our analysis has focused upon unstructured populations in which every individual is equally likely to interact with every other individual . It is well known that population structure can aid or hinder the evolution of social traits [5–7 , 36–38] by altering the likelihood that similar or dissimilar social actors interact with one another . Demographic stochasticity will likely factor into this ( see in particular [16] ) , but its impact will depend upon the relatedness of interacting individuals as well as the magnitude of the benefits of the social trait . As relatedness between individuals increases , in general so too will the strength of selection ( by generating indirect fitness benefits ) , which will tend to diminish the role of demographic stochasticity . However , in populations with low relatedness , or social behaviours with sufficiently low benefits ( and costs ) , we would expect our theory to apply . Interestingly , as shown in [16] , in deme-structured populations although the social behaviour can be disfavoured or neutral at the within-deme level , it can be favoured at the between-deme level if the social behaviour increases population size and so the number of dispersers [16] . An interesting parallel to our results is that in structured populations , helping behaviours effecting fecundity tend to be selectively favoured over those which effect survivorship [36–39]; a prediction that diverges from our model . One key difference between our model and these previous studies is that they focused upon the expected change in the social trait in populations of fixed size; as such , whether the helping behaviour is interpreted as one which effects survivorship or one which effects fecundity is based upon whether the population evolves through birth-death or death-birth updating . Hence this result is mediated through the scale of competition between interactants , whereas our result occurs through how the social action effects the evolutionary noise the population experiences . The role played by demographic stochasticity in populations of fluctuating size has received increased attention recently [14–16 , 40 , 41] . Our work here has provided a very general consideration of the evolution of two fundamental social traits , altruism and spite , and this analysis has revealed the importance of the action of the social trait upon the recipient . In particular , if the social action alters death rate , then provided selection is sufficiently weak , altruism is stochastically favoured while spite is stochastically disfavoured . If instead the social action alters birth rate , altruism and spite can be either favoured or disfavoured , depending upon mutation rate , the underlying population demography and how this determines the ratio of the rate of population turnover to the population size , T ( p ) /n ( p ) . The generality of our analysis suggests this principle likely has implications across other study systems as well . | Explaining the evolution of social traits such as altruism and spite remains a key outstanding problem in evolutionary biology . Here we develop a simple theory for the effect of demographic stochasticity ( random variation in an individual’s birth and death rates ) on the evolution of social traits . Our results provide a clear set of predictions: whether a social trait is favoured or disfavoured is determined by how the social action alters the recipient’s life cycle . If the social action alters the recipient’s death rate , then altruism is favoured and spite disfavoured . If instead the social action alters the recipient’s birth rate , then both altruism and spite can be either favoured or disfavoured—the precise outcome depends upon the ratio of the population turnover rate to the population size . | [
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] | 2019 | Social evolution under demographic stochasticity |
Elucidating the relationships between antimicrobial resistance and virulence is key to understanding the evolution and population dynamics of resistant pathogens . Here , we show that the susceptibility of the gram-positive bacterium Listeria monocytogenes to the antibiotic fosfomycin is a complex trait involving interactions between resistance and virulence genes and the environment . We found that a FosX enzyme encoded in the listerial core genome confers intrinsic fosfomycin resistance to both pathogenic and non-pathogenic Listeria spp . However , in the genomic context of the pathogenic L . monocytogenes , FosX-mediated resistance is epistatically suppressed by two members of the PrfA virulence regulon , hpt and prfA , which upon activation by host signals induce increased fosfomycin influx into the bacterial cell . Consequently , in infection conditions , most L . monocytogenes isolates become susceptible to fosfomycin despite possessing a gene that confers high-level resistance to the drug . Our study establishes the molecular basis of an epistatic interaction between virulence and resistance genes controlling bacterial susceptibility to an antibiotic . The reported findings provide the rationale for the introduction of fosfomycin in the treatment of Listeria infections even though these bacteria are intrinsically resistant to the antibiotic in vitro .
The facultative intracellular pathogen Listeria monocytogenes is the causative agent of listeriosis , a foodborne infection characterized by severe clinical manifestations including meningoencephalitis , bacteremia , miscarriage and neonatal sepsis or meningitis [1–3] . The pathogenesis of listeriosis relies on a group of virulence genes that are co-ordinately regulated by the PrfA transcriptional activator [4] . PrfA-regulated genes are selectively induced within host cells through a mechanism involving cofactor-mediated allosteric switching of PrfA between weakly active ( “Off” ) and strongly active ( “On” ) states [5 , 6] . PrfA regulation is both essential for the activation of the listerial virulence program within the host and for preventing the costly production of unneeded virulence factors when L . monocytogenes is living as an environmental saprotroph [7 , 8] . Listeriosis is the foodborne infection with the highest mortality in the Western hemisphere despite hospital-based therapy ( 20–50% ) [2] . This is partly attributable to the intracellular lifestyle of L . monocytogenes and the location of lesions , e . g . the brain , which render these bacteria relatively inaccessible to drugs thereby limiting the therapeutic choices [9 , 10] . Cell-permeant antimicrobials able to penetrate the blood-brain barrier ( BBB ) and other listerial infection sites at bactericidal concentrations may therefore significantly aid in the treatment of listeriosis . Previous work from our laboratory identified fosfomycin ( disodium salt for parenteral use ) [11–13] as one such potentially useful anti-listerial drugs . Fosfomycin [ ( 1R , 2S ) -epoxypropylphosphonic acid] is a low-molecular-weight bactericidal molecule discovered in 1969 in Streptomyces fradiae [14] that inhibits peptidoglycan biosynthesis through covalent inactivation of UDP-N-acetylglucosamine-3-enolpyruvyl transferase ( MurA ) [11] . Although known to be resistant to fosfomycin by standard in vitro testing [9 , 15 , 16] , we found that L . monocytogenes was actually susceptible to this antibiotic in infected cells and in vivo in mice [17] . The efficacy of fosfomycin against intracellular L . monocytogenes was independently confirmed by others [18] . The basis of this in vitro-in vivo paradox is the PrfA-regulated expression of the listerial sugar phosphate permease Hpt , a homolog of the enterobacterial hexose phosphate transporter UhpT that also transports fosfomycin [17] . Hpt is a virulence factor that promotes rapid replication in the cytosol by allowing bacterial utilization of host-cell hexose phosphates as a carbon source [19] . However , Hpt remains unexpressed outside the host due to PrfA On-Off switching [17 , 20] , preventing Hpt-mediated fosfomycin import into the listerial cell [17] . Importantly , we also showed that L . monocytogenes spontaneous fosfomycin resistance was mostly due to mutations in the prfA ( 56% ) or hpt ( 41% ) genes [17] . Since prfA is essential for pathogenesis [6 , 21] and hpt is required for full in vivo virulence [19] , L . monocytogenes fosfomycin resistant mutants were consequently found to be counterselected in infected macrophages [17] . Despite the above evidence , a potential obstacle for the clinical use of fosfomycin in the treatment of listeriosis is the reported presence in L . monocytogenes of a fosfomycin hydrolyzing enzyme , FosX [22] . The fosX gene was originally discovered in the soil bacteria Mesorhizobium loti and Desulfitobacterium hafniense and in the L . monocytogenes reference genome strain EGDe ( lmo1702 ) by in silico mining for homologs of the fosfomycin resistance proteins FosA and FosB [23] . However , the actual distribution of fosX , and whether this gene actually confers fosfomycin resistance in L . monocytogenes had not been established . In this study , we show that fosX is a core trait of the Listeria genus that confers high levels of resistance to fosfomycin unless epistatically controlled , in the pathogenic species L . monocytogenes , by members of the in vivo-activated PrfA virulence regulon . Our work demonstrates that epistatic interactions between virulence and resistance genes can have dramatic effects on the antimicrobial susceptibility phenotype of bacterial pathogens .
Analysis of a collection of genomic sequences from 1 , 696 L . monocytogenes isolates from 13 countries , representing the four lineages of the species , 164 sublineages and 1 , 013 core-genome MLST subtypes [24] , showed that the fosX gene is universally conserved in L . monocytogenes ( Fig 1A ) . L . monocytogenes strains encoded a 133-residue FosX protein between 92 and 100% identical to the product of the 402-bp fosX gene of strain EGDe [23] ( S1 Table ) . No other putative fosfomycin resistance enzyme genes were identified in L . monocytogenes . FosX orthologs were also encoded in Listeria innocua , Listeria welshimeri ( 89% identity ) , Listeria marthii ( 91% identity ) and Listeria ivanovii ( 73% identity ) ( S1 Table ) . These species belong together with L . monocytogenes to one of the main phylogenetic subdivisions of the genus , clade ( i ) or Listeria “sensu stricto” [25] . With the exception of Listeria seeligeri , in which the gene appears to have been lost , fosX was present at the same chromosomal location in all members of the Listeria sensu stricto clade ( Fig 1C ) . This , and the fact that a phylogenetic tree based on the FosX protein sequence closely mirrored the genus’ phylogenomic structure ( Fig 1B ) , indicated that fosX is an ancient Listeria trait that evolved with the core genome of these bacteria . To investigate the role of fosX in the L . monocytogenes fosfomycin phenotype , we constructed an in-frame deletion mutant in L . monocytogenes P14 , a serovar 4b human clinical isolate ( S2 Table ) . The fosX null mutation caused a strong reduction in the fosfomycin minimum inhibitory concentration ( MIC ) in brain-heart infusion ( BHI ) , from ≥1024 of the parental P14 to 45 . 3 μg/ml ( P < 0 . 0001 ) ( Fig 2 , left panel ) . Knocking out fosX in the representative non-pathogenic species L . innocua , which like L . monocytogenes shows intrinsic fosfomycin resistance , had the same effect ( Fig 3 ) . Complementation of P14ΔfosX with a single copy of fosX expressed from its natural promoter ( S1 Fig ) restored wild-type MIC levels ( Fig 2 , left panel ) , demonstrating that FosX confers strong resistance to fosfomycin on Listeria . The distribution and high degree of conservation of fosX among pathogenic and obligate saprotrophic Listeria spp . could be linked to the resistance phenotype , or might be related to other potential roles of the FosX enzyme in listerial physiology , as suggested for the components of the bacterial intrinsic resistomes [26–28] . For example , FosX from the soil bacterium M . loti is catalytically promiscuous , has a comparatively low capacity to hydrolyze fosfomycin , and is probably primarily involved in rhizobial metabolism , as indicated by the presence of the coding gene in a phn operon presumably involved in transport and utilization of phosphonate [22 , 23] . We investigated possible homeostatic roles of Listeria FosX using competition experiments between wild-type and ΔfosX L . monocytogenes in broth medium and in infected macrophages in the absence of fosfomycin pressure . FosX was in both cases fitness neutral ( Fig 4 ) , indicating that it has no significant housekeeping function , at least in our experimental conditions . Interestingly , L . monocytogenes transcription start site mapping data [29] indicate that fosX ( lmo1702 ) is co-expressed with the upstream gene lmo1703 encoding a putative TrmA superfamily SAM-dependent RNA methyltransferase similar to RlmD/RumA [30] ( S1 Fig ) . This gene arrangement is unique to Listeria; in fact , fosX genes are found in completely different genetic environments in each of the bacterial taxa that carry this determinant ( S2 Fig ) . Modification of specific rRNA nucleotides by methyltransferases plays a critical role in ribosomal function regulation and is also a well-known mechanism conferring resistance to ribosomal antibiotics [31–33] . Specifically , mutagenesis studies of the RmlD/RumA uridine 1939 target in the Escherichia coli 23S ribosomal subunit demonstrated altered susceptibility patterns to antibiotics that affect protein synthesis [34] . Fosfomycin is produced by several species of the ubiquitous environmental microbes Streptomyces and Pseudomonas [11 , 14] , which Listeria spp . are likely to encounter in the natural habitat . It is tempting to speculate that fosX and the conserved adjacent rmlD/rumA-like homolog found in Listeria spp . form a “chromosomal resistance” island conferring simultaneous protection against microbially derived fosfomycin and ribosome-targeting secondary metabolites . It cannot be excluded , however , that the enzymatic activity of FosX may play a more general role in bacterial physiology by mediating epoxide ring hydrolysis ( 23 ) in some catabolic processes relevant to Listeria . The fosfomycin susceptibility of P14ΔfosX in BHI raises the question of how fosfomycin might enter the listerial cell at inhibitory concentrations in conditions where PrfA is “Off” and Hpt is completely downregulated [17] . A double ΔfosXΔhpt mutant had the same MIC as the fosX mutant ( P = 0 . 399 ) ( Fig 2 , left panel ) , excluding leaky expression of the Hpt transporter as the cause . Although a very small molecule ( 138 Da ) , fosfomycin is hydrophilic and unlikely to permeate into the bacterial cell unless through facilitated diffusion via a carrier protein ( s ) . The only known bacterial fosfomycin transporters are two types of organophosphate:inorganic phosphate antiporters ( OPA ) [35] , exemplified by the hexose phosphate transporter UhpT ( and listerial homologue Hpt ) and the GlpT glycerol-3-phosphate permease [11 , 36] . Genome searches confirmed that Hpt is the only OPA permease in Listeria spp . This indicates that the susceptibility of the L . monocytogenes ΔfosXΔhpt mutant ( and the L . innocua fosX mutant ) must depend on another , as yet unknown fosfomycin uptake pathway ( Fig 5 ) . We suggest that the selective pressure imposed by this uncharacterized transport mechanism is a major driver underlying fosX acquisition and maintenance in Listeria . We tested the effect of fosX when Hpt-mediated fosfomycin transport is active using two “infection-mimicking” in vitro conditions: ( i ) supplementation of BHI medium with an adsorbent ( activated charcoal or Amberlite XAD-4 ) , which causes the partial activation of the PrfA regulation system by an as yet not fully understood mechanism [37]; and ( ii ) use of a constitutively activated prfA* allele , where a single amino acid substitution ( e . g . PrfA*G145S ) locks PrfA in “On” state , causing constitutive activation of the PrfA-regulated virulence genes [38] . A strong reduction in the fosfomycin MIC is typically observed with each of these two PrfA-activating strategies [17] ( from ≥1024 to 27 . 3±5 . 3 and 12±0 μg/ml , respectively; P < 0 . 0001 ) ( Fig 2 , middle and right panels ) . In these conditions , the fosX mutation lowered further the fosfomycin MIC to virtually complete susceptibility ( 2 . 2±0 . 4 and 1 . 5±0 . 4 μg/ml , respectively ) ( P = 0 . 01 ) . Complementation of the ΔfosX mutant restored the MIC to parental levels ( Fig 2 , middle and right panels ) . As expected [17] , deletion of hpt or its transcriptional activator gene prfA rendered L . monocytogenes resistant to fosfomycin ( MIC >1 , 024 μg/ml ) ( Fig 2 , middle and right panels ) but had no effect on bacteria with a wild-type prfA allele in BHI ( where PrfA is “Off” and hpt is not expressed ) ( Fig 2 , left panel ) . Ablation of Hpt function in the FosX−background under conditions of PrfA activation raised the MIC , from 2 . 2±0 . 4 to 36 . 8±7 . 0 μg/ml ( charcoal-supplemented BHI [BHI-Ads] ) or 1 . 5±0 . 4 to 35 . 2±6 . 4 μg/ml ( prfA* allele ) ( P <0 . 001 ) . These higher MIC values were similar to those for ΔfosX ( or the double mutant ΔfosXΔhpt ) in BHI ( 45 . 3±2 . 7 μg ml/ml and 41 . 6±3 . 9 , respectively ) ( Fig 2 ) . Overall , the above findings are consistent with a scenario where FosX: ( i ) can successfully inactivate the amounts of fosfomycin that enter the bacterial cell via the uncharacterized “constitutive” transport system , conferring complete resistance in in vitro ( saprophytic ) conditions; but ( ii ) is unable to process an increased influx of fosfomycin molecules via the Hpt transporter in PrfA-activating ( infection ) conditions ( Fig 5 ) . In other words , our data show that the intrinsic resistance conferred by the fosX gene is masked , or epistatically supressed , by the joint effect of the virulence genes prfA and hpt on fosfomycin transport . We next assessed the extent to which the epistatic effect that cancels fosX-mediated resistance manifests during infection , where prfA/hpt are naturally induced [5 , 6 , 39–41] . To this end , the intracellular susceptibilities of wild-type L . monocytogenes P14 and isogenic ΔfosX derivative ( and Δhpt mutant as a control ) were compared in survival/proliferation assays in infected RAW 264 . 7 macrophages in the presence and absence of fosfomycin . Cell cultures were incubated with 5× the physiological concentration of D-glucose as in these conditions listerial intracellular growth is independent of Hpt-mediated hexose phosphate uptake [17] . As shown in Fig 6 , both wild-type and ΔfosX L . monocytogenes were equally susceptible to fosfomycin during intracellular infection ( P = 0 . 996 ) . In contrast , fosfomycin had in these conditions no effect on the Δhpt mutant with disabled Hpt transport . Identical results were obtained using the human epithelial cell line HeLa ( Fig 6 ) . These data confirmed that L . monocytogenes is fully susceptible to fosfomycin in infection conditions , specifically as a consequence of the epistatic supression of fosX-mediated resistance by the PrfA-regulated ( in vivo-activated ) hpt gene . While our data are consistent with the loss of fosX-mediated intrinsic resistance under PrfA induction ( infection ) conditions being primarily due to increased fosfomycin influx via Hpt ( Fig 5 ) , potential effects of PrfA ( or the intracellular milieu ) on fosX expression could also be a contributing factor . To explore this possibility , fosX transcription was analysed by RT-QPCR in BHI and in PrfA-activating conditions in vitro ( adsorbent-treated medium , prfA* allele; see above ) or during intracellular infection . All three PrfA-inducing conditions caused the expected transcriptional activation of the PrfA-regulated hpt and ( control ) actA genes [5 , 42] , with no significant changes in fosX expression ( P = 0 . 615 ) ( Fig 7 ) . These data excluded a potential involvement of reduced expression of fosX in the susceptibility phenotype elicited by PrfA activation . To establish whether the limited effect of fosX when the PrfA system is “On” and Hpt is expressed is a general feature of the L . monocytogenes species , the fosfomycin MIC of 142 wild-type isolates was tested in BHI and BHI-Ads . The MIC50 and MIC90 values shifted from ≥1 , 024 μg/ml in BHI to 16 and 64 μg/ml , respectively , in BHI-Ads ( Fig 8A ) . Thus , despite fosX , in conditions of PrfA activation the fosfomycin MIC remained within the limits of susceptibility for the vast majority of the tested strains ( 90 . 33% with 64 μg/ml PK/PD breakpoint [18]; 80 . 64% with 32 μg/ml general fosfomycin breakpoint for gram-positive bacteria [43] ) . It must be noted that adsorbents only partially activate PrfA ( [41] and Fig 7 ) , and significantly lower fosfomycin MICs ( median 3 μg/ml , range 2–16 ) are observed in L . monocytogenes prfA*G145S bacteria with the PrfA system constitutively activated to in vivo ( within-host ) -like levels [17 , 41] ( see Fig 2 , right panel ) . To determine if the above findings can be extrapolated to infection conditions , the intracellular susceptibility of a selection of L . monocytogenes strains with “normal” ( i . e . adsorbent-activable ) fosfomycin phenotype was analysed in RAW 264 . 7 macrophages . The tested bacteria included eight wild-type human clinical isolates plus the well-characterized strains EGDe ( serovar 1/2a ) , 10403 ( serovar 1/2a ) and CLIP 80459 ( serovar 4b ) . In addition , we also tested a representation ( n = 10 ) of the small proportion of isolates where the fosfomycin MICs remained relatively elevated ( 96–384 μg/ml ) despite being BHI-Ads responsive , to determine if this correlated with differences in intracellular susceptibility . Fig 8B shows that all tested strains were equally susceptible to fosfomycin in infected macrophages ( P = 0 . 632 ) . These data confirmed that L . monocytogenes isolates are characteristically susceptible to fosfomycin during infection , even if the MIC remains above the 64-μg/ml breakpoint as long as they exhibit the capacity to respond to PrfA-activating conditions ( as tested in BHI-Ads medium ) . A percentage of L . monocytogenes clinical isolates exhibit constitutively low fosfomycin MICs under normal in vitro testing conditions [16] ( about 2 to 4 . 5%; data from L . monocytogenes antibiotic susceptibility surveillance at Institut Pasteur’s National and WHO Collaborating Reference Centre for Listeria and ref . [18] ) . We examined nine human isolates carrying a fosX gene but presenting a fosfomycin MIC ≤64 μg/ml in PrfA-non-inducing conditions ( normal BHI ) to determine the underlying mechanism ( S3 Table ) . All displayed a wild-type PrfA phenotype ( see Materials and Methods ) , excluding possible spontaneous prfA* ( hpt-activating ) mutations as the cause for their constitutive in vitro fosfomycin susceptibility [17] . Consistent with the key role of the FosX enzyme in the intrinsic in vitro resistance of L . monocytogenes to fosfomycin , eight of the nine strains analyzed carried fosX mutations ( S3 Table ) . The only isolate with wild-type FosX gave a “slow-positive” sugar-phosphate acidification test ( S3 Table ) , pointing to an increased Hpt activity as the cause . However , no differences in hpt gene expression ( S3 Fig ) or in the DNA sequence of the hpt region that could explain the Hpt ( + ) phenotype ( S3 Table ) were identified . Seven of the mutants had a premature stop codon at fosX triplet 128 , leading to a truncated product where the lack of the six C-terminal residues most likely destabilizes FosX's catalytic site [22] ( S4 Fig ) . The other fosX mutant carried a frameshift mutation at position 88 that introduced premature stop codons from position 89 . Complementation analysis in P14ΔfosX confirmed that the fosX128stop and fosX88frameshift mutant alleles did not encode active FosX enzymes ( BHI MICs: 48 and 32 μg/ml , respectively instead of ≥1 , 024 with wild-type fosX ) . As expected , similar to the P14ΔfosX ( Fig 6 ) , all the spontaneous fosX mutants showed complete susceptibility to fosfomycin in infected host cells ( Fig 8B ) . We finally examined the impact of the potential occurrence of fosX overexpression mutants on the L . monocytogenes fosfomycin phenotype . To approximate this , we expressed the fosX gene in P14ΔfosX under the control of a strong constitutive gram-positive promoter ( Pδ [44] ) . As shown in Fig 9A , Pδ drove fosX expression to significantly higher levels compared to an equivalent construct with the native Plmo1703 promoter from which fosX is expressed in L . monocytogenes [29] . Fosfomycin susceptibility of the two constructs and mock-complemented ΔfosX ( control ) was analyzed in vitro in BHI ( PrfA”Off” ) and BHI-Ads as well as infected RAW 264 . 7 macrophages ( PrfA “On” ) . The data show that overexpression of fosX neither modified the in vitro fosfomycin MIC ( specifically in BHI-Ads , P = 0 . 999 ) ( Fig 9B ) nor significantly affected bacterial susceptibility in infection conditions ( percent reduction of intracellular population respect to untreated control between 99 . 97 and 99 . 99% for the three strains ) ( P = 0 . 115 ) ( Fig 9C ) . Thus , promoter mutations leading to increased fosX expression are unlikely to result in adaptive fosfomycin resistance due to the epistatic effect of prfA/hpt taking prevalence even when fosX is overexpressed . Why fosX overexpression does not result in increased levels of fosfomycin resistance may be related to a variety of reasons , including gene post-transcriptional control or stoichiometric limitations to enzyme activity , and remains to be investigated .
Experimental studies showing that antimicrobial resistance may not only carry fitness costs but also enhance bacterial performance in vivo [45 , 46] have prompted a renewed interest in understanding the complex relationships between resistance and virulence [47–49] . A direct link is obvious when the corresponding determinants are carried on the same mobile genetic element [47 , 50 , 51] . Other illustrative examples include the efflux pumps that serve a dual role in both resistance and virulence [52 , 53] , or when there is co-ordinate modulation or crosstalk between resistance and virulence gene regulatory networks [49 , 51 , 54 , 55] . Most often , however , the connection is subtler , detected at population level through epidemiological [56–58] or genome-wide co-evolution studies [59] , likely involving gene-gene interactions [47 , 56] , and little is known about the precise underlying molecular mechanisms [48] . In this study , we report a compelling example of gene interaction where the resistance phenotype conferred by a Listeria core genome trait is modified by virulence genes specifically present in the genomic background of the pathogenic species L . monocytogenes , strongly affecting the susceptibility to an antibiotic . Independently , the loci involved , the fosfomycin resistance gene fosX and the virulence genes hpt and prfA , are bacterial performance enhancers in the specific conditions for which they presumably evolved in Listeria , i . e . exposure to naturally occurring phosphonic acid metabolites or the intracellular compartment of an animal host , respectively . However , in the ( man-made ) event of L . monocytogenes simultaneously confronting fosfomycin and the host , the effect of prfA and hpt is dominantly deleterious and overrides the beneficial effect of fosX ( Fig 5 ) . In such circumstances , hpt and its regulatory gene prfA “stand above” and “stop” the fosfomycin resistance phenotype conferred by fosX in an archetypal example of epistasis [60 , 61] , to our knowledge the first to be dissected in molecular mechanistic detail between virulence and resistance genes in a bacterial pathogen . Gene-gene epistatic interactions are thought to play a critical role in modulating the phenotypic expression of antibiotic resistance and its impact on microbial fitness , and thereby in the evolution of resistance [62–67] . Our findings extend this notion to the relationships between resistance and virulence , two key specific , clinically relevant pathogen traits . In the particular case described herein , the characterized virulence-resistance gene interplay renders L . monocytogenes susceptible to fosfomycin in vivo during infection although carrying the fosX gene , which otherwise confers high levels of resistance to the antibiotic . Our data indicate that , due to the epistatic effect of the virulence genes prfA and hpt ( which cannot be reversed by fosX overexpression ) , or even naturally occurring spontaneous fosX mutations , the vast majority of L . monocytogenes strains are expected to be fully susceptible to fosfomycin in clinical conditions . Together with our earlier finding that acquired fosfomycin resistance is mostly due to mutations in the virulence genes hpt and prfA , and hence counterselected during infection [17] , this report provides the rationale underpinning the use of fosfomycin against a nominally intrinsically resistant L . monocytogenes . The currently recommended treatment for listeriosis consists in a prolonged course of amoxicillin or ampicillin at high doses , often in combination with gentamicin [1 , 2 , 9 , 68] . However , aminopenicillins cannot be administered in case of allergy to β-lactams , and gentamicin has a poor intracellular penetration , does not cross the BBB efficiently , and is nephrotoxic or ototoxic [10 , 69 , 70] . Due to its bactericidal activity and synergistic action with many antimicrobials including β-lactams , aminoglycosides , meropenem , linezolid , daptomycin and vancomycin , the “old” antibiotic fosfomycin [14] is currently resurging as a therapeutic option for the treatment of critically ill patients with invasive or systemic bacterial infections [12 , 13 , 71] . Rapid , effective bactericidal action is paramount in neuromeningeal , bacteremic or materno-fetal listeriosis to limit disease severity , relapses and fatal outcomes [2 , 9 , 70 , 72 , 73] . With a well-established safety record , low plasma protein binding , excellent intracellular penetration , and superior entry through the BBB and placental barrier than β-lactams [11–13 , 74] , intravenous fosfomycin may prove highly beneficial in the combination therapy of listeriosis . Beyond its fundamental implications , the mechanistic characterization of the epistatic interaction between resistance and virulence genes reported here has , therefore , a potential direct application in the treatment of a life-threatening infectious disease .
The bacterial strains used in this study are listed in S2 and S3 Tables . L . monocytogenes isolates in Fig 8 are of diverse origins ( clinical human and animal , food , environment ) and phylogenomic clades , including lineages I , II and III ) ; they were sourced from Institut Pasteur’s Listeria collection or JV-B laboratory’s isolate collection . Listeria were grown in Brain-Heart Infusion ( BHI , Porcine , BD Difco ) and Escherichia coli in Luria-Bertani ( LB , Sigma ) media ( supplemented with 1% agar w/v and/or antibiotics as appropriate ) at 37 °C unless otherwise stated . The PrfA regulon was activated in vitro by supplementing BHI with the adsorbents , activated charcoal ( 0 . 5% w/v , Merck ) or Amberlite XAD-4 ( 1% w/v , Sigma ) , as previously described [37] ( referred to as “BHI-Ads” ) . The L . monocytogenes genome dataset ( n = 1 , 696 sequences ) was scanned for the presence of fosX ( lmo1702 ) using the BLASTn algorithm [75] implemented in BIGSdb v . 1 . 16 platform [76] as described in ref . [24] , with minimum nucleotide identity of 70% , alignment length coverage of 70% and word size of 10 . General homology searches were carried out using BLASTP with default parameters . L . monocytogenes P14 fosX deletions were made by allelic exchange using the thermosensitive shuttle vector pMAD [77] as previously described [7] . A DNA fragment containing an in-frame ΔfosX allele comprising the last five 5’- and 3’-terminal codons of the wild-type gene was prepared by splicing overlap extension PCR using oligonucleotides fosXDM-1 NcoI and fosXDM-4 BamHI ( S4 Table ) , as previously described [5] . The PCR product was inserted into pMAD using the NcoI and BamHI restriction sites and the resulting pMADΔfosX plasmid ( S2 Table ) was transformed into L . monocytogenes . Allelic exchange was selected at the vector-non-permissive temperature of 42 °C as described in [19] and verified by PCR and DNA sequencing . For complementation , a single copy of the fosX gene from P14 was inserted into the L . monocytogenes chromosome under the control of its native Plmo1703 promoter [29] ( S1 Fig ) using the integrative vector pPL2 [78] ( S2 Table ) . The Plmo1703:fosXP14 fusion was constructed by ligating two PCR products , one containing the 5’ UTR region of lmo1703 including the promoter , the other the coding sequence of fosX from strain P14 , using the NdeI site included in the 5’ tails of oligonucleotides Plmo1703-NdeI-R and fosX-ATG-NdeI-F ( S4 Table ) . The resulting ligation product was inserted in the multicloning site ( MCS ) of pPL2 using SacI-SalI sites ( plasmid pPLPlmo1703:fosXP14 , S2 Table ) . For fosX overexpression , the fosX gene plus 45 bp upstream was placed under the control of the strong constitutive Pδ promoter from the streptococcal pSM19035 plasmid partitioning gene δ [44] and inserted into pPL2’s MCS using SpeI-BamHI sites ( plasmid pPLPδ:fosXP14 , S2 Table ) . pPL2 integrants were selected in BHI containing 7 . 5 μg/ml chloramphenicol and confirmed by PCR and DNA sequencing . The L . innocua fosX gene was knocked out by plasmid insertional mutagenesis . A 363-bp internal fosX gene fragment was PCR-amplified ( see oligonucleotides in S4 Table ) and ligated into the BamHI-EcoRI sites of the thermosensitive vector pLSV1 [79] . The resultant pLSV-fosXLi plasmid was introduced into L . innocua CLIP11262 ( S2 Table ) and the recombinant clones were selected at 42 °C in BHI containing 5 μg/ml erythromycin . L . innocua wild-type fosX revertants were obtained by serial passage in BHI without erythromycin and selected on 150 μg/ml fosfomycin , a concentration that is inhibitory when the fosX gene is inactivated ( see Fig 2 ) . Correct plasmid insertion and subsequent plasmid loss and restoration of the wild-type fosX allele was confirmed by PCR and DNA sequencing . Restriction enzymes were obtained from New England Biolab and high-fidelity PfuUltra II Hotsart DNA polymerase from Agilent . Fosfomycin MICs were determined by the Etest strip method ( bioMérieux ) after 24 h incubation at 37 °C as previously described [17] . The functional status of PrfA was determined using the activities of three PrfA-regulated virulence determinants used as reporters ( hly encoding the hemolysin listeriolysin O [LLO] , plcB encoding the phosphatidyl-choline preferring phospholipase C/lecithinase PlcB , and hpt encoding the Hpt sugar phosphate permease ) , as previously described [7 , 17 , 37] . Briefly , LLO activity was determined by the halo of hemolysis around Listeria colonies in sheep blood agar ( SBA , bioMérieux ) ; PlcB activity was determined by the halo of precipitation in BHI supplemented with 10% of an egg yolk suspension , prepared by dispersing one egg yolk in 100 ml of sterile saline solution; and Hpt activity was determined using a sugar acidification test in phenol red base broth ( Oxoid ) supplemented with 10 mM glucose-1-phosphate after 24 h incubation ( G1P , Sigma ) . PlcB and Hpt tests were carried out also with and without supplementation with 0 . 5% activated charcoal . Using this panel of tests , the L . monocytogenes wild-type phenotype is characterized by: ( i ) weak hemolysis in SBA ( confined to the area beneath the colonies ) , ( ii ) negative lecithinase reaction and G1P utilization in non-charcoal-supplemented media; and ( iii ) strong lecithinase and G1P acidification in charcoal-supplemented medium . A PrfA* phenotype is characterized by ( i ) strong hemolysis in SBA and ( ii ) strong lecithinase and positive G1P in non-charcoal-supplemented medium . Inocula for the competition assays were prepared from L . monocytogenes overnight BHI cultures shaken at 200 rpm until OD600 ≈1 . Bacteria were collected by centrifugation , washed twice in PBS , suspended in 10% glycerol PBS and stored at -80°C in 100μl aliquots . Viable numbers of each P14 and P14ΔfosX strains in the frozen alliquots were determined by plate counting and suitable amounts mixed to prepare a 1:1 suspension . For the in vitro competition assays in BHI , five ml of fresh culture medium were seeded with 40 μl of the 1:1 suspension of P14 and P14ΔfosX ( ≈2×106 CFU/ml total bacteria ) and growth was monitored during incubation with orbital shaking ( 200 rpm ) using an automated plate reader ( Omega apparatus , BMG Labtech ) , as previously described [7] . For assays in infected cells , mycoplasma-free RAW 264 . 7 mouse macrophage monolayers ( sourced from ATCC ) grown in Dulbecco modified Eagle medium supplemented with 10% fetal bovine serum ( Gibco ) ( DMEM ) were inoculated with the 1:1 bacterial suspension at a multiplicity of infection of 10 and intracellular bacterial proliferation monitored using a gentamicin protection assay , as previously described [5] . The competing wild-type and ΔfosX bacteria were enumerated by differential plating in BHI and BHI supplemented with 100 μg/ml fosfomycin ( inhibitory for L . monocytogenes ΔfosX , MIC 45 . 3 μg/ml; Fig 2 ) . The genotype of the bacterial strains was further confirmed by PCR in 50 randomly selected colonies using oligonucleotides fosXDM-5 and fosXDM-6 ( S4 Table ) . The competitive index ( CI ) was calculated using the formula CI = ( test/reference log CFU ratio at t = n ) / ( test/reference log CFU ratio at t = 0 ) . The effect of fosfomycin on intracellular L . monocytogenes was determined as previously described [17] . Briefly , 80–90% confluent monolayers of RAW 264 . 7 macrophages or HeLa epithelial cells ( ATCC ) were infected at 10:1 or 25:1 multiplicity , respectively , and centrifuged at 900 rpm to synchronize infection . After 30 min of incubation , infected monolayers were washed with PBS to remove extracellular bacteria and incubated in DMEM containing 100 μg/ml gentamicin for 30 min ( t = 0 ) . At 40 min after t = 0 , the medium was changed to 25 μg/ml gentamicin with or without 180 μg/ml fosfomycin . Total L . monocytogenes RNA was extracted from mid-exponential cultures ( OD600≈ 0 . 2–0 . 3 for BHI media ) or intracellular bacteria ( t = 4 h ) using RNease mini kit ( Qiagen ) . RNA samples were reverse transcribed using ImProm-IITM reverse transcription system ( Promega ) and specific cDNAs quantified by real-time PCR ( RT-QPCR ) as previously described [5] using Step One Plus real-time PCR apparatus and Step One V2 . 3 software ( Applied Biosystems ) . The PCR signal was monitored using TaqMan probes for the PrfA-regulated genes hpt and actA , and Power SYBR Green master mix ( Applied Biosystems ) and gene-specific primers for fosX . Transcription values of the target genes were normalized using the housekeeping genes rpoB and ldh . Fold-changes in fosX expression were determined by the 2–ΔΔCT method . The oligonucleotides used are shown in S4 Table . Statistical significance was analyzed using Prism 7 . 0 software ( GraphPad Software Inc . , San Diego , CA ) . The specific tests used are indicated in the figure legends . Sequence data obtained in this study are available from the European Nucleotide Archive ( ENA ) under accession no . LT795753 , LT795754 , LT795755 , LT795756 , LT795757 , LT795758 , LT795759 , LT795760 , LT795761 , LT795762 . | Epistasis , or interactions between genes , is the phenomenon where the phenotypic effect of a locus is altered or masked by other loci in a given genomic context . Working with Listeria bacteria , we show that the effect of an intrinsic resistance determinant that protects these organisms against fosfomycin , a natural , microbial-derived antibiotic , is epistatically cancelled by virulence determinants present in the pathogenic species , L . monocytogenes . Since these virulence determinants are specifically activated within the host , the epistatic effect only manifests during infection , not when the bacteria are living saprophytically . Our study dissects the underlying mechanism , substantiating at the molecular level that virulence and resistance can be closely intertwined via gene-gene epistatic phenomena , with strong effects on the antimicrobial susceptibility phenotype . The findings are significant because any functional interaction between resistance and virulence may inextricably link the evolution of these two key pathogen traits . Understanding in detail these interactions is essential for predicting the evolutionary dynamics of resistance among pathogenic microbes or the impact of antimicrobial policies on drug-resistant virulent strains . In addition to their fundamental interest , our study provides the science-based evidence needed for the use of fosfomycin to treat listeriosis , a severe foodborne infectious disease , despite the causative bacteria showing strong resistance to the antibiotic . | [
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] | 2018 | Epistatic control of intrinsic resistance by virulence genes in Listeria |
The mosquito Aedes aegypti is the primary vector of human arboviral diseases caused by dengue , chikungunya and Zika viruses . Many studies have shown the potential roles of small RNA molecules such as microRNA , small interfering RNA and PIWI-interacting RNA in vector mosquitoes . The function of tRNA fragments ( tRF ) , the newly discovered class of small RNAs , in mosquitoes is not known . In this study , we show that specific tRFs are expressed in significantly differential manner between males and females of Ae . aegypti strains . Specific tRFs also show differential response during developmental transition from larvae to adults , as well as after blood feeding of adult females . The expression pattern of tRFs upon blood feeding varied depending upon if the blood contained dengue virus , and also if the females were treated with antibiotic prior to feeding to cleanse of the gut bacteria . Our findings show that a single tRF derived from the precursor sequences of a tRNA-Gly was differentially expressed between males and females , developmental transitions and also upon blood feeding by females of two laboratory strains that vary in midgut susceptibility to dengue virus infection . The multifaceted functional implications of this specific tRF suggest that biogenesis of small regulatory molecules from a tRNA can have wide ranging effects on key aspects of Ae . aegypti vector biology .
Endogenously expressed small regulatory RNAs play diverse biological roles in vector mosquitoes [1–5] . They function in regulating processes that relate to mosquito development , blood digestion , disease vector competence and others [6–10] . More recently , studies have shown that fragments endogenously generated from transfer RNAs , referred to as ‘tRNA-fragment’ ( tRF ) , play active roles in various biological functions in diverse organisms [11–14] . The tRFs are produced from either mature tRNAs or their precursor transcripts , not as random degradation products , but by cleavage at specific sites by specific ribonucleases whose precise mechanisms are not fully understood [15] . While tRFs ranges from 13 to 32 nucleotides ( nt ) in length , relatively longer fragments ( 30–35 nt ) known as ‘tRNA halves’ are also produced as functional molecules from mature tRNAs under certain conditions [16] . The functions of tRFs are multifaceted: they regulate diverse target genes like microRNAs , have association with human cancer and other diseases , confound cell viability , influence RNA stability and degradation , affect sperm maturation and fertilization , and compromise translational selection of genes [17–21] . In insects , limited research has been performed on tRFs . In Drosophila , a role for tRFs in developmental regulation has been shown [22] . In the silk worm , tRFs have been mapped to the D-loop , anticodon and TψC loop of different tRNAs [23] . In the green-bottle blowfly , small RNAs mapping to the 5’ end of mature tRNA Gly-GCC have been reported [24] . Despite the increased research interest on identifying and curating tRNA-derived small RNAs in different species [25] , no study on tRFs has been reported in mosquitoes . In this study , we investigated tRF abundance in a genome-wide manner in the mosquito Aedes aegypti that acts as the primary global vector of different arboviral diseases of humans [26–29] . The primary objective of this study is to determine if tRFs are expressed and differentially regulated in Ae . aegypti . Towards achieving that broad objective , we profiled tRF expression in different biological samples that varied in sex , developmental stage and treatments ( such as antibiotic , blood feeding or oral challenge of dengue virus ) . Furthermore , we generated these biological samples from two laboratory strains of Ae . aegypti , Moyo-S and Moyo-R ( see Methods ) , in order to compare tRF expression between the strains for different biological samples . In addition , we generated tRF expression profiles in males and females of additional three strains to investigate if tRF regulation is sex-biased among the strains . The results of this study show that different tRFs accumulate in significantly differential abundance at different developmental stages , between sexes , and in response to microbiome perturbation by antibiotic treatment . The study further shows that tRF profiles are altered during post-feeding times with a naïve or dengue virus infected blood meal . Importantly , our data further reveal that a specific tRF is commonly differentially expressed between males vs . females , during development as well as has association with the microbiome and dengue virus infection , suggesting its potential multifaceted functional role in Ae . aegypti .
This study was performed in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The animal use protocol was approved by the University of Notre Dame Institutional Animal Care and Use Committee ( Study # 11–036 ) . In this study , four experiments were conducted ( Fig 1 ) . These experiments were designed to study relevance of tRF expression to sex , development , microbiome and vector response to dengue virus challenge in Ae . aegypti . The two laboratory strains Moyo-S and Moyo-R were used in all the four experiments . They are sub-strains of Moyo-In-Dry strain that were originally selected for differential susceptibility to the avian malaria parasite , Plasmodium gallinaceum [30] , and subsequently determined to show high and low susceptibility to dengue virus respectively [31–32] . To compare tRF expression in males vs . females in Ae . aegypti , we included three additional strains ( Liverpool , Rockefeller and Trinidad ) along with Moyo-S and Moyo-R . The mosquitoes were reared and maintained in environmental chambers following standard conditions [33] . Environmental chambers were held at 28°C , 80% relative humidity , with a 16 h light: 8 h dark cycle , which included a 1-h crepuscular period to simulate dawn and dusk . Unlike the other three experiments where multiple samples representing different developmental stages or treatments were used , this experiment had only two biological samples per sex if Moyo-S and Moyo-R were used alone . So , we included the three additional strains ( Liverpool , Rockefeller and Trinidad ) in order to obtain five biological samples per sex . The Liverpool and Rockefeller strains are long-standing and widely used laboratory strains of Ae . aegypti in the research community . Furthermore , the reference genome sequence of Ae . aegypti was assembled from a highly inbreed sub-strain of the Liverpool strain [34] . The Trinidad strain was initiated from eggs collected in Trinidad , West Indies in 2012 . All the strains were maintained at the University of Notre Dame at the time of conducting this study . Upon adult emergence , equal number of males and females ( n = 6 ) from each strain were pooled for library construction and sequencing . The abundance of tRFs in second , third , fourth instar larvae and adults ( 3-days post-emergence , mixed sex ) of Moyo-S and Moyo-R strains were profiled . For the fourth instar larvae , samples were included from two time points , day 1 and day 2 , the day 2 time being closest to the onset of pupation . Two biological replicates per developmental stage per strain were included in library construction . We conducted two independent experiments to study how tRFs are expressed in Moyo-S and Moyo-R females after blood feeding . In the 1st experiment , we fed Moyo-S and Moyo-R females with either non-infectious blood meal ( naïve blood ) or blood mixed with dengue virus serotype-2 strain JAM1409 ( infectious blood ) and profiled tRF expression at 24 hours and 48 hours post feeding . For mosquito feeding , equal volumes of defibrinated sheep blood ( Colorado Serum Company , CO , USA ) mixed with either uninfected C6/36 cell suspension or dengue virus infected C6/36 cell suspension were used . The freshly made blood meals were orally fed to 3-day old adult females of the two strains . Blood/cell suspensions were warmed to 37°C and aliquoted into glass artificial membrane feeders whose openings were covered with sausage casing . Female mosquitoes were allowed to feed for ~20 minutes . Fully engorged females were separated and maintained at 28°C in 80% relative humidity and provided 5% sucrose solution . Groups of mosquitoes were removed at day1 ( 24 hr post feeding ) and day2 ( 48 hr post feeding ) , and frozen at -80°C until extraction of RNA . Five blood fed mosquitoes from each strain and time point were used for RNA isolation . For sequencing , we used two biological replicates for each post-feeding day per strain per treatment . Thus , total of 16 libraries [2 strains x 2 treatments ( naïve vs DENV ) x 2 post-feeding time x 2 biological replicates] were sequenced for this experiment . In the 2nd experiment , we wanted to profile tRF expression upon blood feeding ( 3 hours ) in females that were cleansed for midgut bacteria relative to control ( fed without cleansing ) . For this experiment , the Moyo-S and Moyo-R pupae were separated into two cages prior to adult emergence . The subsequent steps were followed as described elsewhere [35] . Briefly , after adult emergence , the two cages were provided with different sugar solution treatments . One cage was provided a control sterilized 8% sugar solution , while the other cage was provided an 8% sterilized sugar solution containing 2% penicillin-streptomycin and 0 . 8% gentamicin sulfate for bacterial cleansing [36] . Both sugar treatments were provided ad libitum using saturated sterilized cotton balls . Mosquitoes were allowed 8 days of sugar feeding to assure optimal clearance of midgut bacterial populations among the antibiotic treated samples . To verify bacterial clearance , we followed methods described earlier [35] . The midguts were dissected from 20 random females for each treatment and homogenized in sterilized PBS . Thereafter , 1 . 5 μl aliquots of the midgut solutions were prepared and spread on blood agar plates under sterile conditions . After 3 days , plates were examined for microbial growth . Bacterial clearance was also determined using a culture-independent method utilizing 16S rRNA amplification [35] . The antibiotic treated and untreated females were then starved for 24 hours and then separately provided artificial blood meal prepared using defibrinated sheep blood as described above . Females were allowed ~20 min to feed to engorgement and fully engorged females were isolated in separate cups and maintained at 28°C in 80% relative humidity and provided 5% sucrose solution . Midguts were collected at 3 hr post blood feeding from both groups ( the blood was removed via dissection ) , and stored in RNAlater at -80°C . Three independent biological replicates , each consisting of a pool of midguts from 20 blood fed mosquitoes , were used for library construction . Total RNA was extracted using Qiagen RNeasy mini kit according to manufacturer’s protocol . Quality of RNA was assessed using a Bioanalyzer ( Agilent , Santa Clara , CA , USA ) according to manufacturer’s protocol . Quantity of RNA was determined using Qubit Fluorometer ( Life Technologies , Grand Island , NY , USA ) according to manufacturer’s specifications . Library preparation was conducted using the Epicentre ScriptMiner kit ( Cat . No . SMMP101212 ) following the suppliers protocol . The sequencing was performed using Illumina MiSeq at the Genomics Core Facility at the University of Notre Dame . To identify differentially expressed tRFs from the sequence data , we employed a method as illustrated in Fig 2 . Unlike the indexing methods of reference genome in RNA-seq analysis , this method employs a full index reference mapping strategy [37] . In this method , first we generated a full index of the AaegL3 reference assembly by comprehensively extracting 16-mers from every position of the genome . The indexed reference genome was then used for mapping of small RNA sequence reads using Subread aligner [37] that determines map position of the sequence reads in the reference genome ( AaegL3 ) using a seed-and-vote strategy [37] . The threshold value of 4 ( the number of consensus subreads required for reporting a hit ) was used for mapping reads across all the samples . The positions of tRFs in the reference genome were determined from the genomic coordinates of parental tRNAs , and the location of fragments within the tRNAs . These subsequences of tRNAs corresponded to different loop and arm regions of secondary structures of tRNAs as predicted by tRNAscan [38] . The information of genomic positions of tRFs was then used to quantify the number of reads mapped to each tRF in a genome-wide manner by StringTie [39] . To compare tRF expression between different sample groups , we used the R package edgeR-robust [40] which normalizes the read count data by using trimmed mean of M-values method ( TMM ) , and performs statistical inference of differential expression of tRFs by fitting the normalized data to weight-based generalized linear model ( glm ) and conducting a likelihood ratio test of observed weights . All statistical analyses were performed in R . To analyze tRF expression in males vs . females of different strains , we implemented an information theory approach based on mutual information ( MI ) . MI a measure of the information content that two variables share: a numerical value ranging from 0 to 1 depending on , intuitively , how much knowing one variable would predict variability of the other . In this approach , the mutual information ( MI ) of tRF expression variation was determined in pair-wise manner across males and females of the strains using R package minet [41] . The MIs were calculated using Spearman estimator , with no discretization method applied to the data prior to calculation .
We performed Illumina ( MiSeq ) sequencing of small RNA libraries ( n = 40 ) representing different biological samples ( S1 Table ) . All the sequences generated in this study have been deposited at the Gene Expression Omnibus database ( https://www . ncbi . nlm . nih . gov/geo/ ) under the accession number GSE101956 . The MiSeq sequencing generated ~ 1 . 98 million small RNA reads per sample . This estimate is based on the average number of reads generated from sequencing of the 40 samples ( S1 Table ) . A total of 79 , 309 , 784 reads were generated across all the samples . The number of reads generated from different sample groups is listed in Table 1 . The raw reads were processed for adapter removal by cutadapat ( https://github . com/marcelm/cutadapt ) and quality trimming by sickle ( https://github . com/najoshi/sickle ) , and then mapped to the reference genome AaegL3 for quantification of tRNA fragments using a method illustrated in Fig 2 . The binary alignment map ( bam ) files generated from alignments were used to identify reads that mapped to different parts of tRNAs . We followed the tRF nomenclature as described earlier [14] which classifies tRFs originating from either the 5’- or the 3’-end of precursor sequences of tRNA genes ( 5-Pre and 3-Pre respectively ) as well as fragments originating from either the 5’- or the 3’-end of mature tRNAs ( 5tRF and 3tRF respectively ) . We extended this nomenclature to further include tRFs that might be originating from different tRNA loops [12] . To achieve this , first we predicted different regions within clover-leaf secondary structure of tRNAs using tRNAscan [38] from sequences of all the tRNA genes ( n = 984 ) as predicted in AaegL3 . 1 annotation ( www . vectorbase . org ) . We found that 109 tRNAs are possibly either pseudogenes tRNAs or tRNAs for non-standard amino acids SeC ( e ) or tRNAs that contain intron sequences in the precursors ( Tyr , Ile and Leu tRNAs ) . These tRNAs were excluded from further study for the sake of simplicity of analysis . Based on the secondary structures predicted by tRNAscan , a total number of 6 , 118 bins from 875 tRNAs were generated from both mature and flanking sequences . The sequence reads that mapped to different parts of tRNAs were quantified using the StringTie tool [39] . Using this mapping and read quantification approach , we observed that only a total of 55 tRFs are ‘expressed’ in Ae . aegypti ( S2 Table ) . We defined ‘expressed’ tRFs as those fragments that showed more than 100 reads mapped in at least 3 samples . Interestingly , each of these tRFs originated from unique tRNA genes; no tRNA produced more than one tRF . Moreover , the tRFs showed a strong bias in their origins within tRNAs . The tRFs mapping to the 3’-end precursor ( 3-Pre ) sequences of tRNAs accounted the most , while no tRF originating from the 3’-end of mature tRNA sequences ( 3tRFs ) was detected in any sample ( Fig 3 ) . Of all the tRFs we identified , one derived from the 3-Pre region of a Gly-GCC tRNA ( VectorBase gene # AAEL016015 ) was the most abundantly expressed tRF in Ae . aegypti across all samples ( S2 Table ) . The expression level of tRFs varied among samples suggesting differential regulation of tRFs . The expression pattern of tRNA fragments further suggested that they were generated from specific tRNA genes and specific sites within tRNAs , and hence they were not random cleavage products of tRNAs . We profiled tRF expression in males and females of different ( n = 5 ) laboratory strains of Ae . aegypti . They included Liverpool ( Lvp ) , Rockefeller ( Rock ) , Moyo-S , Moyo-R and Trinidad ( Trini ) . By profiling these 10 samples , we identified a total of 31 tRFs that showed biased abundance in males versus females across strains based on principal component analysis ( S1A Fig ) . To determine the level of sex-biased expression of tRFs between strains , we performed pair-wise mutual information ( MI ) analysis of tRF expression of males and females between strains . MI is a measure of the mutual dependence between the two variables that infers how much one variable tells us about another variable . From this analysis , we observed that individual strains had varying pattern of sex-biased changes in expression of tRFs ( S1B Fig ) . The expression of tRFs revealed differential cluster patterns between males and females as shown in a tanglegram generated from hierarchical clustering ( S2 Fig ) . We identified 4 tRFs that showed consistently higher expression in females compared to males across the strains ( female biased expression ) , and 12 tRFs that had higher expression in males compared to females ( male biased expression ) ( Table 2 ) . Fitting the expression data of the 31 tRFs into a weighted generalized linear model implemented in edgeR robust48 , we identified three tRFs that showed significantly differential expression ( p < 0 . 05 ) between males and females . Among these , a 3-Pre tRF produced from the tRNA gene AAEL016015 ( Gly-GCC ) ( Fig 4 ) showed female-biased expression across all the five strains . On average , its mean read count was 17334 . 6 in females and 10792 . 4 in males . On the other hand , two 3-Pre tRFs produced from tRNA-Ala genes ( AAEL016845 and AAEL016846 ) revealed male biased expression that had 4 . 5 to 4 . 6 thousand mean read counts in males compared to 2 . 3 to 2 . 4 thousand reads in females . We studied tRF expression at different developmental stages of Ae . aegypti strains Moyo-S and Moyo-R . We profiled 2nd instar , 3rd instar , 4th instar larvae , and adults ( mixed sex ) to identify tRFs that are differentially expressed between different developmental stages . The reason for using mixed-sex adults was to account for any sex-biased expression of tRFs in larvae which were not sexed . We compared changes in tRF expression between stages of larvae 2 vs . larvae 3 , larvae 3 vs . larvae 4 day1 , larvae 4 day 1 vs . larvae 4 day 2 , and larvae 4 day 2 vs . adults of the two strains . From this experiment , we observed two tRFs with significant differential expression at specific developmental transition periods . One of them was the female-biased tRF described above ( AAEL016015: 3-Pre , Table 2 ) that showed developmentally regulated expression pattern between Moyo-S and Moyo-R ( Fig 5A ) . In the 4th instar larval stage ( between day1 and day2 ) , this tRF showed significant ( p = 0 . 034 , Fisher exact test ) changes in expression between the two strains . It increased in abundance in Moyo-S but decreased in Moyo-R between day1 and day2 within the 4th instar larval stage ( Fig 5B ) . As larvae transitioned to adulthood , the same tRF also showed significant differential expression ( p = 0 . 015 ) in which its abundance diminished almost 5-fold in adults compared to day2 of 4th instar larval stage in Moyo-R , but not in Moyo-S strain . Besides tRF AAEL016015: 3-Pre , we also observed another female-biased tRF that originated from the 5’ precursor region of tRNA AAEL016867 ( Ala-AGC: 5-Pre , Table 2 ) showing significant difference ( p = 0 . 007 ) in abundance in adult mosquitoes compared to larvae ( 4th instar day 2 ) between the two strains ( Fig 5B ) . These results suggested that expression of these two tRFs is sex-biased as well as developmentally regulated , and that these tRFs may be associated with sexual dimorphic regulation of adult development in Ae . aegypti . We further studied tRF expression in post blood feeding responses in Moyo-S and Moyo-R females . We fed adult females with either naïve blood or blood mixed with dengue virus , and then profiled them for tRF expression on day1 and day2 post feeding . From this experiment , we identified 4 tRFs that showed differential expression between day1 and day2 of naïve blood fed and dengue mixed blood fed mosquitoes of Moyo-S and Moyo-R strains ( Fig 6 ) . We observed that the expression of these tRFs changed significantly ( p < 0 . 05 , Fisher Exact test ) upon infectious blood feeding relative to naïve blood feeding between the two strains . Upon naïve blood feeding , these tRFs in Moyo-R females showed little or no changes in expression at the two post feeding times . But , upon feeding with dengue virus mixed blood , they showed elevated expression at day2 relative to day1 post feeding in Moyo-R . In Moyo-S females , however , naïve and infectious blood feeding showed no significant change in expression of these tRFs at the two post feeding time points . These results showed that the presence of dengue virus in the blood was able to alter the expression of tRFs in a strain-specific manner . Interestingly , one of these tRFs was the same AAEL016015: 3-Pre whose expression was female-biased and developmentally regulated as described above . As Moyo-R reflects greater refractory responses to dengue virus [31–32] , we speculate that such significant changes in tRFs in Moyo-R may be linked to tRF-mediated regulation of mosquito vector competence to dengue virus infection . We further investigated effect of midgut bacteria of Ae . aegypti in tRF expression in response to blood feeding . Using antibiotic treatments , we cleansed adult females of Moyo-S and Moyo-R strains separately ( see Methods ) , and compared tRF expression profiles of their midgut with that of non-cleansed females at 3 hr following a naïve blood meal . The results of this experiment showed that bacterial cleaning has a significant effect on differential expression of specific tRFs between Moyo-S and Moyo-R females to blood feeding ( Fig 7 ) . Three tRFs showed significantly higher expression ( p < 0 . 05 , Fisher Exact test ) in midgut of cleansed females relative to uncleansed females of Moyo-S strain . However , the changes in Moyo-R females were non-significant . Most interestingly , the tRF AAEL016015: 3-Pre , whose expression was sex-biased , developmentally regulated and responsive to infectious ( dengue virus ) blood feeding , was also responsive in the midguts of Moyo-S females that were cleansed of bacteria by antibiotic treatment ( Fig 7 ) . Together , these results suggested that expression of the tRF AAEL016015: 3-Pre have wide ranging biological effects in Ae . aegypti .
In this study , we identified fragments of tRNAs that show highly regulated biogenesis in different biological samples of Ae . aegypti . Biogenesis of active fragments from tRNAs is accomplished by precise cleavage at specific sites within specific tRNAs [15] . Active fragments of tRNAs include both ‘tRNA halves’ which are 30–35 nt in length and tRFs which are shorter than tRNA halves and range from 13 to 32 nt in length [42] . In the current study , we have focused only on tRFs . Based on cleavage sites of tRNAs , tRFs are classified according to their origin within mature or precursor regions , and named accordingly , such as 5tRFs , 3tRFs , 3-Pre , and 5-Pre . However , the nomenclature of tRFs is still a contentious issue [43] . It is unknown if there are universal sites that release the same types of tRFs across species [44] . RNase P and RNase Z are well known enzymes involved in tRNA cleavage [45–46] . Dicer and Angiogenin enzymes are also known to cleave mature tRNAs in some cases [15] . However , there is no Angiogenin gene in Ae . aegypti . We don’t know the exact cleavage sites of tRNAs in Ae . aegypti . Given these unknowns , we used a binning approach to quantify tRFs based on mapping of sequence reads to different parts within tRNAs . We generated 21 nt long reads from sequencing of the small RNA libraries . To optimize bin size of tRNAs for quantifying mapped reads , we conducted a sliding window analysis where bins of varying sizes ( 15 , 20 , 25 , 30 and 35 bases ) of tRNAs were generated and the number of mapped reads to each of these bins in genome-wide manner were analyzed . We found that 15 nt bins produced the highest number of differentially expressed tRFs by edgeR robust than other bins suggesting that a predominant portion of active tRFs present in our samples are likely to be about 15 nt long . Based on this initial analysis , we decided to use 15 nt bins for tRF mapping and for subsequent analyses . We have not attempted to assemble reads to delineate length of individual expressed tRFs . We believe that use of an experimental approach rather than bioinformatics method is a better way to identify intact tRFs and their ends . The profile of tRFs that are active at RISC ( RNA-induced silencing complex ) by adopting the RNA immuno-precipitation ( RIP-Seq ) method is possibly a suitable approach for that aim . Previously , this method has been successfully used to detect active tRFs in the silk worm [23] . Given the results from this study that only specific tRNAs produce expressed fragments , a key question is how and on what basis specific tRNAs are selected for biogenesis of tRFs . At ribosome sites during active protein synthesis , mature tRNAs function by recognizing the codon sequences in the mRNA by their anticodon sequences to add the cognate amino acids to the growing peptide chain . Though variation in anticodon sequences of tRNAs produce different isoacceptors ( tRNAs cognate to the same amino acid but with different anticodon sequences ) , sequence variation also occurs in other loops and arms of tRNAs [47] . We have shown in an earlier study that a significant correlation ( p <0 . 05 ) was observed between sequence diversity ( π—values ) of the anticodon arm and the A-box ( internal promoter ) of tRNAs in mosquitoes [47] . In addition , sequence variation in the B-box promoter was also significantly correlated ( p <0 . 05 ) with a triplet ( 63–65 ) just downstream of this promoter and a part of the TψC arm . We have further shown the existence of a significant association ( p <0 . 05 ) between codon bias and cognate tRNA gene copy numbers in mosquitoes , as well as other insect species [48] . It is thus plausible that genetic variation within tRNAs or translation selection pressure on coding genes ( likely the target genes of tRFs ) may be associated with selecting specific tRNAs to produce functional tRFs . However , further studies are required to test this hypothesis . The mapping of small RNA reads to tRNAs in our study showed that many tRNAs had no read mapped . Some tRNAs showed only few reads ( < 10 reads ) mapping to specific bin ( s ) in either one or two samples only . As isoacceptors of tRNAs are highly similar in sequences [47–48] , we chose the see-and-vote mapping strategy of Subread aligner for tRF mapping that minimizes spurious hits by relying on alignment score of subreads to determine the final map location of a read [37] . If our mapping approach had resulted spurious hits , we would have observed random ‘hits’ to gene copies of different tRNA isoacceptors . But , we observed very precise mapping results where tRFs are localized with only specific tRNA genes . In fact , majority of the tRNA genes predicted in Ae . aegypti genome show no read mapping at all . We quantified a total number of 6 , 118 fragments binned from 875 tRNAs , but only 55 ( S2 Table ) were detected as expressed in the whole genome . Thus , we claim that the full reference based subread mapping approach is highly reliable and sensitive method of mapping tRNA fragments using small RNA sequencing data . However , currently there is no specific best practice in tRF mapping on how many reads should map in order to qualify a tRF as ‘expressed’ . There are guidelines to define minimum threshold of read counts to filter out low-expression genes in RNA-seq analysis , but currently we do not have such a guideline in tRF-seq analysis . We selected 100 as the minimum number as this threshold allowed us to identify tRFs that were expressed with greater than 100 reads in more than 3 samples . However , some tRFs including the AAEL016015: 3’-Pre that we highlight in this report is expressed with thousands of reads ( S2 Table ) . In fact , the AAEL016015: 3’-Pre is the most abundantly produced tRF in Ae . aegypti across samples . At the same time , its expression appears to be tightly regulated as seen in significant changes in read counts between samples . This included significant changes in abundance between males and females , developmental transition from larval to adult stages , antibiotic cleansing of female gut bacteria and post blood feeding responses . Based on the expression of the tRF ( AAEL016015: 3-Pre ) , we propose that this active tRNA fragment might regulate genes associated with sex , development , gut microbiome as well as genes that respond to blood feeding . It is believed that a functional tRF binds to messenger RNAs similar to microRNAs binding to target mRNA , causing regulation of target genes at the posttranscriptional stages [49] . In our previous studies , we have predicted miRNAs that target conserved developmental genes in flies and mosquitoes [50] , and identified genes that showed significant changes during different developmental stages of Ae . aegypti [51] . As miRNAs and tRNA fragments may regulate gene expression by similar mechanisms [49] , it is likely that tRFs that are differentially expressed during transition from larval to adulthood could target genes key to adult emergence . However , we have not predicted those target genes in this study . The changes in expression of this particular tRF upon infectious ( dengue virus ) blood feeding suggest its possible role in vector response to dengue virus infection . A similar result was observed in a previous study where differential expression of a single tRF was observed in response to respiratory syncytial virus infection [52] . The results of our current study supports the report on emerging roles of tRFs to viral infections [53] . Furthermore , the multifaceted functional implication of tRF ( AAEL016015: 3-Pre ) makes it a suitable small RNA regulator for further investigating its possible role in vector-virus interactions and pathogen dissemination to humans by blood feeding of Ae . aegypti . To conclude , the genome-wide analysis of tRNA fragments in Aedes aegypti by small RNA sequencing identified active tRFs in different biological samples . Specific tRFs revealed sex-biased expression in multiple laboratory strains . A single sex-biased tRF was identified that showed association with development and post-blood feeding responses . Thus , we claim that tRFs are active in this mosquito , and may play diverse role in disease vector biology . | The mosquito Aedes aegypti is a major vector of arboviral diseases in subtropics and tropics . The confounding effects of immature development and adult microbiome on the ability of Ae . aegypti to transmit diseases ( vector competence ) have gained renewed attention in the recent years . However , the molecular nature of these links/ effects remains unknown . This is major gap in knowledge regarding how vector competence is regulated at molecular level , and how that regulation may be variable among different strains of this mosquito . In this study , we investigated expression of newly discovered class of small RNAs , called tRNA fragments ( tRF ) in Ae . aegypti strains . Based on small RNA sequencing and bioinformatics analyses , we show that tRFs are expressed in Ae . aegypti , and they are associated with significant changes in expression between males and females , during development stages , and post blood feeding responses . A single tRF showed association with sex-biased expression , developmental regulation and in response to blood meals between Moyo-S and Moyo-R strains that differ in midgut susceptibility to dengue virus . The findings of this study are expected to guide future research efforts directed toward examining detailed regulatory mechanisms of tRFs in vector competence of Ae . aegypti to disease transmission . | [
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] | 2018 | Multifaceted functional implications of an endogenously expressed tRNA fragment in the vector mosquito Aedes aegypti |
TcTASV-C is a protein family of about 15 members that is expressed only in the trypomastigote stage of Trypanosoma cruzi . We have previously shown that TcTASV-C is located at the parasite surface and secreted to the medium . Here we report that the expression of different TcTASV-C genes occurs simultaneously at the trypomastigote stage and while some secreted and parasite-associated products are found in both fractions , others are different . Secreted TcTASV-C are mainly shedded through trypomastigote extracellular vesicles , of which they are an abundant constituent , despite its scarce expression on culture-derived trypomastigotes . In contrast , TcTASV-C is highly expressed in bloodstream trypomastigotes; its upregulation in bloodstream parasites was observed in different T . cruzi strains and was specific for TcTASV-C , suggesting that some host-molecules trigger TcTASV-C expression . TcTASV-C is also strongly secreted by bloodstream parasites . A DNA prime—protein boost immunization scheme with TcTASV-C was only partially effective to control the infection in mice challenged with a highly virulent T . cruzi strain . Vaccination triggered a strong humoral response that delayed the appearance of bloodstream trypomastigotes at the early phase of the infection . Linear epitopes recognized by vaccinated mice were mapped within the TcTASV-C family motif , suggesting that blockade of secreted TcTASV-C impacts on the settlement of infection . Furthermore , although experimental and naturally T . cruzi-infected hosts did not react with antigens from extracellular vesicles , vaccinated and challenged mice recognized not only TcTASV-C but also other vesicle-antigens . We hypothesize that TcTASV-C is involved in the establishment of the initial T . cruzi infection in the mammalian host . Altogether , these results point towards TcTASV-C as a novel secreted virulence factor of T . cruzi trypomastigotes .
Trypanosoma cruzi , is the kinetoplastid pathogen that causes Chagas’ disease . There are about 10 million people currently infected and more than 50–60 million people living in endemic areas , at risk of infection . Chagas’ disease is a chronic debilitating illness with symptoms generally appearing 10 or more years after the initial infection [1–3] . At this stage , anti-parasitic drugs are poorly effective and patients are treated according to their cardiac , digestive or neurological compromise [4–5] . Acute infection is usually undetected because of its mild and unspecific symptoms and , therefore , the patients are not diagnosed . The acute phase of the infection is characterized by the presence of high levels of trypomastigotes in blood . These nonreplicative trypomastigotes invade nucleated cells , where they differentiate to the amastigote stage that replicates in the cytoplasm and differentiates again to trypomastigotes . Then , the infected cell bursts and trypomastigotes are released once again to circulation . During the acute phase this cycle repeats itself actively and the trypomastigote disseminates the infection to several organs and tissues [6] . Considering the absence of preventive or chemoprophylactic vaccines as well as the life cycle of the parasite , uncharacterized molecules differentially expressed in the infective trypomastigote stage can be interesting novel targets for rational intervention against Chagas’ disease [7 , 8] . The T . cruzi Trypomastigote Alanine , Valine and Serine ( TcTASV ) rich proteins belong to a medium-size multigene family of ~40 members that was identified from a library of trypomastigote-enriched mRNAs [9] . The TcTASV family is conserved among all the T . cruzi lineages analyzed so far and has no orthologues in other species , including the closely-related trypanosomatids T . brucei , T . rangeli and Leishmania sp . [9] . TcTASV proteins , whose function is still unknown , are expressed mainly in the trypomastigote stage . The N- and C-terminal regions of the TcTASV proteins possess a signal peptide and a consensus for a GPI anchor addition , respectively , and display the highest level of conservation , while the central region presents more variability [9] . TcTASV family can be distinguished by the common amino acid motif tasv_all that starts approximately at amino acid 42 ( Vx1x2x3[CES]x4x5TDGx6Lx7Wx8x9x10x11Ex12x13Wx14x15Cx16x17x18P ) . The TcTASV family is comprised of 4 subfamilies -TcTASV-A , B , C and W- defined by the primary amino acid sequence and length of polypeptides . Further , each subfamily presents certain amino acids at the indeterminate positions ( x1 , x2 , etc ) of the tasv_all motif . For example , subfamilies TcTASV-C and TcTASV-A both have proline and glycine at positions X4 and X5 , while TcTASV-B contains serine and arginine , and TcTASV-W has alanine at X4 and glutamic acid at X5 . The TcTASV-C subfamily includes approximately 15 genes ( small variations are found in different strains ) with protein products of 330–360 amino acids [9 , 10] . A few years ago , in the search for novel vaccine candidates by a genetic immunization approach , a fragment of a TcTASV-C gene ( TcCLB . 511675 . 3; ID TritrypDB , [11] ) was identified among a pool of antigens that protected mice from a parasite challenge with a highly virulent T . cruzi strain [12] . In a first characterization of the TcTASV-C subfamily we found that TcTASV-C is a thickly glycosylated ~60 kDa polypeptide , expressed in trypomastigotes and absent in all other parasite stages [10] . TcTASV-C presents a characteristic distribution pattern of scattered dots along the parasite surface and flagellum , and is spontaneously secreted to the medium . While anti-TcTASV-C antibodies are detected in about 30% of chronically-infected patients , the seroprevalence in reservoir dogs with active infection rises to 75% [10 , 13] . In the experimental murine T . cruzi model , TcTASV-C specific antibodies can be detected early from the beginning of the infection [10] . Although TcTASV-C proteins are not major components of the parasite in trypomastigotes derived from in vitro cultured cells [10] , several TcTASV-C peptides have been recently identified in secretomes of T . cruzi trypomastigotes [14–16] . Interestingly , TcTASV peptides were found in the bloodstream trypomastigote proteome , but not in proteomes from in vitro cultured cell derived trypomastigotes [17] . Also , a recent analysis of an overall transcriptome of the host cell and T . cruzi during the course of infection confirmed that the TcTASV family is extensively over represented in trypomastigotes , relative to all the other stages of the parasite , and several TcTASVs mRNAs are among the most abundant in the trypomastigote stage [18] . After being unnoticed for several years , and in agreement with our previous reports , these findings also outpoint towards TcTASVs as potential virulence factors and as interesting targets for study and rational intervention . Here we present results leading to a deeper understanding of the TcTASV-C subfamily and its performance as a vaccine antigen .
First , a bioinformatic analysis was carried out to determine whether there was a common pattern among all TcTASV-C members . A distinctive and conserved tasv_c motif of 50 amino acids was identified in all TcTASV-C proteins ( Fig 1 ) . In most proteins , the tasv_c motif starts at amino acid 42–43 , including and expanding the previously reported tasv_all motif , common for all TcTASV proteins irrespectively of their subfamily ( asterisks in Fig 1 ) . Only TcTASV-C proteins are retrieved by searching any database with the tasv_c motif . We have already described that TcTASV-C is expressed both at the parasite surface and secreted to the medium [10] . To investigate whether–among the 15 TcTASV-C genes of CL Brener strain- several genes ( or only one ) are simultaneously expressed , and to clarify if surface-located and secreted TcTASV-C proteins are expressed from the same genes , we undertook a 2D gel based approach ( Fig 2 ) . More than one TcTASV-C product was observed both in the parasite-associated and in the secreted fractions ( Fig 2 ) , suggesting that more than one TcTASV-C gene are simultaneously expressed . On the other hand , there were common and differential TcTASV-C spots detected in both fractions . There was a clear band of higher molecular weight only present in the parasite fraction ( arrow in Fig 2 , upper panel ) and two bands of more acidic isoelectric point ( pI ) in the secreted one ( arrowheads in Fig 2 , lower panel ) . Two other bands seemed to be shared by both samples . These results show that the expression of different TcTASV-C genes occurs simultaneously at the trypomastigote stage and suggest that while some secreted and parasite-associated products are found in both fractions , others are different . TcTASV-C is secreted and also detected–by immunofluorescence microscopy- at trypomastigote surface in spots that are compatible with detergent resistant domains [10] . These domains are often associated with secretion of molecules through extracellular vesicles ( EVs ) [20 , 21] . In this context , we investigated whether TcTASV-C proteins were released associated with EVs or as soluble factors ( VF: vesicle-free fraction ) . In a first set of experiments , trypomastigote- derived conditioned media was resolved by ultracentrifugation in density gradients; TcTASV-C was detected in fractions corresponding to extracellular vesicles ( S1 Fig ) . We next investigated whether TcTASV-C proteins were released associated with large ( V2 ) or small ( V16 ) EVs , whose presence and purity was confirmed by transmission electron microscopy ( TEM; Fig 3A ) . All samples showed vesicles of 30–130 nm in size after 2 h of ultracentrifugation , while vesicles obtained after 16 h were smaller ( ~50 nm; Fig 3A ) . TcTASV-C was secreted in both EVs populations in the CL Brener strain ( Fig 3B ) ; in the small EV fraction ( V16 , Fig 3B , upper panel ) TcTASV-C appeared as a highly abundant component , while it was almost undetectable on parasite pellets at these conditions . This agrees with the already reported low level of expression of TcTASV-C on parasite body in cell derived trypomastigotes [10] . Longer exposure times were needed to evidence the TcTASV-C expression on trypomastigotes but render overexposed and unclear images for V2 and V16 fractions . As expected , the heat shock protein 70 ( HSP70 ) , a secretome marker , was detected in all fractions and , TcSR62 , a nucleo-cytoplasmic and non-secreted RNA binding protein , only in the parasite pellet ( Fig 3B ) [10 , 22 , 23] . A proteomic analysis of V2 and V16 secreted extracellular vesicles of CL Brener strain was also carried out , to confirm our western blots results and , besides , because there are no proteomic analysis of V2 and V16 cargo proteins of trypomastigotes . Indeed , the data currently available from exoproteomes of trypomastigotes were derived from total secreted material or from a mixture of vesicles from parasites and host cells purified together [14–16] . High confidence peptides of four TcTASV-C genes were found both in V2 and V16 samples ( TcCLB . 508741 . 440 , TcCLB . 509123 . 10 , TcCLB . 509147 . 40 , TcCLB . 508737 . 10 ) , which is in line with the 2D western blot results . Although TcTASV-C peptides were detected in both EV fractions , is noteworthy that each EV population presented a differential set of major proteins ( V2: n = 271; V16: n = 189 ) , and only a minor core of 142 common proteins ( among which are the TcTASV-C peptides ) ( S2 Fig ) . This suggests that both fractions of vesicles correspond to different populations . Surface , intracellular as well as a considerable percentage of hypothetical proteins were identified by proteomics in trypomastigote EVs ( S2 Fig ) . In context , the picture obtained could indicate that–at least at the assayed conditions- the paucity of TcTASV-C in the parasite’s body probably reflects that most of TcTASV-C produced is delivered to the secretory pathway . We then analyzed the secretion profile of TcTASV-C in T . cruzi strains that encompass a wide spectrum of virulence and also represented the major T . cruzi lineages [24] . Overall , TcTASV-C was secreted in EVs in all the strains analyzed ( Fig 4 ) . Only mild differences in TcTASV-C secretion profile were detected among strains . In the low-virulent SylvioX10 strain , TcTASV-C seemed to be poorly represented in small ( V16 ) EVs , while in 173 strain ( DTU TcI ) –middle-virulence- the secretion profile of TcTASV-C was quite similar to that found in CL Brener strain ( Fig 3 ) , which is also of intermediate virulence in the murine model . In culture-derived trypomastigotes from highly virulent strains ( i . e . Y , TcII and RA , TcVI ) a more dynamic pattern of secretion was observed , and TcTASV-C was detected alternatively in different fractions ( Fig 4 and S3 Fig ) . Particularly for the Y strain , trypomastigotes released on the 1st day after cells began to lyse usually showed the profile depicted on Fig 4 , while TcTASV-C expression shifted to V16 and VF fractions , on EVs secreted from parasites harvested the 2nd and 3rd day , showing a dynamic expression pattern ( S3 Fig ) . As a whole , in all the analyzed strains , TcTASV-C was significantly more represented in the secreted than in the parasite associated fraction . Of note , the total protein content secreted in EVs was similar for all strains thus allowing to discard that changes in TcTASV-C expression by each parasite strain had a correlation with the amount of total EVs secretion . Strikingly , the first proteomic evidences of TcTASV-C were registered few years ago with the publication of the proteome of bloodstream trypomastigotes [17] . In contrast , no evidence of expression of TcTASV-C was noticied when proteomics of culture-derived trypomastigotes were analized . Therefore , we decided to investigate the TcTASV-C expression profile in bloodstream trypomastigotes from different T . cruzi strains , in connection with its expression on culture-derived trypomastigotes . TcTASV-C was detected in 1x10^6 ( or even less ) bloodstream parasites ( Fig 5A , upper panel , BT lanes ) while it was necessary to load more than tenfold of culture-derived trypomastigotes to be weakly detected ( Fig 5A , Cult lanes , upper panel ) . This finding was observed in all T . cruzi strains assayed , belonging to different lineages , and proved that TcTASV-C is upregulated in bloodstream forms . Importantly , this differential expression pattern between both types of trypomastigotes was not observed with other proteins of T . cruzi , which were detected accordingly to the parasite amount loaded on the gel ( Fig 5A , middle and lower panels ) . As well as detected for culture-derived trypomastigotes , bloodstream trypomastigotes were also able to strongly secrete TcTASV-C . In the highly virulent RA strain ( TcVI ) TcTASV-C was essentially identified in all secreted fractions , even from 1-2x10^6 trypomastigotes ( Fig 5B and S5 Fig ) . As molecules released by the parasite could potentially interact with host cells , we evaluated this possibility for TcTASV-C on Vero ( Fig 6A ) and J774 ( Fig 6B ) cells . TcTASV-C ( but not the control protein GST ) exhibited a dose-dependent adhesive capacity , suggesting a ligand-receptor interaction . Similarly , EVs derived from trypomastigotes also interacted with mammalian cells ( S4 Fig ) . This interaction was only observed with freshly isolated EVs , but not with EVs that had been previously purified and stored at -80°C . We also evaluated a potential role of TcTASV-C on T . cruzi cell infection , employing as model two T . cruzi strains from different lineages and obtained from in vitro cultures ( CL Brener , Fig 6C ) or purified from blood of infected mice ( Tulahuen strain , expressing β-galactosidase , Fig 6D and 6E ) [25 , 26] . Neither pre-incubation of recombinant TcTASV-C with mammalian cells before infection ( Fig 6C and 6D ) nor pre-incubation of trypomastigotes with anti-TcTASV-C sera ( Fig 6E ) interfered with parasite internalization or cellular infection . The delivery of molecules in vesicles is a well known immune evasion mechanism exploited by parasites [27] . We therefore investigated whether infected hosts could recognize the protein content of secreted vesicles ( Fig 7 ) . Pooled sera from humans , mice or rabbits chronically infected with T . cruzi failed to efficiently detect EVs antigens , although they strongly reacted with trypomastigote antigens and with “naked” secreted proteins , both from RA and CL Brener strains ( Fig 7 and S6 Fig ) . These findings led us to hypothesize that immunization with TcTASV-C–which is highly expressed in bloodstream trypomastigotes and also secreted- would be a good target for immunotherapy control . This hypothesis was encouraged by our previous finding of a TcTASV-C gene fragment among a pool of protective antigens [12] . Besides , being TcTASV-C expressed at early stages of the infection [10] , we hypothesize that humoral immune response could be mediating TcTASV-C neutralization . To evaluate the performance of TcTASV-C as vaccine antigen , we designed a DNA-prime protein-boost schedule of immunization . The first 2 doses consisted of plasmid DNA of an eukaryotic expression vector carrying a fragment of TcTASV-C [12] adjuvanted with a plasmid coding for GM-CSF [28] . In the 3rd and 4th doses , mice were boosted with TcTASV-C recombinant proteins ( two different genes fused to GST or histidine tags , rTcTASV-CGST and rTcTASV-CHIS ) adjuvanted with aluminium salts . As expected , immunization was effective to induce high levels of total anti-TcTASV-C IgGs ( Fig 8 ) . Most animals presented a mixed Th1/Th2 response , with strong IgG2a and IgG1 responses ( Fig 8B and 8C ) . However , the cellular and cytokine response in splenocytes obtained from mice 15 days after immunization , showed a low proliferative response and negligible levels of IFN-ɣ and IL-10 after rTcTASV-C restimulation in culture . Two weeks after the last dose , animals were challenged with parasites of the highly virulent RA strain ( DTU TcVI ) . TcTASV-C vaccinated mice exhibited a delayed appearance of circulating trypomastigotes and lower parasitemia peaks ( Fig 9A–9C ) . Bloodstream trypomastigotes were detected from the day 9th on , in all controls while were nearly unnoticeable until day 12th in all TcTASV-C vaccinated ( Fig 9A–9C ) . Besides , TcTASV-C vaccinated mice presented reduced trypomastigote numbers at the peak of parasitemia ( Fig 9C ) and–overall- lower bloodstream parasite levels than the control group ( Fig 9C; p<0 . 05 , at 9 and 12 pdi , Mann-Whitney test ) . Immunized mice also showed higher survival rates than controls ( Fig 9D; TcTASV-C , ~30%; control group , 0%; p<0 . 05 Log-Rank test ) . All animals that survived at the end of experiments belonged to TcTASV-C vaccinated groups . After T . cruzi challenge , vaccinated mice showed a strong response against parasite antigens , with an IgG2a-biased isotype , similar to that found in infected animals ( Fig 10A , 10B and 10C ) . Besides , sera from vaccinated mice were able to lyse trypomastigotes in the presence of an external complement source ( Fig 10D ) ; the lytic response was increased in mice after infection . Focusing on the anti-EV response after T . cruzi challenge , vaccinated animals reacted not only with TcTASV-C but also with other EV antigens , that were unseen by infected but non-vaccinated animals ( Fig 10E and 10F ) . Therefore , the vaccination was able to trigger an EV-focused immune response after challenge that can’t be mimicked by unvaccinated infected animals . As the main immune response detected in immunized animals was humoral , we mapped the TcTASV-C epitopes detected . Peptides covering putative TcTASV-C epitopes were designed by weighing linear B-cell epitope predictions ( bioinformatic approach ) and those epitopes previously discovered in a high-density peptide microarray screened with human sera [29] . We selected 5 peptides of 15–20 amino acids to evaluate the reactivity of sera from TcTASV-C vaccinated , control and infected unvaccinated mice ( Fig 11 ) . Eighty-six percent ( 86%; 19/22 ) of the sera from vaccinated mice reacted with at least one peptide , and 45% reacted with 2 or more peptides ( S1 Table ) . In contrast , only 30% ( 4/13 ) of sera from unvaccinated infected mice ( with previously reported reactivity against TcTASV-C ) recognized any of these peptides ( S1 Table ) . P46-62 and P172-189 were the peptides most detected by the sera from vaccinated group , with 84% ( 16/19 ) of sera reacting with one or both of them ( Fig 11 and S1 Table ) . P46-62 was the peptide most detected by sera from vaccinated mice while it was not detected by any of the sera from the infected group . Interestingly , P46-62 is part of the tasv_all motif , but is only partially present in the rTcTASV-Cs employed in the vaccination schedule ( TcTASV-CHIS: KLSWRLRGEEEW; TcTASV-CGST: SWRLQGEEEW ) . Even more striking , the reactivity to peptide P46-62 seems to be driven by the RLR triplet or the second arginine , since an identical peptide with an unique substitution that changes the RLR motif to the RLQ , turned it into an unrecognized peptide ( P47-63; S1 Table ) . Both RLR and RLQ sequences are present in TcTASV-C genes ( see Fig 1 ) , and represented by the rTcTASV-Cs employed in the vaccination scheme ( RLR in TcTASV-CHIS , RLQ in TcTASV-CGST ) . Altogether these results support the idea that the broad anti-peptide reactivity of immunized mice is probably mediating the partial resistance and/or the delay in the appearance of circulating trypomastigotes in challenged mice .
The T . cruzi TcTASV gene family remained unobserved until a few years ago when it was identified by our group through a trypomastigote-enriched cDNA library [9] . Almost simultaneously , an expression library immunization approach designed to discover novel vaccine antigens in T . cruzi , spotlighted the TcTASV-C subfamily , because a fragment of a TcTASV-C gene was identified in a pool of protective clones [12] . A distinctive feature that characterizes TcTASV proteins–and particularly the TcTASV-C subfamily- is their predominant expression in bloodstream trypomastigotes . Recent transcriptomic and proteomic studies uphold our previous observations that the TcTASV family is over-represented in the trypomastigote stage [17 , 18] , and therefore could represent an interesting target for rational intervention in T . cruzi infection . Here the TcTASV-C expression and secretion dynamics and its performance as an individual vaccine candidate were analyzed . We demonstrate that , despite its scarce expression on culture-derived trypomastigotes , TcTASV-C is strongly secreted , and is a major component of trypomastigote’s EVs , at least in the T . cruzi reference strain CL Brener . This was observed both by western blot and proteomics on large ( V2 ) and small ( V16 ) EVs . It is a novelty , although not unexpectedly , that a parasite-associated and low-expressed protein ( or protein family ) is actually a highly abundant component of the trypomastigote secretome . The secretion of EVs by parasites has been proposed as a pathogen-driven mechanism aimed to generate -in the host- an environment that favours the initial infection [30–34] . Indeed , in most of the tested T . cruzi strains , TcTASV-C was mainly secreted contained into EVs . Of note , the more virulent strains ( i . e . RA and Y ) presented also a more dynamic secretion pattern ( Fig 4 , Fig 5 , S3 Fig and S4 Fig ) . On the other hand , we have shown that TcTASV-C expression is upregulated in bloodstream parasites , suggesting that some molecules present in the host trigger TcTASV-C expression . The potential of TcTASV-C as an individual vaccine candidate , however , was somehow limited to the acute phase . In our model , TcTASV-C immunized mice achieved an enhanced control of parasitemia at the beginning of the infection . The delayed appearance of bloodstream trypomastigotes was along with the presence of functional antibodies in sera from TcTASV-C vaccinated mice , with ability to lyse trypomastigotes by ADCC . This is also consistent with the detection of TcTASV-C early upon infection and suggests that TcTASV-C could have a role during this phase of infection [10 , 13] ( unpublished results ) . We hypothesize that the window of time with lower bloodstream parasites , gives a handicap to TcTASV-C primed mice to launch effector mechanisms against the parasite . However , it has to be said , the humoral response induced by vaccination was not strong enough to completely protect and clear parasites from a lethal challenge , and mortality rates were only mildly improved . Likewise , Ramirez et al ( 2017 ) [35] have recently reported that EVs derived from the interaction between mammalian cells and trypomastigotes potentiated parasitemia , particularly in the early acute phase ( 3–6 days ) of infection . This effect was stage-specific since it was not observed with EVs derived from the interaction of mammalian cells with metacyclic trypomastigotes or epimastigotes , suggesting that stage-specific EVs components might play a role in survival and dissemination of this parasite stage in the vertebrate host [35] . Secretion of virulence factors contained in extracellular vesicles has also been understood as a parasite strategy to deliver long distance effector molecules that should act in concert [36] . In particular , T . cruzi trypomastigotes release EVs that can interact with the host and modulate immune responses . The first communication in this way was in 2009 , when Trocoli-Torrecilhas et al [37] demonstrated that inoculation of mice with naked extracellular vesicles predisposed them to a more virulent infection , along with a strong inflammatory tissue damage and higher parasitic loads in heart . In fact , the effect observed with whole EVs had been observed several years before with an EV cargo molecule , the trans-sialidase ( TS ) . Chuenkova and Pereira ( 1995 ) [38] reported that mice sensitized with TS were more susceptible to T . cruzi infection , displaying enhanced parasitemia and mortality . Here , by analyzing the proteome from CL Brener EVs , we found peptides of both TS and TcTASV families , suggesting that both components of trypomastigotes are secreted as part of the same cargo and can act in a concerted fashion . Actually , in retrospective , we found several EV cargo proteins employed as vaccine antigens with promising results [12 , 39–46] . Interestingly , peptides of most of these proteins were found in our EV proteome ( Tc24 , SA85 , CRP , MASP , TS , tryparedoxin-peroxidase , paraflagellar rod proteins , etc ) . We propose that immunization with some of the molecules delivered into EVs with proper adjuvanticity , could allow the host to develop an adequate immune response against T . cruzi . The prime and boost vaccination scheme employed here mostly triggered a humoral mediated immune response able to block or neutralize surface anchored and/or secreted TcTASV-C . Although yet unknown , we speculate that the possible function of the TcTASV-C subfamily is exerted through its most conserved motif ( tasv_c ) , which encompasses a 50 amino acid long sequence at the amino terminus of the protein . Also , the shorter tasv_all motif common to all TcTASV subfamilies can be found within the tasv_c motif , but with specific amino acids at certain positions for each subfamily ( see Fig 1 ) . Interestingly , a linear B-cell epitope located within the tasv_all-tasv_c motif was exclusively recognized by sera from TcTASV-C vaccinated mice ( P46-62 , Fig 11 ) . This reactivity seems to be specifically prompted by the prime and boost vaccination scheme since sera from infected unvaccinated mice are unable to react with this peptide , suggesting that antibodies against this motif are mediating the TcTASV-C neutralization achieved–at least partially- by this vaccination scheme during the early infection . Packaging molecules into EVs can also be considered as a parasite driven strategy to escape from the host immune surveillance or extracellular degradation until they reach the target cells or tissues . In our hands , EV proteins contained in trypomastigote-secreted EVs were not detected by sera from infected hosts from different species , in contrast with trypomastigote-associated and freely secreted antigens , that were recognized by sera from infected hosts . Indeed , this finding suggests that ~30% of sera from infected hosts that do recognize TcTASV-C actually reacted against proteins attached to the parasite’s surface or freely-secreted to the environment , but not against the TcTASV-C genes that are secreted contained into EVs . We support the hypothesis that secretion of cargo in EVs ( and particularly the secretion of TcTASV-C ) is another parasite-driven immune evasion mechanism . In a recently published work , Bautista-Lopez et al ( 2017 ) [14] looked for “Trypomastigote Excreted Secreted Antigens” ( TESA , because the whole secreted population was analyzed ) that are exposed to the host immune system . They carried out an immune capture assay with T . cruzi-infected patient’s antibodies to screen for novel and secreted antigens , which could be useful markers of disease status . In accordance with our results , and although TcTASV-C peptides were found in the TESA proteome , none of TcTASV proteins were revealed by patient’s sera . Altogether , these results reinforce the idea that most of the proteins delivered into EVs are hidden from the host or , at least , are hard to be detected in the way they are presented to the host immune system . In 2016 , Queiroz et al [15] published the first proteomic analysis of the trypomastigote secretome ( which included both free and EV-secreted proteins ) from the Y strain ( DTU TcII ) , and soon after Bautista Lopez et al ( 2017 ) [14] presented the proteome of EVs derived from the culture of both cells and trypomastigotes ( Tulahuen strain; DTU TcVI ) . In both of these proteomes TcTASV peptides were eventually identified , supporting the results presented here that demonstrated that , despite being a medium-size gene family , TcTASV proteins are an important component of the trypomastigote secretome and EVs . Regarding the expression of TcTASV-C in the trypomastigote EVs , it is notable that TcTASV-C is easily detected by western blot , suggesting it as a major EV component , especially in contrast with its weak expression on parasite’s body of culture-derived trypomastigotes . Although TcTASV-C is hard to be detected in culture-derived trypomastigote homogenates ( undetectable for the conditions of western blot in Fig 3B , upper panel ) , it is revealed as a major component of EVs in CL Brener strain . In fact , the identification of peptides from 4 different TcTASV-C genes in our EV proteome corresponds with this observation and also with the 2D gel results , where 4 spots were detected as TcTASV-C proteins in the secreted fraction . The picture obtained for TcTASV-C could indicate that the paucity of TcTASV-C in the parasite’s body probably reflects that most of the protein produced is delivered to the secretory route , thus suggesting that its putative function is related somehow to the development of a permissive environment for early T . cruzi settlement . It is well known , but poorly documented , that parasites–and particularly T . cruzi trypomastigotes- express a very different set of molecules when isolated from the host ( i . e . in vivo infection ) than from culture ( i . e . in vitro infection ) , basically in response to the pressure of the immune system . Here , we demonstrate that TcTASV-C expression is much higher in bloodstream than in culture-derived trypomastigotes . We show that this is true for different T . cruzi strains and also that it is specific for TcTASV-C , but not for other antigens or virulence factors of trypomastigotes . We still do not known what factors of the vertebrate host trigger this expression , which is a matter of our current research . Besides , these results highlight the relevance of working with trypomastigotes obtained from in vivo sources to study the T . cruzi biology , especially when research involves parasite stages that are under immune system pressure in the vertebrate host . The delivery of virulence factors in exosomes or extracellular vesicles is also a strategy to interfere with host cell signaling pathways required to control infection . Exposure of mice to exosomes of L . infantum resulted in higher parasitic loads in spleen , which was linked to a suppressive T cell phenotype [30 , 31] . As well as Leishmania exosomes display immunomodulatory properties , T . cruzi extracellular vesicles also do . In fact , Nogueira et al ( 2015 ) [47] found that different T . cruzi strains secreted different concentration of vesicles . This variability could not be associated with the current T . cruzi DTU classification because -for example- two TcI strains presented polar secretion levels ( Col vs . YuYu ) . Protein concentration and alpha-galactosyl residues in secreted EVs also varied among the different strains , and without a lineage specific association [47] . Focusing on the modulation of immune responses by EVs in the acute phase of infection , only after stimulation with EVs from YuYu ( DTU TcI ) and CL-14 ( DTU TcVI ) , peritoneal macrophages from C57BL/6 mice produced high levels of proinflammatory cytokines ( TNF-alpha ) and NO , via the TLR-2 . This profile was not stimulated by EVs from other T . cruzi strains . Similar findings were recently reported by Clemente et al ( 2017 ) [48] employing EVs secreted by metacyclic trypomastigotes from other strains . In the present work , we registered a variable expression of TcTASV-C in the different secretory fractions ( i . e . V2 , V16 and soluble factors ) among the different strains analyzed . Although we found similar levels of total protein content in EVs derived from the different strains , it should be stated that the protocols employed to isolate EVs and the strains analyzed were different . As in previous works , we could not link a particular secretion profile with a certain T . cruzi DTU or strain . This complex scenario led us to speculate that differences in EVs cargo could reflect the broad spectrum of clinical manifestations observed in Chagas’ disease . Our opinion is that we are still building a puzzle from somehow complementary but still fragmented data , showing currently a complex and not very well understood picture . In brief , we have demonstrated that TcTASV-C is a major component of bloodstream trypomastigotes , and that TcTASV-C is mainly secreted , either contained into EVs or free . Besides , although with the prime and boost strategy employed TcTASV-C did not result a promising vaccine candidate , it was possible to interfere with the early acute phase of T . cruzi infection . Indeed , the strong anti-TcTASV-C humoral immune response elicited by immunizations allowed to understand–partially- the TcTASV-C functionality; we hypothesize that TcTASV-C is involved in the establishment of the initial T . cruzi infection in the mammalian host . Although we highlight TcTASV-C as a potential antigen to bit the parasite in the early acute phase , we bear in mind that an effective vaccine to control Chagas’ disease should include other antigens and/or trigger also other arms of the host immune response . Ultimately , results presented here strongly highlight TcTASV-C as a novel secreted virulence factor of T . cruzi trypomastigotes .
All protocols conducted with animals were designed and carried out in accordance with international ethical standards for animal experimentation ( Helsinki Declaration and its amendments , Amsterdam Protocol of welfare and animal protection and National Institutes of Health , USA NIH , guidelines ) and were approved by the Institutional Animal-Care Ethics Committee of the University of Buenos Aires ( CICUAL , res number: 2846/2013 ) and from University of San Martin ( CICUAE , protocol number: 01/2012 and 08/2016 ) . TcTASV-CGST ( amino acids 65 to 330 of ORF Tcruzi_1863-4-1211-93 ) was already cloned in our laboratory in pGEX-3X and was expressed and purified as we previously described [10] . The same procedure was used with GST . Amino acids 52 to 342 of the TcTASV-C gene AM492203 ( GenBank; emb . CAM33606 . 1 ) were cloned between BamHI and KpnI restriction sites , fused in the N-term to a Histidine tag into pQE-30 ( Qiagen ) . A point mutation was introduced to change the amino acid H56R . TcTASV-CHIS was expressed and purified by standard methodologies for histidine-tagged proteins ( The QIAexpressionist ) . Purity of proteins was analyzed by SDS–PAGE , followed by staining with Coomassie Brilliant Blue . Proteins were quantified ( Bradford assay and/or Picodrop ) and dialyzed against PBS . Recombinant proteins were stored aliquoted at −80°C until use . For mice immunizations , purified recombinant proteins were incubated with a Polymyxin B resin in a column format ( Detoxy-Gel Endotoxin Removing Gel Thermo Scientific ) . Endotoxin levels were quantified by Amebocyte lysis assay ( Limulus Amebocyte Lysate Test , Lonza ) . Only preparations with endotoxin levels <100 U/mg were used . Recombinant TcTASV-CGST was digested with Factor Xa ( GE Healthcare ) and the purified TcTASV-C fragment was used to produce specific anti-TcTASV-C sera in mice [49] . The specificity of the anti-TcTASV-C sera was verified by competition assays and western blot , both against trypomastigotes lysates and recombinant proteins . Recombinant TcTASV-CGST was used to produce complete anti-TcTASV-C-GST serum in mice , following the same immunization protocol described above . The sera obtained reacted both with TcTASV-C and GST . Vero and J774 cells were grown at 37°C in a 5% CO2 humidified atmosphere in MEM or RPMI ( Gibco ) , respectively , supplemented with 10% fetal bovine serum ( Natocor ) , 10 μg/mL streptomycin ( Sigma ) , 100 U/mL penicillin ( Sigma ) . Cell-derived T . cruzi trypomastigotes were cultured by passages in Vero cells at 37°C and 5% CO2 humidified atmosphere in MEM ( Gibco Life Technologies ) supplemented with 10% fetal bovine serum , 10 μg/mL streptomycin , 100 U/mL penicillin . Trypomastigotes were harvested from supernatants of infected cells as previously described [10] . As a rule , T . cruzi stocks are kept in liquid nitrogen and all strains are regularly thawed twice a year to preserve their biological characteristics . Parasites from Sylvio ( TcI ) , 193–173 ( TcI ) , K98 ( TcI ) , Y ( TcII ) , Tul ( TcVI ) , VD ( TcVI ) , CL Brener ( TcVI ) and RA ( TcVI ) were employed [50–54] . Bloodstream trypomastigotes of the RA strain ( DTU TcVI ) were maintained in vivo by weekly passages in CF1 mice with 105 trypomastigotes , at IMPaM ( School of Medicine , University of Buenos Aires-CONICET ) and at the BLS3 laboratory at UNSAM . The Tulahuen strain expressing E . coli β-galactosidase ( Tul-β-gal ) was also maintained in vivo by passages on CF1 mice at UNSAM [25] . Purification of bloodstream trypomastigotes was essentially carried out by a Ficoll gradient with a swinging bucket rotor , essentially as previously described [55] . Bloodstream RA trypomastigotes used for EV purification , were either purified as stated above or by swimming ( 2 x 40 min at 37°C ) , essentially as described by Miranda et al ( 2015 ) [56] . Briefly , heparinized blood was diluted with 3 volumes of PBS and , after centrifuged at 300 x g for 5 min , the sample was incubated for 40 min at 37°C . The supernatant containing parasite forms was then carefully harvested–to exclude the erythrocyte containing phase–and the procedure was repeated twice . Then , trypomastigotes were pelleted , washed with PBS-1% BSA , resuspended in MEM and incubated for shedding assays , as described below . Similar volumes of blood from non-infected mice were processed in parallel and used as controls . Cell-derived trypomastigotes , were washed with MEM without serum and incubated at a concentration of 108 parasites/ml in MEM at 37°C , during 6 hours , in a 5% CO2 humidified atmosphere . Trypomastigote-secreted products ( soluble plus vesicles ) were isolated as previously described [10] . A similar procedure was carried out for bloodstream trypomastigotes . Extracellular vesicles were purified by an iodixanol density gradient ( Optiprep , Sigma ) ultracentrifugation as described by van Deun J et al ( 2014 ) [57] or by sequential ultracentrifugation as described by Bayer-Santos et al ( 2013 ) [22] . Briefly , for both procedures , after shedding , parasites were removed by centrifugation and the cell-free supernatant filtered through a 0 . 45-μm syringe filter ( Micron Separation Inc . ) . A discontinuous gradient was created by layering 2 , 7 mL of 40% , 20% , 10% and 2 , 3 mL of 5% Optiprep solutions from bottom to top in a 13 , 2 mL polyallomer tube ( Beckman Coulter ) . The cell-free supernatant of trypomastigotes ( EVs plus free secreted fraction , 2 ml ) was overlaid onto the top of the gradient , which was then centrifuged for 18 hours at 100 , 000 x g without brake at 4°C in a SW 41 Ti rotor in an Optima XL 100k ultracentrifuge ( Beckman Coulter ) . Gradient fractions of 1 mL were collected from the top of the gradient . Density was determined weighing on an electronic balance a known volume of each fraction . Alternatively , to isolate large and small extracellular vesicles , EVs plus the free secreted fraction were centrifuged at 100 . 000 x g for 2 h at 4°C to obtain the first pellet , enriched in large extracellular vesicles ( V2 ) , and the resulting supernatant was centrifuged again at 100 . 000 x g for 16 h at 4°C , to obtain the second pellet , enriched in small extracellular vesicles ( V16 ) , and the EV-free supernatant fraction ( VF ) . All ultracentrifugation steps were carried out in a 70Ti fixed angle rotor in an Optima XL 100k ultracentrifuge ( Beckman Coulter ) . All relevant methodological data of our EV’s isolation procedures have been submitted to the EV-TRACK knowledgebase ( EV-TRACK ID: EV170020 ) [58] . Extracellular vesicles were resuspended in Hepes Buffer , pH 6 . 5 , and fixed with paraformaldehyde ( 4% in Hepes ) . Negative staining was carried out on grids coated with acrylic membranes and graphene oxide . Extracellular vesicles were stained with 5 μl of 0 . 5% ammonium molybdate at pH 7 . 5 , and observed using a Zeiss EM 109T transmission electron microscope operating at 80kV; the images were acquired with a Gatan ES1000W ( 11 Mpx ) digital camera . For 2D gel electrophoresis , trypomastigotes were incubated in serum-free DMEM for 2 h a 37°C ( or at 0°C for controls ) . The medium containing the secreted antigens and the parasites were separated by centrifugation at 4000 x g for 10 min at 4°C . Pelleted parasites were washed twice in 10 mM Tris-Cl , pH 7 . 0 , 25 mM sorbitol and , after being pelleted by centrifugation , were lysed by vortexing for 30 s in 250 μl of IEF rehydration buffer ( 9M urea , 2M thiourea , 2% CHAPS , 65 mM DTT , 0 . 5% IPG buffer [Amersham Pharmacia] and 0 . 002% bromophenol blue ) with protease inhibitor cocktail ( Roche ) . The secreted material was also mixed with IEF rehydration buffer and both the trypanosome lysate and the secreted antigens were incubated at R . T for 1 h , with vortexing for 30 s every 15 min , as described by van Deursen et al ( 2003 ) [59] . Samples were loaded into Immobiline DryStrip ( pH 4–7 , 13 cm; GE Healthcare ) and isoelectric focusing carried out in an IPGphor Isoelectric focusing System for 24 h . Second-dimension SDS-PAGE was carried out in a Hoeffer SE 600 , and gels were transferred to nitrocellulose membranes in a semi-dry TE 70 PWR ( Amersham Biosciences ) . Blocking and washing solutions and antibodies used were similar to those described below for conventional western blot . Thirty million ( 30x106 ) of in vitro cell-derived trypomastigotes , or its secretion equivalent from small ( V16 ) , large ( V2 ) , total EVs or the soluble EV-free fraction ( VF ) were electrophoresed on 10% denaturing polyacrylamide gels , and transferred to nitrocellulose membranes by standard methodologies [60] . The correct transfer was verified by reversible membrane staining with Ponceau Red ( 5% w/v ) in 1% ( v/v ) acetic acid . The membrane was blocked with PBS-3% non-fat milk for 1 hour , washed with PBS-0 . 05% Tween and incubated with primary antibodies . Anti TcTASV-C ( mouse , 1/400 ) , anti HSP-70 ( rabbit , 1/1000 ) and anti-SR62 ( rabbit , 1/1000 ) were employed . Then , washes were repeated and membranes were incubated with a peroxidase-conjugated secondary antibody ( anti-mouse or anti-rabbit , both from Thermo Scientific ) for 1 hour and the washes repeated . For the detection , we used a chemiluminescent reagent ( SuperSignal West Pico , or SuperSignal West Femto , Thermo Scientific ) . The emitted signal was detected by exposure on radiographic plates ( AGFA ) . For bloodstream trypomastigotes , or its secretion equivalent from EVs , an additional blocking step with non-labelled anti-mouse IgG ( Sigma-Aldrich ) before incubation with the primary antibodies was included . Western blots were developed as indicated above or with Alexa Fluor 590 goat anti-mouse IgG or Alexa Fluor 680 goat anti-rabbit IgG as secondary antibodies ( Invitrogen ) at a 1:20000 dilution and visualized with an Oddysey Infrared Imager ( Li-Cor ) . Purified EVs ( 20 μg ) were diluted in 50 mM ammonium carbonate . Mass spectrometry analysis was carried out at Centro de Estudios Químicos y Biológicos por Espectrometría de Masa ( CEQUIBIEM ) , Argentina , in a Q Exactive HESI-Orbitrap coupled to a nano HPLC Easy-nLC 1000 ( Thermo Scientific ) . MS/MS data were used to search the all the available Trypanosoma cruzi databases at Tritrypdb ( version 30 ) [11] . Interaction assays were carried out by an ELISA-like assay , as described by Baida et al ( 2006 ) [61] . Briefly , macrophage ( J774 ) or epithelial ( Vero ) cells were cultured overnight , washed with PBS-3% BSA and fixed with 1% paraformaldehyde in PBS for 15 minutes . The fixed cells were blocked with PBS-3% BSA-1% normal goat serum for 1 hour at room temperature and washed again . Recombinant proteins ( TcTASVGST or GST ) were incubated for 1 hour at 37°C . The cells were washed and then incubated for 1 hour at 37°C with complete anti-TcTASVGST sera , which recognizes both TcTASVGST and GST proteins . Normal mouse serum was used as background control . Detection continued as for conventional ELISA technique . Three replicates per condition and three independent tests were carried out . Data were analyzed by Student t-test . A similar protocol was employed to assay the interaction EVs with Vero cells . Briefly , cells were incubated with freshly isolated EVs for 1 . 5 h at 37°C , washed and the interaction detected by a pool of sera developed against soluble and membrane antigens of trypomastigotes . Normal mouse sera and frozen EVs were used as controls . The ability of rTcTASV-C to interfere with parasite infection on Vero cells was assessed in vitro by two different methods and with two T . cruzi strains . In both set ups cells were incubated with rTcTASV-C or GST ( as a control ) , before infection . On one hand , 20000 Vero cells/well were incubated in p24 Wells ( Costar ) for 24 hs at 37°C . Then the cells were washed and incubated with recombinant proteins ( TcTASV-CGST or GST ) in MEM 4% FBS at 37°C for 30 min . CL Brener trypomastigotes ( 10:1 ) were added to the cultures , and 18 h later uninternalized parasites were washed and infection proceeded for additional 48 hs . Cells were then fixed and stained with May-Grünwald Giemsa . At least 500 cells were counted in each technical replicate , and the presence of amastigotes registered . Data were normalized to infected ( untreated ) cells; 3 independent experiments were performed with 3 technical replicates each one . On the other hand , T . cruzi bloodstream trypomastigotes ( Tulahuen strain ) expressing E . coli β-galactosidase were used to infect treated Vero cells in p96 ( Costar ) in a relation of 10:1 [25 , 26] . After an overnight incubation ( 37°C , 5% CO2 ) , cells were washed with PBS to remove non-infecting trypomastigotes and the culture maintained for additional 72 hs . Cells were then lysed with Igepal ( 1% v/v ) and β-galactosidase activity was spetrophotometrically measured with the chromogenic substrate chlorophenol red β-D-galactopyranoside ( CPRG ) . Reaction was read at 595 nm in a multi-plaque reader FilterMax F5 ( Molecular Devices ) . Purified T . cruzi bloodstream trypomastigotes ( Tul- β-gal ) were pretreated for 30 min at 37°C with anti-TcTASV-C sera ( 1/10 ) and then co-incubated ( 37°C , 5% CO2 , 18 h ) with Vero cells ( ratio 10:1 ) in MEM–5% FBS in a 96-well plate format . Parasites pretreated with anti-GST or normal sera were used as controls . Cell culture and quantification of infection were the same as stated above . Untreated parasites were used to determine 100% of infection; 3 independent experiments were performed with 3 technical replicates each one . For the preparation of plasmid DNA used in immunizations , E . coli DH5a containing a fragment of the TcTASV-C gene TcCLB . 511675 . 3 ( amino acids 233 to 305 ) cloned in pCI_Not_32 [12] or the plasmid VR1019 that contains the murine GM-CSF gene [26] were first grown as starter cultures in LB containing ampicillin at 37°C for 8 hours , then inoculated into a larger culture and grown O . N . and , finally , incubated additional 8 h in the presence of chloramphenicol ( 170 μg/ml ) for amplification of plasmid copy number . Plasmid DNA was purified with the QIAGEN EndoFree Plasmid Mega Kit ( QIAGEN , GmbH , Germany ) according to manufacturer’s instructions . Purified DNA was resuspended in TE endotoxin-free buffer and DNA concentration was estimated and stored at -20°C . For mice immunization , DNA was precipitated with ethanol and reconstituted at 1 μg/μl with sterile endotoxin-free PBS . The VR1019_GM-CSF plasmid was gently provided by Dr . Walter R . Weiss of the "Malaria Program and Pathology Division , Naval Medical Research Center , " Maryland , United States . C3H/He mice ( n = 10 per group ) were vaccinated with a prime ( plasmid DNA ) and boost ( recombinant proteins ) immunization protocol . Briefly , the first two doses consisted in intramuscular injections of plasmid DNA containing 100 μg of pCI_Not-TcTASV-C and 25 μg of VR1019_GM-CSF [12 , 28] . The third and fourth doses consisted in subcutaneous injections of mixed TcTASV-CGST and TcTASV-CHIS ( 12 . 5 μg each one ) with a colloidal suspension of aluminum hydroxide ( Sigma ) . Control groups were immunized with 100 μg of the empty plasmid backbone pCI_Not_32 plus 25 μg of VR1019_GM-CSF ( doses 1 and 2 ) and 25 μg of GST along with aluminum hydroxide ( doses 3 and 4 ) . Fifteen days after the last dose , 3 mice per group were sacrificed to evaluate cellular responses in spleen cells ( cytokine production after culture ) and the remainder 7 mice challenged with 100 bloodstream trypomastigotes of the RA strain by the intraperitoneal route [12] . Parasitemia was determined from day 7-on every 2–3 days until day 35 . Mortality was daily monitored . Spleens of immunized mice were aseptically removed and homogenized . Red blood cells were lysed and cells cultured in RPMI 1640 supplemented with 2 mM L-glutamine , 100 U of penicillin/ml , 50 μg of streptomycin/ml , and 10% FCS at a concentration of 4 × 106cells/ml in 24 well plates ( Nunc ) . Cells were stimulated with TcTASV-CGST , GST ( 10 μg/ml ) , anti-CD3 ( 0 . 2 μg/ml ) or solely maintained with culture medium ( basal control ) at 37°C in a humidified atmosphere of 5% CO2 . After 72 h , supernatants were collected , and production of gamma interferon ( IFN-γ ) and interleukin-10 ( IL-10 ) was evaluated by sandwich ELISA according to manufacturer's instructions ( BD OptEIA , Pharmingen , San Diego , CA ) . Serology against recombinant antigens or whole T . cruzi trypomastigote lysates was determined by ELISA , as we previously described [10 , 12] . Mice were bled by submandibular puncture to take serum samples 5 days before the immunization schedule start , 15 days after the last dose and at 42–62 days post infection . ELISA plates were sensitized with 50 ng of recombinant proteins or 100 ng of T . cruzi trypomastigote homogenates . Goat anti-mouse IgG , anti-IgG1 or anti-IgG2a conjugated to peroxidase ( Thermo Fisher Scientific ) were used as secondary antibodies . Reaction was revealed with 3 , 3’ , 5 , 5’-Tetramethylbenzidine ( Sigma ) and H2O2 in citrate buffer and read at 450 nm in a multi-plaque reader FilterMax F5 ( Molecular Devices ) . Parasites ( 500000/assay ) were incubated with inactivated sera ( 53°C , 40 min ) from vaccinated or control mice , for 1 h at 37°C , followed by treatment with fresh human sera ( 1:4 ) either with active or inactivated complement for an additional 1 h at 37°C [43] . Trypomastigote lysis was calculated by counting living , motile and unstained parasites in a Neubauer chamber after staining with Trypan blue . Putative B-cell epitopes in TcTASV-C proteins were predicted by Bepipred software [62] . The TcTASV-C epitopes identified in a previous work ( peptide microarray screened with human antibodies ) were also considered [29] . Peptides were purchased from Genscript and screened by ELISA . Briefly , plates were sensitized with 1 or 0 . 33 μg of peptide ( for 96 or 384 plate format , respectively ) in PBS O . N . Sera from vaccinated or infected mice were assayed at 1/100 dilution by triplicate . After incubation with a peroxidase- conjugated secondary antibody the reaction was developed as described above . The cut off was set up for each peptide as the media of the O . D . of the negative ( uninfected unvaccinated ) sera plus 3SD plus 10% . Reactivity of an experimental serum was classified positive for an X peptide , if the ratio between its O . D . for the peptide X and the cut-off value for peptide X , resulted in a value higher than 1 ( i . e . O . D . sera for peptide X/ cut-off peptide X >1 ) . For ELISA comparisons , we employed one-way ANOVA . Differences in the parasitemia between groups were determined by the Mann–Whitney U test . In survival analysis , groups were compared by the Log-rank test . In all cases , Graph Pad Prism version 5 . 01 ( GraphPad Software , USA ) was used and a P value below 0 . 05 was considered significant . | Trypanosoma cruzi is the kinetoplastid parasite that causes Chagas’ disease , a neglected infection endemic in Latin America and emerging worldwide . Being vaccines currently unavailable and treatments not completely effective , identification and characterization of parasite molecules that can be target for these interventions are urgently needed . Of particular interest are surface anchored and secreted proteins involved in parasite—host interplay . Recently , extracellular vesicles released from protozoan pathogens have been shown to alter host cell function favoring the establishment of infection . Trypomastigotes are the disseminating stage of T . cruzi , being their presence in peripheral blood a hallmark of early acute infection in mammals . While the most abundant proteins of the trypomastigote surface are fairly well characterized , little is known about other , less abundant and more recently discovered multigenic families , which could have critical functions in the parasite—host interaction . The T . cruzi Trypomastigote Alanine , Valine and Serine rich proteins ( TcTASV ) belong to a medium-size multigene family of ~40 members that remained unobserved until a few years ago when it was identified through a trypomastigote-enriched cDNA library . Almost simultaneously , an expression library immunization approach designed to discover novel vaccine antigens in T . cruzi , spotlighted the TcTASV-C subfamily , as a fragment of a TcTASV-C gene was identified in a pool of protective clones . A distinctive feature that characterizes TcTASV proteins–and particularly the TcTASV-C subfamily- is their predominant expression in trypomastigotes . Recent transcriptomic and proteomic studies uphold our previous observations that the TcTASV family is over-represented in the trypomastigote stage , and therefore could represent an interesting target for rational intervention against T . cruzi infection . Here show that TcTASV-C is mainly secreted through extracellular vesicles ( EVs ) of trypomastigotes , and is a major cargo of its content . We have also shown that TcTASV-C is much more expressed in trypomastigotes purified from blood from infected mice than in trypomastigotes harvested from in vitro cultures , suggesting that host molecules should trigger TcTASV-C expression in vivo during the infection . The immunization of mice with TcTASV-C interfered with the early acute phase of T . cruzi infection through a strong humoral immune response . TcTASV-C should be considered as a novel secreted virulence factor of T . cruzi trypomastigotes and -although its biological function is still unknown- we hypothesize its participation in the early steps of T cruzi infection in the mammalian host . | [
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] | 2018 | The protein family TcTASV-C is a novel Trypanosoma cruzi virulence factor secreted in extracellular vesicles by trypomastigotes and highly expressed in bloodstream forms |
Myristoylation is a lipid modification involving the addition of a 14-carbon unsaturated fatty acid , myristic acid , to the N-terminal glycine of a subset of proteins , a modification that promotes their binding to cell membranes for varied biological functions . The process is catalyzed by myristoyl-CoA:protein N-myristoyltransferase ( NMT ) , an enzyme which has been validated as a drug target in human cancers , and for infectious diseases caused by fungi , viruses and protozoan parasites . We purified Caenorhabditis elegans and Brugia malayi NMTs as active recombinant proteins and carried out kinetic analyses with their essential fatty acid donor , myristoyl-CoA and peptide substrates . Biochemical and structural analyses both revealed that the nematode enzymes are canonical NMTs , sharing a high degree of conservation with protozoan NMT enzymes . Inhibitory compounds that target NMT in protozoan species inhibited the nematode NMTs with IC50 values of 2 . 5–10 nM , and were active against B . malayi microfilariae and adult worms at 12 . 5 µM and 50 µM respectively , and C . elegans ( 25 µM ) in culture . RNA interference and gene deletion in C . elegans further showed that NMT is essential for nematode viability . The effects observed are likely due to disruption of the function of several downstream target proteins . Potential substrates of NMT in B . malayi are predicted using bioinformatic analysis . Our genetic and chemical studies highlight the importance of myristoylation in the synthesis of functional proteins in nematodes and have shown for the first time that NMT is required for viability in parasitic nematodes . These results suggest that targeting NMT could be a valid approach for the development of chemotherapeutic agents against nematode diseases including filariasis .
Nematode parasites are the causative agents of a large and diverse group of infectious diseases that affect millions of people , particularly in tropical and sub-tropical regions of the world . Lymphatic filariasis and onchocerciasis are chronic , disabling , neglected tropical diseases ( NTDs ) caused by filarial nematodes . Currently more than 1 . 4 billion people in 73 countries are threatened by lymphatic filariasis , with over 40 million incapacitated by the disease [1] Onchocerciasis occurs mainly in Africa with more than 99% of the 26 million infected people living in 31 countries in sub-Saharan Africa [2] . Mass drug administration ( MDA ) campaigns , involving annual large-scale treatment with albendazole together with either ivermectin ( where onchocerciasis is endemic ) or diethylcarbamazine citrate ( where onchocerciasis is not present ) to cover the entire at-risk population irrespective of disease status , form the foundation of attempts to control filarial infections . The drugs interrupt transmission by killing juvenile parasites but do not kill mature worms , and therefore multiple rounds of treatment are required before adult worms eventually die . In the absence of an adulticide , it is recommended that the MDA should be continued for 4–6 years for lymphatic filariasis [1] and 10–15 years for onchocerciasis [2] . Of particular concern to the MDA programs in Africa is co-endemic loiasis which can result in severe adverse neurological events following medication . The limitations of existing treatments and concerns for emergence of drug resistance [3] highlight the need for additional effective , safe and affordable drugs to treat the populations affected by filarial diseases . One approach to anti-infective drug discovery involves target repurposing , where targets are selected based on their homology to a target for which a drug has already been identified for another species or indication . Existing knowledge of the biochemistry , structure and medicinal chemistry around the target is leveraged to enable rapid identification of new drug candidates . For example , the rapid development of HIV protease inhibitors was largely based on chemical expertise resulting from studies on human aspartic proteases [4] , [5] . Target repurposing is a particularly attractive strategy for NTD drug discovery as it can provide an accelerated and more economical path to new treatments [6] . A key element to this approach is the identification of shared protein sequences among organisms , which has been greatly facilitated by the increased availability of genome sequences . Functional genomic studies in the pathogen or a model organism can validate candidate targets and guide a repurposing program . The genome of the free-living nematode Caenorhabditis elegans has been used extensively as a model to understand molecular pathways common to nematodes and mammals on the basis of conserved evolution . For more than 30 years C . elegans has been employed in screening for anti-nematode compounds [7] . It has also been used in pharmacology studies [8] and to gain insight into the mechanism of action of known compounds , and the development of drug resistance [9] . The ability to perform forward and reverse genetic studies in C . elegans enables comprehensive validation of putative drug targets , which is key to the success of a target-based drug discovery approach . The abundance of functional genomic data from C . elegans and the high degree of genome sequence conservation between C . elegans and the human filarial parasite Brugia malayi , a causative agent of lymphatic filariasis , has enabled the prediction of potentially essential genes in B . malayi that may be exploited as drug targets [10] . The enzyme myristoyl-CoA:protein N-myristoyltransferase ( NMT , EC 2 . 3 . 1 . 97 ) has been extensively investigated as a drug target in human cancer [11]–[13] , and in infectious diseases caused by viruses [13]–[15] , pathogenic fungi [16] , and parasitic protozoa including Trypanosoma brucei [17]–[19] , Trypanosoma cruzi [20] , Leishmania major [18] , Leishmania donovani [21] , and Plasmodium falciparum [17] , [18] , [22] . NMT is an essential monomeric enzyme responsible for the co- and post-translational modification of proteins by transferring the fatty acid myristate ( C14 . 0 ) from myristoyl-CoA to N-terminal glycine resides of a peptide/protein substrate , enabling their targeting to various membranes [23] , [24] and/or activation and stabilization of the substrate protein [17] . Numerous biological studies have validated T . brucei NMT ( TbNMT ) as a druggable target using the prototypic TbNMT inhibitor DDD85646 [19] , [23] . In this study we performed detailed molecular and biochemical studies on the NMT enzymes from C . elegans and the human filarial parasite B . malayi . We validated NMT as a new nematode drug target by performing genetic studies in C . elegans , and generated initial validation data using chemical inhibition studies with DDD85646 ( and a related derivative DDD100870 ) in C . elegans and B . malayi . We also predicted the downstream target proteins in B . malayi that may be disrupted as a result of NMT inhibition . These studies represent the first analysis of N-myristoyltransferases from a helminth parasite , and demonstrate the importance of lipidation in nematodes including filarial worms . Our data strongly support repurposing N-myristoyltransferase for development of new therapies against nematode infection including filarial diseases .
Full-length NMT sequences from Brugia malayi ( XP_001896037 ) , Caenorhabditis elegans ( NP_498326 . 1 ) , Loa loa ( XP_003141266 . 1 ) , Trypanosoma brucei ( EAN78792 . 1 ) , Plasmodium falciparum ( AAF18461 . 1 ) , Leishmania major ( AAG38102 . 1 ) , Saccharomyces cerevisiae ( NP_013296 . 1 ) , Homo sapiens ( AAH06376 . 1 ) and Ascaris suum ( ERG81997 . 1 ) were retrieved from NCBI . B . malayi NMT was used to query additional databases to identify unannotated orthologs in other sequenced nematode genomes . The orthology assignments determined by TBLASTN analysis were recovered and contig sequences were assembled manually to produce a full-length protein sequence . The following list of nematode NMT proteins were manually curated in this manner: Wuchereria bancrofti ( http://www . broadinstitute . org/annotation/genome/filarial_worms/Blast . html ? sp=Stblastn; contigs WUBG_03313 . 1 , WUBG_18444 . 1 , and WUBG_06452 . 1 ) , Trichinella spiralis ( https://www . wormbase . org/tools/blast_blat; GL622787 , Length = 12041450 ) , Dirofilaria immitis ( http://xyala . cap . ed . ac . uk/downloads/959nematodegenomes/blast/filareu . php; nDi . 2 . 2 . scaf00055 , Length = 228449 ) , Acanthocheilonema viteae ( http://xyala . cap . ed . ac . uk/downloads/959nematodegenomes/blast/filareu . php; nAv . 1 . 0 . scaf00057 , length = 84838 ) , Litomosoides sigmodontis ( http://xyala . cap . ed . ac . uk/downloads/959nematodegenomes/blast/filareu . php; nLs . 2 . 1 . scaf00231 , length = 65134 and nLs . 2 . 1 . scaf00078 , length = 103883 ) , Onchocerca volvulus ( http://xyala . cap . ed . ac . uk/downloads/959nematodegenomes/blast/filareu . php; nOv_contig22006 , length = 20337 ) , and Onchocerca ochengi ( http://xyala . cap . ed . ac . uk/downloads/959nematodegenomes/blast/filareu . php; nOo . 2 . 0 . Scaf00398 , length = 32289 ) . Protein alignment was performed using the ClustalW alignment software ( http://www . genome . jp/tools/clustalw/ ) and displayed using BOXSHADE ( http://www . ch . embnet . org/software/BOX_form . html ) . The residues involved in binding of L . major NMT to myristoyl-CoA and DDD85646 determined from co-crystal structural analyses were obtained from: http://www . rcsb . org/pdb/explore/explore . do ? structureId=2wsa . A rooted phylogenetic tree with branch length was generated using the UPGMA ( Unweighted Pair Group Method with Arithmetic Mean ) ClustalW software . Sequence identity values between B . malayi and an ortholog were generated using BLASTP . A homology model of the B . malayi NMT structure was built based on several structurally characterized NMT's , including enzymes from Leishmania donovani ( 2wuu ) , Plasmodium vivax ( 4a95 ) , and Saccharomyces cerevisiae ( 2p6f ) using the PHYRE program ( http://www . sbg . bio . ic . ac . uk/phyre2/html/page . cgi ? id=index ) . The FASTA sequence for B . malayi was uploaded to the Protein Homology/analogY Recognition Engine V 2 . 0 ( Phyre2 ) server , where models were automatically generated using the best possible template model [25] . The predicted structure of B . malayi NMT was then compared to the structure of L . major NMT ( 2wsa ) bound to myristoyl-CoA and inhibitor DDD85646 using the UCSF Chimera software [26] . Full-length CeNMT cDNA was reverse transcribed from total C . elegans RNA and then amplified using the following primers: ( GATCGGGAATTCATATGTCCCACGGACACAGTC ) and ( GATCCCGCTCGAGTTGAAGAACAAGCCCGATTT ) containing a NdeI or XhoI restriction site ( underlined ) , respectively , to enable cloning into the corresponding sites of the expression vector . BmNMT ( Bm1_22900 ) was a synthetic ( codon-optimized ) version of the gene ( GenScript Corporation , Piscataway , NJ ) designed to optimize expression in E . coli . Each nematode NMT gene was cloned into the pET19b vector to express a fusion protein with a 10-His tag at the N-terminus . The insert was then fully sequenced for verification . Plasmids were transformed into the E . coli strain NiCo21 ( DE3 ) ( NEB ) for protein expression . Cultures were grown at 37°C and induced with 0 . 4 mM IPTG at 16°C overnight . The His-tagged proteins were extracted in lysis buffer ( 20 mM NaH2PO4 , 300 mM NaCl , 10 mM imidazole , pH 8 . 0 ) containing protease inhibitors ( Complete EDTA-free Protease Inhibitor , Roche ) , and purified on 1 mL HisTrap HP ( GE Healthcare ) using an ÄKTA-FPLC system ( GE Healthcare ) . Protein was eluted using a linear ( 0–100% ) gradient of elution buffer ( 20 mM NaH2PO4 , 300 mM NaCl , 250 mM imidazole , pH 8 . 0 ) . Fractions containing NMT were pooled and dialyzed into storage buffer ( 20 mM Tris-HCl , 100 mM NaCl , 2 mM DTT , 2 mM EGTA , 2 mM EDTA , and 50% glycerol , pH 7 . 5 ) . Nematode NMT proteins were assayed using synthetic peptides GGVMSYRRR ( ARL-1 ADP ribosylation factor related protein ) , GHSHSTGKRR ( ABL-1 tyrosine kinase ) and GCLFSKERR ( SRC-1 tyrosine kinase ) , derived from N-terminal sequence of several C . elegans N-myristoylated proteins . One or two basic amino acids ( underlined ) were added at the C-terminal end of the peptide to generate a positive charge at pH 7 . 3 . The NMT assay was performed as described with some minor modification [18] , [27] . Reactions were carried out in a final volume of 50 µL containing 30 mM Tris-HCl buffer ( pH 7 . 5 ) , 0 . 5 mM EDTA , 0 . 45 mM EGTA , 4 . 5 mM 2-mercaptoethanol , 1% Triton X-100 , 1 mM peptide and 5 ng purified enzyme . N-myristoylation was initiated by the addition of 0 . 5 mM myristoyl-CoA . Myristoyl-CoA was prepared by mixing equal volumes of cold myristoyl-CoA ( 1 mM , Chem-Impex International ) with [3H] myristoyl-CoA ( 5 µM , 42 Ci/mmol , Perkin Elmer ) . After a 10 minute incubation at 30°C , the reaction was terminated by spotting 25 µL reaction mixture onto P81 phosphocellulose paper ( Whatman ) . The paper was dried under a heat lamp and washed three times for 10 minutes in 20 mM Tris-HCl pH 7 . 5 prior to scintillation counting using a Liquid scintillation Analyzer ( Tri-Carb 2900 TR , Perkin Elmer ) . All reactions were performed in duplicate , and reactions performed in the absence of enzyme , or peptide , were included as controls . Background radioactivity values generated from control samples were subtracted from the values obtained from experimental samples . To determine the kinetic constants: Km , Vmax , Kcat and Kcat/Km of B . malayi and C . elegans NMT for myristoyl-CoA , the concentration of myristoyl Co-A was varied from 0 . 1–50 µM in the presence of 1 mM peptide ( ABL-1 ) and 5 ng enzyme . Reactions were performed in triplicate . Optimum fitting of data to the Michaelis-Menten equation was calculated using non-linear regression software ( http://www . colby . edu/chemistry/PChem/scripts/lsfitpl . html ) . One unit of NMT activity is defined as 1 pmol of myristoylated peptide formed per min . Inhibition assays for compounds DDD85646 and DDD100870 were performed in a final volume of 50 µL containing 10 mM Tris pH 7 . 5 , 0 . 5 mM DTT , 0 . 1 mM EDTA , 10 nM enzyme and 5 µM myristoyl-CoA ( as above ) . Enzyme mixtures were preincubated with various concentrations of each compound ( 0 . 0025–1 µM ) dissolved in DMSO for 7 . 5 min at 25°C then the reactions were initiated by adding peptide substrate ( ABL-1 , 12 . 5 µM ) . Following a 15 minute incubation at 25°C , reactions were terminated by spotting 25 µL onto P81 phosphocellulose paper , washed , dried and prepared for scintillation counting as described previously . Reactions performed in the absence of inhibitor or peptide were included as controls . Experiments were performed in triplicate . Mean IC50 values were determined in the concentration range of 2 . 5–10 nM . C . elegans culture and handling were performed following standard procedures [28] . N2 strain var . Bristol was used as wild-type [29] . Mutant strains ( NL4256 rrf-3 ( pk1426 ) , lin-15b ( n744 ) ;eri-1 ( mg366 ) ) were obtained from the C . elegans stock center ( CGC , University of Minnesota ) and also ( nmt-1 ( tm796 ) /hT2[qla48] ) from Dr . S . Mitani ( National Biosource Project of Japan , Tokyo Women's Medical University School of Medicine , Tokyo ) . Three C . elegans strains were used for RNAi knockdown of NMT: C . elegans wild-type and two RNAi sensitive C . elegans strains , one containing a mutation in rrf-3 [30] , and a second strain carrying mutations in both eri-1 and lin-15B I [31] . RNAi was performed by feeding an Escherichia coli strain expressing dsRNA [32] . Full-length NMT cDNA was subcloned into the feeding vector pL4440 . HT115 E . coli transformed with this construct were spread on agar plates containing NGM supplemented with 1 mM isopropylthio-β-D-galactoside ( IPTG , Sigma ) and 50 µg/mL ampicillin , and incubated overnight at room temperature to induce dsRNA expression . RNAi assays were carried out by feeding L4-staged worms HT115 E . coli expressing dsRNA corresponding to NMT or pL4440 plasmid vector without CeNMT . The relative health of the progeny of each worm was determined qualitatively by its appearance relative to the controls over the course of several days . For each RNAi experiment , 50 worms were assayed from each of the 3 strains . Phenotypic analyses were also performed using the mutant strain nmt-1 ( tm796 ) /hT2[qls48] . To verify the deletion , genomic DNA was prepared from a single mutant animal , and one worm from heterozygous and wild-type strains using standard methods [33] . The deletion was confirmed on the basis of the change in size of a PCR product using forward primer TCAACTGATTGCACCGTCAT and reverse primer AAGCGGAACATGGAATCATC . PCR reactions were carried out using 25 picomoles of each primer and LongAmp DNA Polymerase ( NEB ) . Bands of the expected sizes were obtained . Wild-type C . elegans were grown on NGM plates seeded with OP50 E . coli . Compound screening commenced with L4-staged worms placed into a well of a sterile 96-well micro titer plate ( Falcon 3072 ) containing a 100 µL suspension of previously frozen HB101 E . coli bacteria in S medium . Various concentrations of compound ( 25 , 50 or 100 µM ) or DMSO ( control ) were then added . Plates were maintained at 20°C in a humidity chamber for 7 days . Worm growth and development was scored daily by measuring a decrease in OD600 nm resulting from consumption of E . coli , and by microscopic examination of the size and number of F1 progeny produced on day 3 of the experiment . For each condition , 10 L4-stage worms were used , and the average ± standard deviation was plotted . Living B . malayi worms including adult female , adult male and microfilariae were purchased from TRS labs ( Athens , GA ) . Worms were washed extensively with RPMI 1640 medium prior to culture in RPMI 1640 medium supplemented with 2 mM glutamine , 10% Fetal Calf Serum ( Gibco ) and 100 µg/mL streptomycin , 100 units/mL penicillin , 0 . 25 µg/mL amphotericin B ( Sigma ) at 37°C , in 5% CO2 . After overnight recovery , adult worms were separated into 3 different groups each containing either 8 females ( 2 worms/well ) or 12 males ( 3 worms/well ) . Compounds ( DDD85646 or DDD100870 ) at 100 µM or 50 µM , or 1% DMSO were added to the culture medium . Microfilariae were cultured in 24-well plates and compounds were added to the culture medium at a final concentration of 100 µM , 50 µM , 25 µM or 12 . 5 µM . Experiments were performed in at least triplicate . The culture media were replaced every other day with fresh media containing compound or DMSO only . Parasite motility was video-recorded daily and analyzed at the end of the experiment ( day 7 ) . Parasite motility was assessed and scored in the range 0–20 ( most motile ) . Observations are expressed as a percentage of the motility relative to the motility scored on day 0 of the experiment . Counting numbers of parasites on days 1 , 3 and 5 assessed production of microfilariae from female worms . The data obtained from triplicate samples are expressed as mean ± standard deviation . To identify potential targets for N-terminal glycine myristoylation in B . malayi , predicted myristoylated proteins in the proteome of C . elegans were retrieved ( http://mendel . imp . ac . at/myristate/myrbase/28731NEgenpept227MYRclusterV05 . html ) and used to query the genome of B . malayi . Homologs with a BLASTP E-Value <e−10 were analyzed using the MYR predictor ( http://mendel . imp . ac . at/myristate/SUPLpredictor . htm ) to predict myristoylation sites . Protein sequences that were scored as “Reliable” or “Twilight zone” were retained and duplicates were discarded .
Both C . elegans and B . malayi genomes contain a single copy NMT gene predicted to encode a N-myristoyltransferase which catalyzes the covalent attachment of fatty acid myristate to the N-terminal glycine of proteins ( Fig . 1 ) . In C . elegans , gene T17E9 . 2 encodes three isoforms: A ( 450 aa , 51 kDa , pI 7 . 6 ) , B ( 452 aa , 51 kDa , pI 7 . 9 ) and C ( 403 aa , 46 kDa , pI 8 . 6 ) . All isoforms share the same amino acid sequence , with isoforms A and B possessing an additional 47 or 49 amino acids at the N-terminus , respectively . BmNMT encodes one 472 aa protein ( XP_001896037 ) of 54 kDa with a pI of 6 . 6 . C . elegans and B . malayi NMTs share approximately 60% amino acid identity and 75% similarity to each other ( Fig . 2 ) . To investigate if NMT is present in other nematodes , BmNMT was used as a query to search ∼300 , 000 EST sequences at Nematode . net [34] that are available for 30 different parasitic nematodes species . Matching NMT sequences were found in free-living nematodes and a highly diverse group of parasitic nematode species found in humans ( Strongyloides stercoralis ) , animals ( Strongyloides ratti , Ancylostoma caninum , Teladorsagia circumcincta ) and plants ( Meloidogyne hapla , Heterodera glycines ) representing four major clades of the phylum Nematoda ( data not shown ) . To determine the degree of conservation of the enzyme among nematodes , full-length sequences ( Fig . S1 ) were obtained for a number of species ( B . malayi , C . elegans , Loa loa , Ascaris suum , Wuchereria bancrofti , Trichinella spiralis , Dirofilaria immitis , Acanthocheilonema viteae , Litomosoides sigmodontis , Onchocerca volvulus and Onchocerca ochengi ) . The relationship of the nematode enzymes to other eukaryotic ( Homo sapiens , Saccharomyces cerevisiae ) and prokaryotic ( Trypanosoma brucei , Plasmodium falciparum , Leishmania major ) NMTs was also examined . Phylogenetic tree analyses ( Fig . 2 ) indicated that the filarial nematode sequences ( Clade III ) form a close cluster with a high degree of conservation ( greater than 88% identity , 94% similarity ) . Trichinella spiralis ( Clade I ) and Ascaris suum ( Clade III ) enzymes are 73% and 84% similar to B . malayi NMT , respectively . B . malayi NMT also shares significant similarity to protozoan ( 56–62% ) , human ( 64% ) and yeast ( 59% ) enzymes . Multiple sequence alignment of B . malayi NMT and orthologs from Loa loa , O . volvulus , C . elegans , S . cerevisiae , L . major , T . brucei and H . sapiens also revealed conservation in the residues involved in binding to myristoyl-CoA ( Fig . S2 ) [23] . Consistent with other NMT enzymes , the nematode NMTs are divergent at their N-termini , a region not involved in substrate binding [21] , [23] , and all possess an N-terminal extension not present in the previously studied NMTs from protozoa or yeast . A homology model indicates that B . malayi NMT possesses a canonical NMT structure with a typical NMT fold characteristic of the Acyl-CoA N-acyltransferases superfamily [35] . The NMT fold basically consists of a large saddle-shaped β-sheet that is flanked on both of its faces by several helices ( Fig . 3A ) . The model generated is based on the structurally characterized NMTs from Saccharomyces cerevisiae ( c2p6fA ) and Leishmania donovani ( 2wuu ) , and is predicted to have a 100% confidence level . When the predicted B . malayi NMT structure is compared with the structure of the Leishmania major NMT ( 2wsa ) enzyme bound to myristoyl-CoA and inhibitor DDD85646 , a high degree of conservation was revealed in the myristoyl-CoA binding site as well the drug binding pocket ( Fig . 3B ) . The 2 small helixes ( Fig . 3B , arrow ) formed by an insertion of 21 amino acids in L . major NMT are replaced with a loop in B . malayi NMT that is unlikely to have any major effects on the binding of substrate or inhibitor . To verify their NMT activities , recombinant C . elegans NMT ( 51 kDA ) and B . malayi NMT ( 54 kDA ) were expressed , purified and assayed using several synthetic peptides containing a canonical myristoylation motif derived from the N-terminal sequence of three C . elegans proteins . BmNMT ( Bm1_22900 ) was a synthetic ( codon-optimized ) version ( Fig . S3 ) of the gene ( GenScript Corporation , Piscataway , NJ ) designed to optimize expression in E . coli . Expression studies using BmNMT cDNA isolated by RT-PCR ( Fig . S3 ) generated largely insoluble protein . Both recombinant proteins displayed similar levels of activity using the various peptides , with the highest activity observed with ABL-1 ( Fig . 4 ) . Kinetic analyses of recombinant nematode NMTs were performed using ABL-1 as the peptide . The Km , Vmax , Kcat and Kcat/Km for myristoyl-CoA were determined for both enzymes ( Fig . S4 ) . The C . elegans Km = 12 . 2±0 . 91 µM ) and B . malayi ( Km = 4 . 4±0 . 12 µM ) enzymes showed similar affinities for myristoyl-CoA . Kcat/Km values were also comparable for both C . elegans ( 0 . 13 s−1 µM−1 ) and B . malayi ( 0 . 15 s−1 µM−1 ) NMTs . While these values cannot be compared directly with those measured for the NMTs of other species as the assay substrates and conditions are different , the data obtained are of the expected order of magnitude . DDD85646 is a known inhibitor of NMT enzymes from several trypanosomatids . DDD85646 inhibits the proliferation of T . brucei in culture with >200 fold selectivity over mammalian cells [19] , [23] . Given the degree of conservation observed in comparative sequence and structural analyses between nematode and trypanosome NMT enzymes , we examined the potency of DDD85646 and its analogue DDD100870 ( Fig . 5 ) against C . elegans and B . malayi NMT proteins ( Fig . 6 ) . A dose-dependent inhibition of C . elegans NMT activity with an IC50 value of ∼10 nM was observed for both compounds ( Fig . 6A ) . The IC50 value of DDD85646 for B . malayi NMT is also ∼10 nM . DDD100870 was a more potent inhibitor of the filarial enzyme since an IC50 value of 2 . 5 nM was obtained . To investigate the importance of C . elegans NMT in vivo , phenotypic analyses were performed on a strain carrying a deletion allele ( nmt-1 ( tm796 ) /hT2[qls48] ) ( Fig . 7A , B ) and on worms with reduced endogenous activity resulting from RNAi [36] . The deletion was confirmed by PCR analysis . DNA prepared from wild-type and heterozygous worms generated a band of approximately 1295 bp , whereas a smaller band ( 799 bp ) was obtained from both heterozygous and mutant animals ( Fig . 7B ) . The C . elegans nmt gene comprises four exons and the size of the smaller band is consistent with a deletion involving exon 1 and exon 2 ( partial ) ( Fig . 7A ) . Three C . elegans strains were used for RNAi knockdown of NMT: C . elegans wild-type and two RNAi sensitive C . elegans strains , one containing a mutation in rrf-3 [30] , and a second strain carrying mutations in both eri-1 and lin-15B [31] . Homozygous nmt-1 ( tm796 ) /hT2[qla48] animals had an obvious phenotype marked by a maternal effect larval lethality with no viable worms present in the F2 population . In wild-type C . elegans , RNAi knockdown of CeNMT expression resulted in a severe growth defect , while in the RNAi sensitive strains ( rrf-3 and eri-1; lin-15B ) larval arrest was observed ( Fig . 7C ) . No abnormal phenotype was observed for these three strains when fed E . coli containing the pL4440 plasmid without CeNMT . These data demonstrate depletion of NMT activity in nematodes causes severe developmental defects and establishes the requirement of NMT for viability in C . elegans . The potent activities of DDD85646 and DDD100870 in nematode NMT activity assays and requirement of NMT for viability in C . elegans prompted evaluation of the compounds for in vivo activity against C . elegans and B . malayi . C . elegans L4s were treated with 25 , 50 or 100 µM of each compound and worm growth and development was scored daily by measuring a decrease in OD600 nm resulting from consumption of E . coli ( Fig . 8A ) and by microscopic examination of the size and number of F1 progeny ( Fig . 8B ) . In control worms exposed to DMSO alone there was a rapid consumption of the E . coli food source as indicated by a decline in OD600 value . DDD100870 treated worms showed slower feeding/growth that was dose dependent , with 100 µM compound resulting in almost complete inhibition of feeding . In contrast , 100 µM of the related compound DDD85646 only slightly delayed the clearance of bacteria from the culture . On day 3 , DDD100870 treated worms showed a concentration dependent decline in the number of F1 progeny produced ( Fig . 8B ) while no such effect was observed for DDD85646 ( data not shown ) . The progeny of controls averaged ( 140 worms ) 930 µm in length , whereas worms treated with 100 µM DDD100870 resulted in no progeny or fewer progeny of smaller size ( 360 µm long ) . Worms exposed to 50 and 25 µM DDD100870 showed a dose-dependent decline in the number of F1 progeny produced and their size . Adult worms and microfilariae of B . malayi were cultured in the presence of both NMT inhibitors ( Fig . 9 ) . The parasites were observed for changes in motility and production of microfilariae by adult female worms compared to control worms exposed to 1% DMSO . Both DDD85646 and DDD100870 exerted a significant and profound effect on B . malayi , though DDD100870 ( effective against C . elegans ) was more potent than DDD85646 ( Fig . 9 ) . A decline in the motility of exposed adult female ( Fig . 9A ) and male ( Fig . 9B ) worms was apparent within 24 hours of treatment . By day 6 , DDD100870-treated adult worms showed little movement , while the control worms still displayed vigorous activity . Interestingly , DDD85646 displayed more potent activity against female worms than male worms . Microfilariae were more sensitive than adult worms to both compounds with concentrations as low as 12 . 5 µM immobilizing the worms within 24 hours of exposure ( Fig . 9C ) . Microfilariae production by female worms was also severely affected by treatment and a decline was apparent at 24 hours post culture ( Fig . 9D ) . To predict downstream target proteins that may be disrupted due to NMT inhibition by compounds DDD85646 and DDD100870 , a bioinformatics approach was used to identify potential targets for N- myristoylation in B . malayi . Predicted myristoylated proteins in the proteome of C . elegans ( 145 clusters ) were retrieved from MYRbase and used to query the B . malayi genome . A total of 40 unique protein sequences were obtained; 30 scored “reliable” and 10 designated “twilight zone” ( Table 1 ) . The putative substrates are involved in diverse and essential pathways . These include ADP-ribosylation factors , hydrolases , protease activity , receptor activity and several are protein kinase domain containing proteins . In C . elegans , the majority of the predicted substrates ( 31/40 ) display multiple developmental defects and impaired survival in RNA interference assays ( Table 1 ) , and include the phenotypes we observed in RNAi knockdown of CeNMT and in mutant worms carrying a deletion allele . We cannot exclude the possibility that NMT has unknown functions in addition to myristoyltransferase activity . However , it is most likely that the pleiotropic effects observed are likely caused by inhibition of myristoylation of multiple important substrates , demonstrating the requirement of protein myristoylation in nematodes .
NMT inhibitors are being investigated by various groups for diseases including leishmaniasis [18] , Chagas' disease [20]–[21] , African sleeping sickness [17]–[19] , [23] , and malaria [17] , [18] , [22] , [37] . The protozoan and nematode enzymes share significant similarity to human NMT yet some compounds show selectivity largely as a result of affinity and differences in the off-rates . Current drug development projects are focusing on this parameter for optimization of selectivity [23] . At the time of writing all of these programs are in the discovery phase seeking to identify clinical candidates . To our knowledge , there have been no biochemical studies on NMT enzymes , or their substrates , from any helminth parasite . Even in the widely studied free-living nematode C . elegans , there is a paucity of information available . Fatty acylation of polypeptides has been experimentally demonstrated in extracts including myristoylation following metabolic labeling of C . elegans using [3H]-myristic acid [38] , [39] . However , only one of many labeled proteins in the extracts was identified , namely the catalytic subunit of cAMP-dependent protein kinase [38] . In this study we established the importance of myristoylation in C . elegans and also in a parasitic nematode responsible for lymphatic filariasis . Our finding of highly conserved NMT sequences in the genomes of many nematode species suggests that myristoylation is likely required for the synthesis of functional nematode proteins . Comparative sequence and structural analyses between nematode and T . brucei NMT enzymes enabled us to consider a drug repurposing strategy using the prototypic TbNMT inhibitor , DDD85646 , which has potent activity against T . brucei enzyme and cells in culture ( nM range ) [19] , [23] . The compound is also active against T . cruzi , although ∼10–20 -fold more compound is required for enzyme inhibition , and ∼1000-fold more ( µM range ) to inhibit parasite growth [40] . The cause for the reduced potency against intact T . cruzi is not known but differences in the rate of plasma membrane turnover , cellular pharmacokinetics of drug uptake or efflux , or differences in other essential biological functions requiring N-myristoylation have been proposed [40] . DDD85646 and DDD100870 are potent inhibitors of the nematode enzymes ( nM range ) , and possess activity against larval stages and adult worms of B . malayi ( µM range ) . Interestingly , despite comparable IC50 values for both compounds in C . elegans NMT enzyme assays , only compound DDD100870 showed micromolar activity against intact C . elegans . The need for substantially more compound to detect activity against the nematode has also been reported in other enzyme-based screening campaigns for new filarial leads [41]–[43] . It is thought that the outermost layer of the cuticle is the main barrier to penetration of drugs , stains , and other chemicals [44] , [45] . Further studies are needed to determine the physicochemical factors that favor the absorption of compounds in nematodes . This knowledge would facilitate confirming the linkage between enzyme inhibition and organism activity , and greatly expedite the discovery and optimization of anthelmintic leads . We also pursued a genetic approach to assess the requirement of the enzyme in C . elegans . Knockdown of endogenous activity using RNAi resulted in severe growth defects and larval arrest , and gene deletion caused maternal effect lethality with no viable worms present in the F2 population . The importance of the gene in B . malayi can be inferred from our RNAi analysis since there is good concordance between the phenotypes resulting from RNAi knockdown of orthologous genes in parasitic nematodes and C . elegans [46]–[48] . Essential roles for NMT have also been genetically validated in other pathogens including pathogenic fungi Candida albicans [49] and Cryptococcus neoformans [50] , and the kinetoplastid protozoan parasites L . major [51] , [52] , L . donovani [21] , T . cruzi [40] , and T . brucei [52] . Overexpression of NMT in T . brucei causes gross changes in parasite morphology , including the subcellular accumulation of lipids , leading to cell death [51] . In Drosophila , a null mutation of the single NMT gene causes disruption of the actin cytoskeleton and ectopic apoptosis in embryos , possibly attributed to loss of function of myristoylated tyrosine kinases [53] . Detailed studies performed in T . brucei indicate that inhibition of myristoylation of multiple substrates accounts for the effectiveness of NMT inhibitors in cell culture and in vivo [19] . While there remains incomplete knowledge of the targets of NMT in T . brucei and their subsequent downstream effects , trypanosomatids mainly use myristate for incorporation into glycol-phosphatidylinositol ( GPI ) anchors that tether the major surface glycoproteins and glycoconjugates to the external surface of the plasma membrane [54] . Recent bioinformatics analysis suggests there are in excess of 60 potential substrates in this organism [55] . Similar genome sequence analysis has identified 62 putative protein substrates for N-myristoylation in L . major [51] , [56] and more than 40 substrates in P . falciparum [22] . Many of the malaria targets have been validated experimentally in a recent study [37] . In humans and other eukaryotes , including C . elegans , approximately 0 . 5–0 . 8% of all proteins in the genome are myristoylated [57] . The biological role of myristoylation in nematodes is poorly understood and the effect of NMT inhibition in these organisms is probably complex , likely involving the functionality of many proteins that are essential for viability . This is consistent with our genetic studies in C . elegans and the finding that the majority of the predicted substrates in C . elegans display multiple developmental defects and impaired survival in RNAi screens . We predicted potential substrates in B . malayi encompassing a range of functions , including SRC-1 which encodes a non-receptor tyrosine kinase involved in cell signaling pathways specifying cell fate and division in C . elegans [58] , [59] . The majority of the substrates are present in the proteome of one or more developmental stages of B . malayi [60] . Collectively , these data provide evidence for the importance of NMT activity in nematodes and provide some insight into the many diverse pathways that are likely dependent on myristoylated proteins . For further development of NMT as a drug target it would be of great interest to identify these proteins and evaluate the impact of inhibition of NMT activity resulting from genetic or chemical knockdown of the enzyme . Myristoylation is an unexplored area for drug discovery in nematodes . Our studies have shown for the first time that NMT is a potential drug target in filarial parasites . The discovery of a related family of lead molecules , originally identified as protozoan NMT inhibitors , with activity against microfilariae and adult worms is particularly exciting and highlights the potential value of a repurposing approach to new drug discovery for filarial parasites . | Lymphatic filariasis and onchocerciasis are neglected tropical diseases caused by filarial nematodes . The limitations of existing drugs to treat these infections highlight the need for new drugs . In the present study , we investigated myristoylation , a lipid modification of a subset of proteins that promotes their binding to cell membranes for varied biological functions . The process is catalyzed by N-myristoyltransferase ( NMT ) , an enzyme which has been validated as a drug target in protozoan parasites . We performed kinetic analyses on Caenorhabditis elegans and Brugia malayi NMTs . NMT inhibitors were active against B . malayi microfilariae and adult worms , and C . elegans in culture . RNA interference and gene deletion in C . elegans further demonstrated that NMT is essential for nematode viability . Our genetic and chemical studies indicate the importance of myristoylation in the synthesis of functional proteins in nematodes and have shown for the first time that NMT is required for viability in parasitic nematodes . These results suggest that targeting NMT could be a valid approach for the development of new therapies against nematode infection including filarial diseases . | [
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] | 2014 | A Target Repurposing Approach Identifies N-myristoyltransferase as a New Candidate Drug Target in Filarial Nematodes |
Intracellular triacylglycerol ( TAG ) is a ubiquitous energy storage lipid also involved in lipid homeostasis and signaling . Comparatively , little is known about TAG’s role in other cellular functions . Here we show a pro-longevity function of TAG in the budding yeast Saccharomyces cerevisiae . In yeast strains derived from natural and laboratory environments a correlation between high levels of TAG and longer chronological lifespan was observed . Increased TAG abundance through the deletion of TAG lipases prolonged chronological lifespan of laboratory strains , while diminishing TAG biosynthesis shortened lifespan without apparently affecting vegetative growth . TAG-mediated lifespan extension was independent of several other known stress response factors involved in chronological aging . Because both lifespan regulation and TAG metabolism are conserved , this cellular pro-longevity function of TAG may extend to other organisms .
Lipid is essential for all life forms on earth . Polar lipids , most notably phospholipids , are the primary components of biological membranes , whereas neutral lipids such as triacylglycerols ( TAG; or triglycerides , TG ) , are long believed to store excessive energy and to provide thermal and physical insulation for animals . By esterifying three molecules of fatty acids to the glycerol backbone , TAG packs the highest density of chemical energy among major biomolecules , consistent with a role in storing surplus energy . In addition , TAG metabolism is linked to the overall lipid homeostasis in cells and the organism [1] . This is commonly achieved via diacylglycerol , DAG , which is a shared precursor for TAG and phospholipids biosynthesis . Interestingly , in many organisms , TAG accumulation is not a response of surplus nutrients , but of different stresses . For example , starvation for nitrogen , phosphorus , or sulfur causes a model photosynthetic microalga Chlamydomonas reinhardtii to accumulate TAG [2 , 3] . Even the oleaginous marine alga Nannochloropsis species increase the TAG content by 50% when starved of nitrogen , and to a lesser extent when stressed by high light and salinity [4] . In animals , mild ( 5% ) calorie restriction administered to laboratory mice shifted the relative abundance of fat and muscle by increasing the fat mass by 68% , and reducing the lean mass by 12% , with total bodyweight remained unchanged [5] . In budding yeast Saccharomyces cerevisiae , a minimal amount of TAG is synthesized by vegetatively growing cells . When glucose becomes limited and that cells enter the stationary phase , TAG synthesis rises sharply [6] . One apparent reason for these organisms to maintain a larger storage of TAG under stress is to cope with the uncertainties of the environments . By storing chemical energy and materials for membrane lipid biosynthesis , TAG helps the underlying cells quickly resume robust metabolism and growth when conditions improve . On the other hand , whether the existence of TAG in cells affords other benefits for survival through the environmental stress remains an unaddressed question . TAG is composed of a glycerol backbone esterified to three fatty acids by acyl coenzyme A:diacylglycerol acyltransferases , DGATs , and phospholipid:diacylglycerol acyltransferases , PDATs . In budding yeast , Dga1p and Lro1p are the major DGAT and PDAT , respectively [7] . Of these two , Lro1p appears to be responsible for TAG synthesis in vegetatively growing cells , whereas Dga1p contributes more significantly to the post-diauxic shift accumulation of TAG [8–10] . In addition to TAG , fatty acids can be esterified to sterols to form steryl esters ( SE ) , another class of storage neutral lipids [10] . The two major enzymes responsible for SE biosynthesis are Are1p and Are2p [10] . In contrast to the stark differentiation of TAG abundance in log and stationary phase cells , SEs are maintained at a constant level at different phases of the growth curve [11] , suggesting a function unique to TAG in the stationary phase . Intriguingly , deleting the four major neutral lipid biosynthetic genes ( DGA1 , LRO1 , ARE1 , ARE2 ) , while causing yeast cells to lose practically all storage neutral lipids , does not result in significant deleterious effects in vegetatively growing cells [10] . However , these lean cells are hypersensitive to exogenous fatty acids and die with a phenotype of membrane over-proliferation [12] , indicating that maintaining the capacity of incorporating excessive free fatty acids in the form of TAG or SE affords an important means to prevent lipotoxicity of free fatty acids . Accumulation of TAG and SE in the ER membrane causes expansion of the membrane , which eventually buds out to form lipid droplets ( LD ) , a dynamic phospholipid monolayered organelle that has gained increasing research interests [13 , 14] . A variety of proteins have been found associated with LD , including multiple TAG and SE hydrolases and signaling proteins that together play important roles in lipid homeostasis [15] . The yeast TGL3 and TGL4 genes encode the two major TAG lipases in yeast; additional lipases Tgl5p , Ayr1p and Lpx1p appear to be less robust enzymatically [16] . Tgl4p , which is thought to be the functional orthologue of the mammalian ATGL [17 , 18] , has been shown to be regulated by Cdk1-mediated phosphorylation in G1-to-S transition of dividing cells [19] . Blocking this phosphorylation event delays bud emergence . Deleting either or both major TAG lipases causes accumulation of TAG in stationary phase cells without a clear growth defect [18] . Similar to TAG , SE can be broken down by functionally redundant lipases Tgl1p , Yeh1p , and Yeh2p [20 , 21] . SE lipase triple knockout cells , aside from possessing a significantly larger pool of SE , are phenotypically indistinguishable from the wildtype counterpart [20] . Together , these studies demonstrated clearly that yeast cells have the capacity of metabolizing neutral storage lipids . However , these lipids are not essential for cellular viability . Budding yeast has been a model for two modes of aging [22 , 23] , chronological lifespan ( CLS ) and replicative lifespan ( RLS ) . CLS refers to the overall viability of stationary-phase cells over time . RLS examines the number of daughters that each mother cell can produce before ceasing division . These two modes simulate , respectively , the senescence of post-mitotic ( e . g . , muscles and neurons ) and stem cells in metazoans . Chronological aging in yeast has been linked closely to the nutrient status . When glucose is depleted , yeast cells exit from the log phase to enter diauxic shift , then into the stationary phase , a mitotically inactive yet metabolically active state [24] . The population viability is maintained in cells that enter the quiescent state [25 , 26] . However , over time , the number of viable quiescent cells diminishes as well , resulting in a progressive increase of population mortality , a condition similar to metazoans including humans [22] . While there are yeast-specific chronological senescence and longevity factors ( e . g . , acetic acid and glycerol , respectively ) [27 , 28] , a number of pathways are conserved [22 , 23] . Some of the most notable pro-aging pathways include the Target Of Rapamycin ( TOR ) /S6 kinase ( Sch9p in yeast ) [29] and the Ras/adenylate cyclase/PKA pathways [30] . Intriguingly , these pathways control both CLS and RLS [23] . These two pathways are activated in response to the intake of selective nutrients , and are suppressed by caloric restriction , consistent with many reports that different organisms extend their lifespan when subjected to calorie restriction [31 , 32] . Other pro-aging factors include oxidative stresses [33] , mitochondrial dysfunction [34 , 35] , defective autophagy [36] , DNA damages and replication stresses [37] , and metabolic alterations [38] . Yeast genome-wide studies have identified a variety of gene mutations that extend chronological lifespan [39–42] . Many of these genes are involved in the metabolism of amino acids , nucleotides , or alternative carbon source , further underscoring the important roles played by metabolites in the control of lifespan . However , despite these relatively unbiased screens , very little is known regarding the role of lipids in the control of CLS . Here we present evidence for a novel energy usage-independent , anti-senescence function of TAG in yeast .
Budding yeast is an excellent model for elucidating gene functions , biochemical pathways , stress responses , and aging . Natural variations among yeast strains , including traits altered during laboratory domestication may help uncover the relationships between genotype and phenotype [43 , 44] . We speculated that phenotypic differences between laboratory and wild isolates could reveal important biological information , including the relationship between lipid and aging . To this end , we first examined the growth curves , under standard laboratory growth conditions , of eight wild strains isolated from diverse natural environments and three laboratory strains . These wild strains were ho- ( i . e . , unable to switch mating type ) haploid segregants of isolates from vineyards , oak exudates , and clinical samples . The three laboratory strains were yMK839 , a derivative of EG123 [45] , W303 , and BY4742 . Fig 1A shows that , in general , wild strains had a shorter lag phase and faster growth rate in log phase than laboratory strains ( doubling time in YPD: lab , 94 ± 2 min; wild , 80 ± 3 min ) . In addition , the cell density at saturation , i . e . , stationary phase , was also higher for the wild strains . The ability to accumulate higher cell density in spent medium indicated that these wild strains may have adapted more effectively to nutrient limitations in a harsh environment , and , if true , further suggested that such cells might survive better through stationary phase than their domesticated counterparts . To test this hypothesis , we measured the percent of cells able to re-enter vegetative growth from revival of the saturated cultures over a period of one month , using the quantitative “outgrowth” approach developed by Kaeberlein and colleagues [46] . The survival plot in Fig 1B demonstrates higher viability of wild strains . In a separate “spot assay” , cells cultivated for 61 days were serially diluted and spotted to a fresh solid medium . Two of the three laboratory strains fell below the detection limit of this assay , whereas the majority of the wild strains were capable of forming new colonies , indicating higher survival rates ( Fig 1C ) . Fig 1A–1C suggest that the laboratory domestication of S . cerevisiae might have artificially selected for certain physiology features leading to distinct phenotypes in the stationary phase [47] . Microscopic inspection of 5-day old stationary phase cells revealed more abundant cytoplasmic granules stainable by the neutral lipid dye Nile red in wild strains ( S1 Fig ) . These Nile red stained lipid droplets are organelles that store neutral lipids , i . e . , triacylglycerol and steryl esters [14] . Whereas the SE level stays constant through the growth curve [11] , TAG abundance rises sharply when cells enter the stationary phase [6] . TAG thus seemed to be a plausible causal link to the observed difference in survival following saturation . To test this hypothesis , we first quantified cellular TAG abundance of day-1 and day-8 post-saturation cultures . Consistent with the microscopic observations , the wild strains contained higher levels of TAG at both time points ( Fig 1D ) , providing a positive correlation between TAG abundance and survival . The differential age-dependent survival shown above is equivalent to yeast chronological lifespan [48 , 49] , suggesting that TAG may have a role in maintaining or even extending chronological lifespan . To understand the causal relationship between TAG metabolism and chronological lifespan , we turned to a laboratory strain ( yMK839 ) for genetic manipulation and phenotypic assessment . In yeast , Dga1p and Lro1p are the major TAG biosynthetic enzymes ( Fig 2A ) . The aliphatic chain of each constituent fatty acid contains chemical energy that can be converted to metabolically useful energy by a series of -oxidation reactions taking place in peroxisomes , following lipolysis by lipases Tgl3p and Tgl4p [18 , 50 , 51] . To test whether the increased TAG storage prolongs chronological lifespan , we deleted TGL3 and TGL4 , which caused TAG accumulation while blocking energy extraction from this lipid species . Consistent with published results [18 , 51] , the TAG level was elevated in tgl3Δ , tgl4Δ , and tgl3Δ tgl4Δ strains at stationary phase ( S2 Fig ) . Importantly , all three TAG-rich strains showed extension of chronological lifespan ( Fig 2B , and S5 Fig rows 2 to 4 ) . These results indicate that the storage of TAG , but not its hydrolysis as a prerequisite for energy conversion or other cellular uses , is associated with improved viability in stationary phase . Intriguingly , deleting the TAG lipase did not cause discernible defects in doubling time ( Fig 2E ) , mating efficiency of haploid cells , or sporulation of homozygous tgl3Δ/tgl3Δ cells ( S3 Fig ) . Because deleting either or both TAG lipase genes resulted in similar phenotypes with respect to TAG accumulation and lifespan extension ( Fig 2B ) , the tgl3Δ strain is representatively shown in further experiments below . Blocking TAG hydrolysis also diminishes downstream reactions , including peroxisomal -oxidation that produces H2O2 that may cause oxidative stresses and cell death [52] . If a reduction of lipolysis-associated H2O2 production was solely responsible for the observed viability retention , deleting the two major TAG biosynthetic acyltransferases , Dga1p and Lro1p , would prevent TAG synthesis and the subsequent -oxidation ( Fig 2A ) , and a similar beneficial effect on longevity would result as well . However , phenotypic analysis of the dga1Δ lro1Δ strain revealed the opposite . Deleting the two TAG biosynthetic acyltransferases caused a reduction of chronological lifespan ( Fig 2C , and S5 Fig , row 6 ) and nearly eliminated cellular TAG ( Fig 2D ) . The log phase growth rate also was reduced by the double deletion by approximately 12% ( Fig 2E ) , which likely resulted from a defect in maintaining the cell’s replicative potential ( see Fig 6 and below ) . This shortened chronological lifespan could not be rescued by deleting TGL3 ( S5 Fig , row 5 ) , underscoring the necessity of keeping the physical presence of TAG to maintain cellular viability during stationary phase . To further confirm the pro-longevity role of TAG , we overexpressed a TAG biosynthetic enzyme Dga1p [9] by introducing a multi-copy plasmid bearing DGA1 under the control of the native DGA1 promoter or a constitutive ADH1 promoter to TGL3+ and tgl3Δ strains . Cells from day-8 post-saturation cultures were processed for lipid extraction and TAG quantification . Data in Fig 3A confirmed the increased TAG content by Dga1p overproduction . The wildtype cells with a higher level of TAG exhibited longer lifespan ( Fig 3B ) , strongly suggesting that TAG plays a causal role in preserving cellular viability during chronological senescence . Intriguingly , while Dga1p overexpression also raised the TAG content in tgl3Δ cells , the lifespan extension was relatively minor in this already long-living background . This observation indicates a limit of lifespan extension by TAG . Taking together the data in Figs 1 to 3 , we conclude that intracellular triacylglycerol is essential for the maintenance of chronological lifespan , and that forcing the accumulation of TAG by either blocking its hydrolysis or increasing its biosynthesis , can extend lifespan . The yeast chronological lifespan is regulated by common as well as yeast-specific factors . Rapamycin and paraquat extends and shortens lifespan , respectively [40 , 53] . Caloric restriction , e . g . , reducing the initial glucose concentration from 2% to 0 . 5% or lower in the medium , promotes longevity , whereas excessive glucose ( e . g . , 10% ) shortens it . More specific to yeast is medium acidification from fermentation that causes senescence , an aging mechanism that can be antagonized by using a buffered , neutral pH medium [27 , 54 , 55] . We examined the relationship between TAG and these lifespan regulators , and found that the lifespan-extending regimes of caloric restriction ( 0 . 05% glucose ) , medium neutralization ( pH6 with citrate phosphate ) , and high osmolarity ( 8% sorbitol ) all delayed senescence for both the yMK839 wildtype and its TAG-depleted derivative ( Fig 4A and 4B ) , with the latter still exhibiting shorter lifespan . The exceptionally long lifespan of tgl3Δ cells prohibited us from quantitatively assessing the effect of these lifespan extending treatments . 10% glucose caused all three strains to die early , yet the lipase-null cells remained to be the longest-living strain , suggesting strongly that CLS regulation by the abundance of TAG operates in a novel pathway . The observation that medium neutralization effectively extended the lifespan of dga1Δ lro1Δ and wildtype cells ( Fig 4A fourth column from left ) could be interpreted as that differences in medium acidification underlay the observed differential lifespan . However , direct measurement of the medium pH of the three normal , lean , and fat strains for more than 10 days ( Fig 5A ) , or of 4-day old cultures of the three lab strains and 8 wild strains ( Fig 5B ) revealed statistically indistinguishable degrees of medium acidification . These data therefore ruled out that changes in the TAG level would alter the acidity of the medium and , consequently , the lifespan of cells . Together , Figs 4 and 5 demonstrate that the chronological lifespan can be controlled by the abundance of intracellular TAG in a mechanism that is independent of pathways involving glucose , medium pH and osmolarity . Rapamycin and paraquat are two potent extragenic lifespan modulators for many species [31 , 40] . When treated with these two compounds , all three core strains responded similarly . That is , rapamycin extended , whereas paraquat shortened the lifespan of all three ( Fig 6 ) . When several highly conserved lifespan control genes TOR1 , RAS2 , and SOD2 were deleted from the three core strains , we observed differential responses ( Fig 7A ) . From the time for each strain to drop to 10% , 1% , and 0 . 1% viability ( Fig 7B ) , it is clear that deleting TOR1 made the wildtype strain live longer , in agreement with previous findings [28] . Intriguingly , despite that rapamycin ( 10 nM ) treatment prolonged cellular survival ( Fig 6A ) , deleting the entire Target of Rapamycin TOR1 gene actually shortened the lifespan of both the fat , tgl3Δ cells and the lean , dga1Δ lro1Δ cells ( Fig 7 ) . Because these lipase- and DGAT-deficient strains were unable to extract energy from TAG metabolism , we suspect that tor1Δ cells survived at least partly on the energy stored in TAG . Lacking either TGL3 or DGA1 and LRO1 resulted in the loss of viability during chronological aging . In contrast to the differential effects TOR1 deletion , knocking out RAS2 or SOD2 shortened lifespan of all three parental strains ( crosses and open squares , Fig 7A ) . RAS2 in the RAS/cAMP/PKA pathway is involved in stress response and lifespan control [56] . Deleting RAS2 has been shown to preserve chronological lifespan [28] . However , a genome-wide screen showed reduced survival of chronologically aging ras2Δ cells [57] . In our hands , all ras2Δ strains died earlier than their corresponding parental strains regardless of the TAG content ( Fig 7A , cross markers , and Fig 7B summary ) , suggesting that Ras2p controls the lifespan in a TAG-independent manner . Similarly , deleting SOD2 , which encodes a mitochondrial manganese superoxide dismutase that is a key to the defense against reactive oxygen species originated from mitochondria , and to the preservation of full lifespan potential [58] , significantly reduced the life expectancy of all three strains . These observations suggest that Sod2p remained to be a critical vitality enzyme in the long-living tgl3Δ cells , and that TAG likely functions independently of Sod2p to protect aged cells . Like CLS , replicative lifespan of budding yeast is another model for cellular senescence , which is determined as the number of daughters produced by a mother cell during its lifespan [48] . One fundamental difference between chronological and replicative lifespan is that the ability to proliferate is measured from cells sampled from saturation versus logarithmic growth , respectively ( Fig 8A ) . Intriguingly , log phase yeast cells , which allocate most fatty acids to phospholipid synthesis to support cell growth and division , store very little TAG [6] . Although TAG is dispensable for cell survival [10] ( Fig 2 ) , the immediate precursor for TAG , diacylglycerol , also supplies building blocks for phospholipids [59] . It is possible that changes in the flux of the albeit small amount of TAG in dividing cells may still impact their replicative lifespan by , for example , influencing the metabolism of other lipids derived from DAG . To assess the influence of TAG on replicative lifespan , yMK839 wildtype , dga1Δ lro1Δ , and tgl3Δ strains were subjected to replicative lifespan comparison by the traditional microscopy approach [23] . Deleting the TAG biosynthetic enzymes further diminished the small TAG pool in log phase cells ( 12-hour post-inoculation , black bars , Fig 8B ) . Importantly , both maximum and median replicative lifespan were decreased in cells depleted of TAG ( Fig 8C ) . This shortened replicative lifespan likely accounted for the increased population doubling time of dga1Δ lro1Δ cells ( Fig 2E ) . On the other hand , deleting TGL3 had a minimal effect on the TAG level in early log phase cells ( 12-hour post-inoculation ) , and the lifespan of tgl3Δ cells also was unchanged ( blue cross marker , Fig 8C ) . Together , these data demonstrate that maintaining a certain amount of TAG , or the ability to synthesize TAG , is required to reach full replicative potential . TAG hydrolysis apparently is not essential for replicative lifespan maintenance .
Here we present evidence for a novel pro-longevity function of intracellular TAG in yeast . Deleting TAG lipases or overproducing a DGAT increased TAG accumulation and extended chronological lifespan . Deleting the two TAG biosynthetic enzymes practically eliminated TAG and significantly shortened the chronological lifespan , as well as the median and maximum replicative lifespan . The fact that chronological lifespan extension is seen in different lipase knockout ( i . e . , tgl3Δ , tgl4Δ , and tgl3Δ tgl4Δ ) and in DGAT overexpression strains argues strongly that the accumulation of TAG was the contributing factor for lifespan extension . This conclusion is consistent with the observation that deleting TGL3 cannot rescue the early death phenotype of dga1Δ lro1Δ lean cells ( rows 5 and 6 , S5 Fig ) . Unlike other lifespan extension regimes such as SCH9 knockout and rapamycin treatment that also retard mitotic growth [60 , 61] , we have yet to detect obvious growth defects in the three lipase deletion strains . For example , aside from normal , or even faster growth rates ( Fig 2E ) , mating and sporulation efficiency of these fat cells was essentially identical to their wildtype mother strain ( S3A and S3B Fig ) . There were no significant differences in cellular sensitivity to heat ( 55°C ) , 260 nm UV , high concentrations of NaCl , or to H2O2 ( S4 Fig ) . It is perceivable that a negative phenotype would be linked to TAG lipase null cells if they were grown without any fatty acid supplement , and with concomitant presence of a fatty acid synthase inhibitor such as cerulenin [62] . These cells would suffer from the lack of energy and fatty acid building blocks for growth and division . TAG hydrolysis is thus by and large dispensable as long as fatty acids are available from the environment or can be synthesized by de novo activities . It should be noted that Daum and colleagues reported that tgl3- homozygous knockout cells were unable to form spores [18] . Possible causes for this discrepancy may include differences in the genetic background of the strains , and the protocols for sporulation . Chronological lifespan of S . cerevisiae is regulated by both yeast-specific and conserved factors and drugs . Experimental results shown in Figs 4 to 7 strongly suggest that TAG preserves viability during chronological senescence in a manner that is independent of those factors tested herein , including caloric restriction , high osmolarity , medium pH , rapamycin and paraquat responses , and conserved pathways involving TOR1 , RAS2 , and SOD2 genes . Results from genetic interaction tests also suggest a novel TAG pathway in CLS control . Both fat and lean strains responded similarly to the deletion of TOR1 , RAS2 , or SOD2 ( Fig 7 ) , indicating that changes in TAG metabolism does not affect the function of these conserved CLS regulators . Intriguingly , deleting TOR1 shortens the lifespan of both lipase-null and DGAT-deficient cells ( Fig 7 ) but , as expected , protects those cells possessing the normal TAG metabolic capacity . We suggest that tor1Δ cells that survive on the caloric restriction pathway through chronological aging [23 , 28] need to tap into the energy depot of TAG . Without TAG biosynthetic enzymes or TAG hydrolytic lipases perturbs this energy flux , thus resulting in early death of tor1Δ cells . In addition to TOR1 and RAS2 , we have also combined tgl3Δ and sch9Δ mutations . Deleting SCH9 , the ribosome S6 kinase homologue , has been shown to activate the Rim15-Msn2/4 and superoxide dismutase ( SOD ) stress pathways and prolongs lifespan significantly [29 , 63] . Our tests of the genetic interaction between TGL3 and SCH9 were inconclusive . Independent tgl3Δ sch9Δ isolates showed mixed results , ranging from longer CLS to synthetic sickness ( S6 Fig ) . The reason for the stochastic phenotypes is unclear . Taking into account the observations presented above as well as from previous reports , we hypothesize that TAG has a role in stress response that underlies the observed phenotypes in chronological aging . Firstly , TAG accumulates when yeast cells enter stationary phase in which nutrients are becoming progressively limited [6 , 47] . Starvation and stress-induced TAG accumulation appears to be a widespread response in different organisms , including photosynthetic algae [2–4 , 64] and animals as well [65–67] . Dietary restriction has been suggested to prolong lifespan by eliciting cellular stress response [31] . Intriguingly , laboratory mice [5] and developing Caenorhabditis elegans [68] have an increased body fat mass when subjected to dietary restriction . Secondly , wild yeast strains in general exhibit higher TAG content and longer chronological lifespan ( Fig 1 ) . Food shortage is a common environmental crisis in the wild , but rarely a relevant factor for lab strains . A systematic phenotypic and transcriptomic survey of wild and laboratory strains showed that the latter are less tolerant of many environmental stresses [44] . Certain traits , including stress responses and high levels of TAG , might have lost during the domestication of S . cerevisiae in laboratory environments , in which the selection pressure for long-living , stress-tolerant stationary phase cells is low . It seems plausible that besides preserving energy to cope with uncertainties in food supply , the increased fat content in stressed cells may confer an additional , energy-independent function that helps sustain longevity . The presumptive stress antagonized by TAG in post-mitotic cells remains to be identified . One candidate is fatty acid-solicited lipotoxicity [69] . Sequestering fatty acids in the form of TAG may prevent lipotoxicity that erodes replicative potential and chronological viability . Disabling TAG biosynthesis results in surplus fatty acids , which may arise from de novo synthesis , uptake from environment , or from lipolysis , that may disrupt membrane lipid homeostasis [9] . Indeed , in an extreme situation where the ability to incorporate fatty acids to TAG and SE is altogether eliminated , yeast cells become hypersensitive to fatty acids and die with membrane hyper-proliferation [12] . Similarly , the fission yeast Schizosaccharomyces pombe dga1+ and plh1+ double knockout cells ( equivalent to the dga1Δ lro1Δ strain of S . cerevisiae ) also die upon entering stationary phase , and are hypersensitive to exogenous fatty acids during vegetative growth [70] . While this lipotoxicity model explains the early death phenotype of dga1Δ lro1Δ cells , total lipid analysis of our long-living fat cells failed to detect significant changes in free fatty acids or DAG ( see , for example , Fig 3A ) . While we cannot rule out the possibility that a small but critical change in certain lipid species contributes more critically to lifespan extension , other hypotheses are worth considering . One frequently cited cause of aging is mitochondrial dysfunction that also involves oxidative damages [71] . While subcellular compartmentalization confines TAG synthesis and storage to ER and lipid droplets , respectively , a number of reports have demonstrated physical association of mitochondria with ER and LD [72] , lending support for functional crosstalk between neutral lipid metabolism and mitochondria biogenesis [73] . Moreover , mitochondria also possess a type II fatty acid synthesis pathway [74] . Deleting enzymes within this pathway causes mouse embryonic lethality and yeast respiratory defects [75 , 76] . Fatty acids trafficking between mitochondria and LD may help achieve mitochondrial lipid homeostasis . Importantly , mitochondria are a major source for reactive oxygen species . Free radicals that would otherwise escape from mitochondria and cause pleiotropic cellular damages might enter LD and attack the fatty acyl chains of the storage TAG molecules [77] . It is possible that the high density of peroxidated fatty acids in LD facilitates crosslinking of neighboring radicalized molecules , hence terminating the vicious propagation of radicals . This “radicals sink” model appears to be consistent with the experimental findings presented above . TAG metabolism and many aspects of cellular aging are conserved . It is thus possible that the cytoprotective role of TAG also exists in higher organisms . For example , Bailey et al . recently reported an anti-oxidant role of lipid droplets in the stem cell niche of Drosophila during neurodevelopment by limiting the levels of reactive oxygen species and inhibiting the oxidation of polyunsaturated fatty acids [78] . In transgenic mice , overexpression of DGAT1 in the skeletal muscle and heart increased intracellular TAG abundance as well as insulin sensitivity of the underlying animals [79 , 80] . Similarly , deleting adipose triglyceride lipase ( ATGL ) protected animals from high-fat diet-induced insulin resistance [81] . However , excessive TAG in heart muscle resulting from ATGL knockout also was associated with cardiac dysfunction [73 , 82] . These transgenic animal studies underscore the complexity of mammalian metabolism and the interdigitating relationships between triglycerides ( dietary , circulating , and in different tissues ) and other nutrients . While the positive influence of intracellular TAG on chronological lifespan in yeast is reminiscent of the so-called obesity paradox in humans , that is , the overweight population has the lowest mortality under a number of medical conditions [83 , 84] , we caution that the comparatively simple yeast may not be immediately applicable to the complex human system . An integrative strategy combining metabolomics , lipidomics , and transcriptomics of representative yeast strains will help elucidate the molecular basis of this novel function of TAG , which might provide a toolbox for a better understanding of the benefits of intracellular TAG in humans .
Yeast strains used in this study are shown in Table 1 . YPD medium contained 2% glucose ( Sigma-Aldrich ) ( unless otherwise stated in the text ) , 2% peptone ( BD Difco ) , and 1% yeast extract ( BD Difco ) . SC medium ( synthetic complete ) contained 2% glucose , 5 g/l ammonium sulfate ( Sigma-Aldrich ) , 1 . 7 g/l yeast nitrogen base without amino acids or ammonium sulfate ( BD Difco ) , and complete amino acids as described in [85] . Auxotrophic nutrients were supplied at four-fold excess as recommended [23] . Citrate phosphate buffering was done as described [27] . Rapamycin ( Sigma-Aldrich ) and paraquat dichloride ( Fluka ) were added from 10 μM and 250 mM stocks to SC medium to make final concentrations of 10 nM and 10 mM , respectively . Yeast transformation was performed using the lithium acetate method [86] . Deletion of TGL3 and TGL4 was described in [2] . To delete DGA1 , PCR reactions using primers ATGTCAGGAACATTCAATGATATAAGAAGAAGGAAGAAGGAAGATCCCCGGGTTAATTAA and TTACCCAACTATCTTCAATTCTGCATCCGGTACCCCATATTTATTCGAGCTCGTTTAAAC and template pFA6a-KanMX6 [87] were conducted . To delete LRO1 , primers TATCCATATGACGTTCCAGATTACGCTGCTCAGTGCGGCCGCATGTCAGGAACATTCAAT and GAATTTCGACGGTATCGGGGGGATCCACTAGTTCTAGCTAGATTACCCAACTATCTTCAA were used with pBS1539 as the template to amplify K . lactis URA3 gene as the selective marker [88] . To delete TOR1 , primers GAACCGCATGAGGAGCAGATTTGGAAGAGTAAACTTTTGAAATGAAGCTTGATATCGAAT and CCAGAATGGGCACCATCCAATATAATGTTGACATAACCTTTCTACGACTCACTATAGGGC were used with pBS1539 to amplify K . lactis URA3 . For RAS2 deletion , primers CCTTTGAACAAGTCGAACATAAGAGAGTACAAGCTAGTCGTCTGAAGCTTGATATCGAAT and ACTTATAATACAACAGCCACCCGATCCGCTCTTGGAGGCTTCTACGACTCACTATAGGGC were used to amplify K . lactis URA3 on pBS1539 . For SOD2 deletion , primers TTCGCGAAAACAGCAGCTGCTAATTTAACCAAGAAGGGTGGTTGAAGCTTGATATCGAAT and GATCTTGCCAGCATCGAATCTTCTGGATGCTTCTTTCCAGTTTACGACTCACTATAGGGC were used to amplify K . lactis URA3 on pBS1539 . The PCR products were gel-purified for yeast transformation . Genomic PCR was used to verify the correct insertion . To overproduce Dga1p , a yeast genomic PCR product was co-transformed with Not I-linearlized pMK595 [89] for ADH1-controlled expression ( pMK595-DGA1 ) . A second multicopy DGA1 overexpression plasmid with DGA1 under its own promoter control ( pMK595 PDGA1-DGA1 ) was constructed similarly , except that the DGA1 promoter ( 961 bp ) was included in the transforming PCR DNA , using primers ATCCATATGACGTTCCAGATTACGCTGCTCAGTGCGGGTAAAGAATCTAAATCGAGCTAC and atcggggggatccactagttctagctagagcggccTAGATAGGTACAATCGACTTAAAGC . The tgl3Δ /tgl3Δ diploid strain yWH74 was generated by first transforming yXL004 with YCp50-HO [90] to induce mating type switch and subsequently spontaneous mating of cells in the same colony . Homozygous diploid cells were identified by their inability to mate as either a MATa and MATalpha strain . Diploid cells were then grown in YPD for two days to allow for the loss of YCp50-HO , resulting in 5-FOA resistant , ura3- cells . For growth curve analyses , yeast cells were seeded at an initial concentration of 0 . 1 OD600 in 150 μl of YPD medium in 96-well plates with biological and technical duplicates . The plates were examined by measuring OD630 every 30 minutes via a BioTex PowerWave XS plate reader until cells reached saturation . The machine was programmed to shake at the high-speed setting and to control temperature at 30°C . Chronological lifespan was measured by the outgrowth method as described in [85] . Briefly , yeast from stab cultures were inoculated to YPD broth and grown at 30°C overnight or until late log phase . Cultures were then diluted into 5 ml SC medium at 0 . 1 OD600 . The seeded cultures , in 15-ml glass tubes with a loose metal cap , were incubated in a rotator drum at 30°C . At selected time points , 5 μl of stationary phase cultures were sampled out and mixed with 145 μl of fresh liquid YPD in a 96-well plate . The plates were sealed with parafilm to prevent evaporation , and incubated in the BioTex PowerWave XS plate reader . Cell density ( OD630 ) was monitored every 30 minute for 48 hours . Growth curves , doubling times , and survival fractions were calculated according to [85] . To compare the quantitative differences in lifespan of different strains , the time by which each culture reached 10% , 1% , and 0 . 1% survival fractions was obtained from three independent survival curves . The cultures which survived beyond 30 days before reaching the specified survival fractions were not included in the statistical calculation and represented as >30 days . For spot assays , stationary phase cultures at selective time points were adjusted to 1 OD600 with sterile water in 96-well plates and ten-fold serially diluted . 5 μl of cells from each well were spotted to a YPD plate and grown at 30°C for two days . The culture viability was quantified by counting visible colonies at the most diluted spot . The number of colonies multiplied by the dilution factor of that spot was regarded as the viability . This method was adapted from the Tadpole assay as described in [91] . Replicative lifespan was performed according to previously described [92] by counting number of progeny produced by 60–90 virgin cells from young mother of each strain . Daughter cells were removed every 90 minutes by a micromanipulator . The lifespan analyses were performed by using R software , version 3 . 0 . 3 with survival and KMsurv packages . Breslow test was used for statistic analysis . Total lipid extraction was conducted essentially as described before [70] with modifications . Briefly , 3 OD600 cells from selected time points were harvested by centrifugation at 5 , 000 rpm for 5 minutes at room temperature , followed by washing once with 1 ml water , and were kept at -80°C if lipid extraction was not done immediately after cell collection . To extract total lipids , cells , if frozen , were removed from the freezer and mixed directly with 300 μl of glass beads ( 425–600 μm , Sigma-Aldrich ) and 1 ml of chloroform: methanol ( 17:1 , v/v ) ( JT Baker ) by vortexing twice for 90 seconds at 4°C . The mixtures were briefly spun and the supernatant was moved to a new glass tube . The remaining cell debris and glass beads were vortexed with an additional 1 ml of chloroform: methanol ( 2:1 , v/v ) . The supernatant was collected as above and pooled with the previous fraction . 1 ml of 0 . 2 M phosphoric acid and 1 M KCl , was added to the pooled organic fraction and vortexed vigorously for 30 seconds , and spun at 3 , 000 rpm , 4°C for 5 minutes to separate the organic and aqueous phases . The chloroform phase at the bottom was collected and dried under nitrogen gas . This lipid extract served as the total lipid fraction . To purify TAG , total lipids were developed by thin-layer chromatography ( TLC ) on a G60 silica plate ( EMD Chemicals ) . The mobile phase was composed of petroleum ether ( 35–60°C , Macron ) : diethyl ether ( JT Baker ) : acetic acid ( JT Baker ) = 80: 20: 1 ( v/v/v ) . Following development , the TLC plates were briefly stained with iodine vapor to reveal the position of TAG . Spots co-migrating with a TAG control ( olive oil , Dante ) were isolated and converted to fatty acyl methyl esters ( FAME ) by reacting with 1 ml of 1 N Methanolic HCl ( Sigma-Aldrich ) at 80°C for 25 minutes [93] . A universal internal control of 5 μg of pentadecanoic acid ( Sigma-Aldrich ) was included in all samples for FAME derivatization and gas chromatography ( GC ) . 1 ml of 0 . 9% NaCl was added to stop the FAME reaction , followed by the addition of 1 ml of hexane , and vortexed for 30 sec to extract FAME . Centrifugation at 3 , 000 rpm for 5 min at 4°C was conducted before the hexane layer was aspirated to another glass tube , and the volume reduced to 30 μl under nitrogen blowing . 2 μl of the FAME in hexane was injected to a gas chromatography system ( Agilent Technology , 7890A ) for quantification . To visualize lipid droplets in yeast cells , approximately 0 . 5 OD600 cells were collected by centrifugation ( 14 , 000 rpm for 1 min in a microfuge ) and washed once by 1 ml TE buffer ( pH 7 . 4 ) . Cell pellets were suspended in 100 μl of TE buffer and stained with 1 mg/ml Nile red in the presence of 3 . 7% formaldehyde for concomitant fixation , and let sit in the dark for 20 min . Cells were collected again by microcentrifugation and re-suspended in 100 μl TE buffer and stored in the dark for no more than two days . For microscopy , an Olympus BX51 station with a Exfo X-cite 120 UV light fixture and a DP30-BW CCD camera were used . A GFP filter was used for lipid droplet fluorescence detection . For semi-quantitative comparison of lipid droplets , a fixed exposure ( typically 2 seconds ) for fluorescence was applied to all samples . Sporulation efficiency was determined by microscopically examining the percent of diploid cells forming asci . Overnight YPD cultures of diploid strains were transferred to PSP2 medium ( Potassium phthalate ( Sigma-Aldrich ) 8 . 3 g/l , yeast extract 1 g/l , 1 . 7 g/l yeast nitrogen base without amino acids or ammonium sulfate , ammonium sulfate 5 g/l , potassium acetate 10 g/l , pH 5 . 4 at 0 . 1 OD600 and grown at 30°C under vigorous shaking ( 200–250 rpm ) . After 24 hours , cells were harvested by centrifugation ( 14 , 000 rpm , 30 sec in a microfuge ) , washed once with sterile water , and re-suspended in 1 ml SPM medium ( potassium acetate 3 g/l , raffinose 0 . 2 g/l ) . Cultures were shaken vigorously at 200–250 rpm at 30°C for 48–72 hours . A small amount of cells were removed from the culture , spun , and suspended in 1 mg/ml DAPI ( 4 , 6-diamidino-2-phenylindole ) in a mounting medium ( 1 mg/ml p-phenylenediamine , 0 . 9% glycerol , 2 . 25 μg/ml DAPI ) . Percent of cells forming asci with four DAPI foci ( i . e . , four spores ) were counted . Mating efficiency was quantified by mixing 0 . 1 OD600 of “tested” strains with 0 . 5 OD600 of tester strains of the opposite mating type ( 227a or 70 ‹ ) , all in early log phase , in 1 ml of YPD . Cell mixtures were let sit at 30°C for 5 hours . The tested and tester stains were also incubated separately as the negative control . Cells were washed once with sterile water and plated on SD medium ( 2% glucose , 1 . 7 g/l nitrogen base with amino acids or ammonium sulfate , 5 g/l ammonium sulfate , 20 g/l agar ) . Mating between the tested and tester strains generated prototroph diploid cells that were able to form colonies on the SD medium plate . The strains B454 , B756 , B779 , B359 , B370 , B357 and B390 were segregants of isolates collected in the wild ( see Table 1 for source and reference ) . In order to generate isogenic haploid strains , the heterozygous wild isolates were sporulated and self diploidized due to homothallism . We randomly selected one of the four homozygous segregants from each tetrad . Next , strains were made heterothallic via removal of the HO gene , using homologous gene replacement with either a Hgh cassette ( pAG32 ) [94] ( strain B454 due to a natural kanamycin resistance ) or a KanMX6 cassette ( pFA6a ) [95] ( strains B756 , B779 , B359 , B370 , B357 and B390 ) . Transformed strains were sporulated and dissected to verify 2:2 segregation of either hygromycin or kanamycin resistance . One MATa and MATalpha haploid transformant was retained from each strain . Proper integration at the HO locus was verified via PCR . Strain B653 was obtained from John McCusker . | Triacylglycerol ( TAG ) is a ubiquitous lipid species well-known for its roles in storing surplus energy , providing insulation , and maintaining cellular lipid homeostasis . Here we present evidence for a novel pro-longevity function of TAG in the budding yeast , a model organism for aging research . Yeast cells that are genetically engineered to store more TAG live significantly longer without suffering obvious growth defects , whereas those lean cells that are depleted of TAG die early . Yeast strains isolated from the wild in general contain more fat and also display longer lifespan . One of the approaches taken here to force the increase of intracellular TAG is to delete lipases responsible for lipid hydrolysis . Energy extraction from TAG thus is unlikely an underlying cause of the observed lifespan extension . Our results are reminiscent of certain animal studies linking higher body fat to longer lifespan . Potential mechanisms for the connection of TAG and yeast lifespan regulation are discussed . | [
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] | 2016 | An Energy-Independent Pro-longevity Function of Triacylglycerol in Yeast |
The eyes absent ( eya ) gene of the fruit fly , Drosophila melanogaster , is a member of an evolutionarily conserved gene regulatory network that controls eye formation in all seeing animals . The loss of eya leads to the complete elimination of the compound eye while forced expression of eya in non-retinal tissues is sufficient to induce ectopic eye formation . Within the developing retina eya is expressed in a dynamic pattern and is involved in tissue specification/determination , cell proliferation , apoptosis , and cell fate choice . In this report we explore the mechanisms by which eya expression is spatially and temporally governed in the developing eye . We demonstrate that multiple cis-regulatory elements function cooperatively to control eya transcription and that spacing between a pair of enhancer elements is important for maintaining correct gene expression . Lastly , we show that the loss of eya expression in sine oculis ( so ) mutants is the result of massive cell death and a progressive homeotic transformation of retinal progenitor cells into head epidermis .
Construction of a properly functioning organ or tissue is dependent upon the activity of hundreds of genes that can be conceptually organized into a gene regulatory network ( GRN ) [1–4] . These genes control the specification/determination , patterning , differentiation , and physiology of all cell types within the developing and adult organ . The development of the retina in the fruit fly , Drosophila melanogaster , is controlled in part by an evolutionarily conserved gene regulatory network called the retinal determination ( RD ) network [5] . The core members are two PAX6 genes , twin of eyeless ( toy ) and eyeless ( ey ) , the SIX gene sine oculis ( so ) , the EYA family member eyes absent ( eya ) , and the SKI/SNO proto-oncogene dachshund ( dac ) [6–11] . In addition to these core members , the fly version of this network contains an additional nine genes of which some are functionally conserved within the vertebrate eye [5] . Mutations in the fly RD genes lead to drastic reductions of the compound eyes while forced expression in non-ocular tissues such as the wings , antennas , and legs leads to the formation of structurally complete ectopic eyes . These observations suggest that these factors occupy the highest positions within the larger eye/lens gene regulatory network . In addition to the eye , the core members are used reiteratively during development to also determine the fate of many non-ocular tissues such as the musculature , skeletal system , nose , ear , pancreas , and kidney [12–14] . Studies of the RD network can therefore provide invaluable insights into the specification and patterning of a wide range of tissues and organs beyond the eye . The RD network has been best studied in Drosophila with a quarter century of investigation having identified a wealth of genetic , biochemical , and molecular interactions amongst the different members . Numerous review articles over the years have summarized these findings in static circuit maps [5 , 12 , 15–17] . While these interaction diagrams have been helpful in understanding the relationship amongst network members , they can be misleading since the network genes are expressed in dynamic patterns that change both spatially and temporally [18] . In addition , individual genes initiate expression at different times in development [6 , 8 , 9 , 11 , 17 , 19] , are co-expressed with other network genes in some cells but not in others [18] , and appear to interact differently depending upon the exact spatial , temporal , and developmental context [20 , 21] . As a result the static maps of regulatory interactions do not necessarily reflect the reality of what is happening throughout the eye in either space or time . In this report , we have focused on understanding how , at the level of cis-regulatory elements , the eya gene is regulated temporally and spatially in the developing retina . We then use this information to evaluate one tenant of the RD circuit map–namely we test the potential regulation of eya by the So transcription factor . The Eya protein functions as a transcriptional co-activator and protein tyrosine phosphatase [22–24] , although the latter activity appears dispensable for eye development in Drosophila [25] . Within the nucleus Eya interacts with members of the SIX/So family of homeodomain containing DNA binding proteins [22] . Together , SIX-EYA complexes function as bipartite transcription factors to activate targets necessary for the specification , differentiation , and growth of the retina [22 , 23 , 26] . Recent reports indicate that these complexes also function as transcriptional repressors although the exact mechanism underlying this activity has yet to be determined [19 , 20 , 27 , 28] . Both genes are expressed in nearly identical spatial patterns within the developing eye [10 , 11] . Expression of both genes is lost in both eya and so mutants [29] . These properties have led to the proposal that the So-Eya complex regulates the expression of both genes . In the wild type eye eya expression is temporally and spatially dynamic [11] . This expression is completely eliminated from the retina of eya2 mutants , which are viable but completely lack the adult compound eyes [11] . These flies harbor a 322bp deletion , which lies 576bp upstream of the transcriptional start site [11] . When multimerized this 322bp fragment drives expression of a transcriptional reporter in a pattern that approximates the wild type gene [30 , 31] . It also contains sufficient activity to partially restore eye development to eya2 mutants when driving expression of a rescuing transgene [30 , 31] . Based on this evidence this enhancer , for many years , was thought to be the sole cis-regulatory element controlling eya expression within the developing eye . Sequence analysis identified the presence of a canonical So binding site within this enhancer thereby raising the possibility that eya expression in the eye is controlled by So [31 , 32] . More recently , several studies of the eya locus have identified two additional retinal enhancers , the presence of additional So binding sites , and multiple genomic positions where So appears to bind in eye-antennal discs [33–35] . Together these data have been used to support the premise that the initiation and maintenance of eya expression is under the control of So . In this paper we report the identification of several cis-regulatory elements within the eya locus that contribute to its expression in the developing eye . Three of these enhancers lie adjacent to each other and we demonstrate that they function cooperatively to regulate the temporal and spatial expression pattern of eya during eye development . We also show that the spacing between two of these enhancers is important for the activity of each cis-regulatory element . And finally , we show that each of the retinal enhancers ( those identified in this and other studies ) remain active in so loss-of-function mutants . This is at odds with the model in which eya is regulated by So . We show that the loss of eya expression in so mutants is actually the result of cell death and a progressive fate transformation of the retina into head epidermis . Our findings do not support a role for So in the initiation of eya expression . However we do not rule out the possibility that So functions to maintain eya transcription in the retina .
In third larval instar retinas eya is expressed in a small stripe of cells ahead of the advancing morphogenetic furrow , in differentiating photoreceptor , cone , and pigment cells , and in the developing ocelli ( Fig 1A and 1B ) [11] . In the eya2 mutant eya expression is completely lost from the eye field ( Fig 1C and 1D ) . We first set out to determine if the So consensus sites and regions of So ChIP peaks that are found outside of the original 322bp enhancer are functional . To do this we attempted to rescue the eya2 mutant by forcibly expressing a So-VP16 chimeric construct in the developing eye with an ey-GAL4 driver . This protein is capable of fully restoring eye development to so1 mutants [27] and activates a luciferase reporter at levels that are 20-fold higher than So alone and 5-fold higher than the So-Eya complex ( Fig 1G ) . Based on these data we reasoned that So-VP16 serves as a strong transcriptional activator and therefore is a suitable substitute for the So-Eya complex ( So-VP16 = So-Eya ) . Expression of So-VP16 partially restores both eya expression and eye development to 62% of the 57 animals that we examined ( Fig 1E and 1F; S1A–S1C Fig ) . Consistent with being a very weak activator , expression of wild type So alone is insufficient to restore either eya expression or eye development to eya2 mutants ( Fig 1G; S1D and S1E Fig ) [27] . These results led us to initially conclude that additional So-responsive enhancer element ( s ) are present within the eya locus . In order to identify regulatory elements that are responsive to the So-Eya complex we used the osm-6 gene and a CTSF insulator site to define the 5`and 3`boundaries respectively of the eya locus and then cloned fragments of DNA between these two genomic markers ahead of a minimal hsp70 promoter and a lacZ reporter ( Fig 2A ) . These constructs were inserted into the same genomic coordinates ( attP-3BVK00033—cytological position 65B2 ) using the PhiC31 integrase system to maintain similar expression levels across reporters . Wandering third instar eye-antennal imaginal discs were then examined for lacZ reporter expression . We identified six genomic fragments that are capable of driving expression of the reporter in portions of the endogenous eya pattern ( Fig 2B–2G ) . Three of these fragments ( PSE , 1 , and E ) have been previously identified as enhancers controlling eya expression in the retina [30 , 31 , 35] . The PSE , which stands for photoreceptor specific enhancer , drives expression solely in cells behind the morphogenetic furrow ( Fig 2A and 2B ) [35] while fragment 1 ( also called IAM for immediately anterior to the morphogenetic furrow ) drives expression ahead of the advancing morphogenetic furrow and in differentiating cells ( Fig 2A and 2C ) [35] . Fragment E ( for extant ) is the enhancer that is deleted in eya2 mutants ( Fig 2A and 2D ) [30 , 31] . Our sequence analysis indicates that the fragment is 319bp in length ( and not 322bp as originally reported ) . Fragments 2 , 3 and 4 are three new retinal enhancers that control eya expression in the developing eye ( Fig 2A and 2E–2G ) . We next determined the temporal and spatial expression patterns of each individual fragment and compared these patterns to endogenous eya expression . Eya protein is present in the wild type eye disc as early as 48hrs AEL ( early 2nd instar , Fig 3A ) and continues to be expressed broadly at 72hrs AEL ( early 3rd instar , Fig 3B ) . By the late third larval instar stage eya expression is restricted to a narrow band of cells ahead of the morphogenetic furrow and to all differentiating photoreceptor and cone cells ( Fig 3C ) . No single individual fragment fully recapitulates the endogenous eya expression pattern . For example , reporter expression driven by fragment 1 is temporally and spatially delayed compared to wild type eya expression meaning that although it is activated in a few eya expressing cells early in development , it is not until late third instar that expression starts to coincide with the spatial pattern of endogenous eya ( Fig 3D–3F , Table 1 ) . In contrast , while reporter expression driven by fragment E coincides with early endogenous eya , its late expression is weak in intensity and appears mottled ( Fig 3G–3I , Table 1 ) . Lastly , the bulk of fragment 2 driven expression within younger discs is in eya negative cells while in later discs reporter expression does coincide with the endogenous eya gene ( Fig 3J–3L , Table 1 ) . Since each of these three fragments ( 1 , E , 2 ) does mimic a specific temporal and/or spatial aspect of eya expression we hypothesized that these enhancers , which lie adjacent to each other , might function cooperatively to control all temporal and spatial aspects of eya expression . To test this model we generated a single 1181bp fragment consisting of fragments 1 , E , and 2 and as predicted this composite enhancer fully recapitulates the temporal and spatial expression pattern of eya within the developing eye ( Fig 3M–3O , Table 1 ) . To rule out position dependent effects we inserted this construct into a second genomic landing site ( attP-9A VK00019—cytological position 68D2 ) and observe that the expression pattern of this insertion is identical to the original insertion and recapitulates endogenous eya expression ( S2 Fig ) . It appears that the temporal expression of the composite enhancer is the sum or addition of the individual elements . And interestingly , recreating the genomic organization of these three cis-regulatory elements eliminates the ectopic expression from the eye-antennal disc ( Fig 3J–3O , Table 1 ) . Since the composite enhancer recapitulates the entire eya expression pattern it is possible that fragments 3 , 4 , and PSE are functionally redundant . Consistent with this model , the expression patterns controlled by these fragments are fully covered by the composite enhancer ( Fig 3P–3U , Table 1 ) . We then set out to test if the composite enhancer is sufficient to fully rescue the no-eye phenotypes of eya2 and eya1 mutants . The original characterization of the eya1 mutant indicated two chromosomal aberrations are associated with this mutation . First , a chromosomal re-arrangement completely inverts the orientation of the eya locus within the left arm of the second chromosome . This is not thought to interfere with normal eya expression . Second , an approximately 1 . 5kb deletion was detected at the 5`end of the gene . The 319bp deletion in eya2 lies within the larger ~1 . 5kb deletion in eya1 . Thus the no-eye phenotype of eya1 and eya2 is thought to result from the disruption of the same regulatory sites [11 , 31] . To precisely determine the breakpoints of the eya1 deletion in relation to the composite enhancer we isolated and re-sequenced the region around the transcriptional start site and determined that the deletion is actually 1826bp in length with the deletion extending 581bp upstream of the eya2 deletion and 344bp downstream of the transcriptional start site . This deletion completely deletes the composite enhancer , the transcriptional start site , and a large portion of the eya RB transcript 5`UTR ( Fig 2A ) . Using qRT-PCR we confirmed that the RB transcript is completely eliminated in eya1 mutants and drastically reduced in eya2 mutants ( Fig 4A ) . The RA transcript is also greatly reduced , but not eliminated , in both mutant alleles suggesting that the composite enhancer regulates both eya promoters ( Figs 2A and 4A ) . To test whether fragments 1 , E , and 2 are sufficient to rescue the two eya mutants , each enhancer element , as well as the full composite enhancer , was cloned upstream of a minimal hsp70 promoter and the eya RB cDNA . Using the PhiC31 integrase system these constructs were inserted into the same genomic location that we used for the original lacZ reporter expression analysis ( attP-3BVK00033—cytological location 65B2 ) . For all rescue experiments at least 100 adult flies were initially assayed qualitatively for the restoration of eye development . For the rescue quantification in Table 1 the number of ommatidia in adult right eyes from 2–3 individual female flies were counted and compared to wild type . The number of ommatidia per rescue is presented as an average of the 2–3 individuals . A wild type eye from a female fly is defined as having between 750 and 800 ommatidia [36] . Both fragments 1 and E are capable of partially restoring eye development in 100% of eya2 and eya1 mutants . Fragment 1 restores eye size to approximately 38% of wild type in eya2 and 25% in eya1 ( Fig 4B and 4I , Table 1 ) . Enhancer E restores eye size to approximately 49% of wild type in eya2 but less than 1% in eya1 ( Fig 4C and 4J , Table 1 ) . Expression from fragment 2 , on its own , fails to rescue either mutant ( Fig 4D and 4K , Table 1 ) . Consistent with our expression analysis , the full composite enhancer fully restores eye development to 100% of both eya mutants ( Fig 4E and 4L , Table 1 ) . And finally , neither fragment 3 nor 4 are capable of rescuing the no-eye phenotype of either mutant ( Fig 4G , 4H , 4N and 4O , Table 1 ) . The majority of fragment 3 driven expression is outside of the endogenous eya expression pattern and would therefore not be predicted to restore eye development to eya mutants ( Fig 3P–3R , Table 1 ) . The inability of fragment 4 to rescue eye development stems from the fact that it is normally expressed only in differentiating cells posterior to the morphogenetic furrow ( Fig 3S–3U , Table 1 ) . Neither the furrow nor differentiated photoreceptor cells are present in either eya1 or eya2 mutants [11] . The lack of any discernable rescue by fragment 2 and its inappropriate expression pattern initially indicated that it may not function as an enhancer . Instead , its proximity to the transcriptional start site of eya RB , suggested that it might serve as a basal core promoter . To test this idea we placed fragment 2 and the composite enhancer into a plasmid that contains a lacZ reporter but lacks a minimal promoter . Under these conditions fragment 2 is still capable of driving lacZ expression in the developing eye but only in developing photoreceptors ( S3A and S3B Fig ) . In contrast , lacZ reporter expression driven by the composite enhancer is identical to the construct that contained the minimal hsp70 promoter fragment ( S3C and S3D Fig ) . These data support the proposal that fragment 2 functions , in part , as a basal promoter . As such we then tested the model that all pertinent regulatory information may reside only in fragments 1 and E . We first examined lacZ reporter expression with a fragment that contained segments 1 and E only and as expected this construct fully recapitulates endogenous eya expression ( S3E and S3F Fig ) . We next attempted to rescue both eya1 and eya2 mutants with this shorter fragment . While we observed rescue in 100% of animals it only restores eye size to 82% of wild type in eya2 and 44% in eya1 mutants ( Fig 4E , 4F , 4L and 4M , Table 1 ) . This is unlike the composite enhancer , which completely restores eye size to the both eya mutants . This suggests that , in addition to functioning as a basal core promoter , fragment 2 does indeed contain regulatory information that is necessary for robust eya expression . We were intrigued by the differences in rescue efficiency of our constructs in eya1 and eya2 mutants . Since the endogenous transcriptional start site for the RB transcript is intact in the eya2 mutant but is deleted in the eya1 mutant we hypothesized that the higher degree of rescue in the eya2 mutant is due to a reactivation of the endogenous eya gene . Using qRT-PCR we measured eya RB transcript levels within eya mutants that have been rescued by expression from enhancer E . This enhancer was chosen since it showed the most dramatic difference in rescue efficiency . As predicted , we observe that expression of the eya RB cDNA initiates a positive feedback loop on the endogenous locus and reactivates eya expression in eya2 but not eya1 ( Fig 4A , 4C and 4J ) . In the eya2 mutant , fragments 1 ( IAM ) , 2 , 3 , 4 , and PSE are present and one or more of these could be targets of the auto-regulatory loop . To test this possibility we brought combinations of rescue constructs together within a single eya1 animal and asked if the degree of rescue could mimic that of the extant enhancer rescue of eya2 . We combined the extant enhancer ( E , Fig 5A ) with each of the other enhancer elements ( Fig 5B–5F ) and observed a synergistic increase in the quality of rescue only with enhancer 1 ( Fig 5G ) . The quality of eye restoration did not improve by combining the other elements with the extant enhancer ( Fig 5H–5K ) . These data , when combined with the reporter expression and rescue results , suggest that enhancer 1 mediates the Eya-dependent auto-regulatory loop . The ability of enhancer 1 to partially restore eye development to eya1 and eya2 mutants was of particular interest to us since eye development is completely blocked in the eya2 mutant despite the continued presence of enhancer 1 . We hypothesized that the loss of eye development is due to the combined loss of enhancer E and a disruption of enhancer 1 activity . To test this model we recapitulated the genomic organization of the eya2 mutant by fusing enhancers 1 and 2 together . When the enhancers are placed in this configuration expression of the lacZ reporter is lost throughout young eye discs and ahead of the furrow in third instar discs . Expression of the reporter only remains in some differentiating cells posterior to the furrow ( Fig 6A–6C , Table 1 ) . Consistent with the loss of expression in undifferentiated cells , this construct drives reporter expression in a very small number of cells in the eya2 mutant ( Fig 6D ) . The loss of expression in undifferentiated cells prevents this construct from rescuing the eya2 mutant ( Fig 6E , Table 1 ) . The inability of this construct to properly drive lacZ and eya cDNA expression could be due to either the unintended creation of a synthetic binding site for a transcriptional repressor at the junction where enhancers 1 and 2 meet or there might be a need for some amount of genomic space between the two enhancers . To test the first possibility we placed a BamHI restriction site between enhancers #1 and #2 . Addition of this 5bp spacer restores expression to some cells in wild type discs and to a few cells in eya2 mutant discs ( Fig 6F–6I , Table 1 ) . The expression pattern in wild type discs resembles that of enhancer 2 suggesting that insertion of the 5bps failed to allow for the early activation of enhancer 1 . Consistent with this construct behaving similar to enhancer 2 we did not see any rescue of the eya2 mutant ( Fig 6J , Table 1 ) . Since this construct failed to restore eya expression and eye development , we can rule out the possibility that a synthetically created repressor site is the underlying reason for the loss of eya expression in eya2 mutants . To test the latter hypothesis that a certain amount of genomic space is required between enhancers 1 and 2 we inserted a 319bp fragment of DNA ( the size of enhancer E ) between the two fragments in an effort to reinstate normal spacing . On its own this neutral sequence , which comes from intron 1 of the eya locus , does not direct expression of lacZ or rescue the eya2 mutant ( Figs 2A and 6Q–6S , Table 1 ) . At 48hrs and 72hrs AEL the majority , but not all , of the reporter expression of 1+spacer+2 was still seen in non-eya expressing cells ( Fig 6K and 6L , Table 1 ) . However , by the late third larval instar reporter expression is now seen in the majority of Eya positive cells ( Fig 6M , Table 1 ) . Overall early reporter expression of 1+spacer+2 is similar to that of enhancer 2 alone while late reporter expression is comparable to enhancer 1 alone ( compare to Fig 3D–3F and 3J–3L , Table 1 ) . This construct can drive expression in and partially rescue both eya1 and eya2 mutants demonstrating that the reconstitution of spacing was sufficient to restore limited function to enhancers 1 and 2 ( Fig 6O and 6P , Table 1 ) . The restoration of eye size in eya2 and eya1 is approximately 64% and 58% of wild type respectively , compared to 100% for the composite ( 1+E+2 ) enhancer , suggesting that in addition to providing critical space between enhancers #1 and #2 , enhancer E must also contain regulatory information necessary for robust eya expression ( Table 1 ) . Since we are able to partially restore eya expression and eye development to eya2 mutants through expression of the So-VP16 chimeric protein ( Fig 1F and 1G ) we reasoned that one or more of the newly discovered enhancers might be regulated by So . Three enhancers contain canonical So binding sites and So ChIP peaks are present within two other enhancers ( Fig 2A ) [33 , 34] . We first tested whether the So-VP16 chimeric protein is capable of activating the composite ( 1+E+2 ) enhancer . When forcibly expressed in the antennal disc under the control of the dpp-GAL4 driver , So-VP16 is surprisingly unable to activate the composite enhancer ( S4A–S4D Fig , rose arrow ) . In contrast , forced Ey does activate the reporter suggesting that Ey , but not So , regulates the eya locus during eye development ( S4E–S4H Fig , yellow arrow ) . To further test whether activation of any of the enhancers is So dependent we placed each of the lacZ reporter constructs into the so1 mutant background and assayed for lacZ expression . so1 mutants are viable , lack compound eyes , have a small eye disc , and have drastically reduced levels of so expression ( S5 Fig ) [10 , 37] . Any So dependent element should remain silent in this mutant background . However , all of the elements with the exception of enhancer 4 remain activated in so1 mutant eye discs ( Fig 7A–7F ) . Enhancer 4 drives expression exclusively in differentiating cells thus the lack of activation from this enhancer is most likely due to the fact that so1 mutants lack photoreceptor , cone , and pigment cells . It is striking that the composite enhancer remains strongly activated in so1 mutants ( Fig 7D ) . To ensure that this is not due to residual So protein we examined lacZ expression driven by the composite enhancer in so3 null mutant clones . We again find that the composite enhancer is strongly activated in clones both ahead and behind the morphogenetic furrow ( Fig 7G–7J ) . Thus , despite the presence of a So binding site and the apparent binding of So , the composite enhancer ( which contains all regulatory information for proper eya expression ) is not activated by So . When we examined the potential activation of the composite enhancer in so3 null clones , we were quite surprised to see clones that contain Eya protein ( Fig 7H ) . This clearly suggests that activation of reporters in so mutants is not due the persistence of lacZ protein although we cannot entirely rule that out . However , this result certainly was inconsistent with what we observed in late third instar whole mutant so1 discs where Eya protein was completely missing in the eye portion of the disc . These data were also inconsistent with qRT-PCR data , which showed a dramatic reduction of eya transcript levels in so1 mutants ( S4 Fig ) . A possible explanation for these apparently contradictory observations could be that Eya protein expression is lost over the course of larval eye development . This could be the result of a requirement for So in the maintenance of eya expression , retinal progenitor cell death , a fate transformation , or a combination of all three [28 , 38] . Retinal progenitors have previously been defined as those proliferating in the most anterior regions of the eye disc and express Ey but lack So and Eya . Retinal precursors are defined as cells anterior to the morphogenetic furrow which express all three genes [39] . Support for the model that eya expression is lost over developmental time comes from three previously published observations: ( 1 ) eya expression is lost within the retinal field in roughly 50% of mid-late second larval instar so1 eye-antennal discs [29]; ( 2 ) so1 mutants undergo a significant wave of cell death that eliminates retinal progenitors in the growing eye field [10]; and ( 3 ) retinal progenitors within so and eya mutants undergo a fate transformation into head epidermis [28 , 38] . To test our hypothesis that loss of Eya in so1 mutants is progressive we first re-examined eya expression in so1 mutants over the course of larval eye development . Beginning at 72hrs AEL we found that 100% of so1 mutant discs had strong Eya expression throughout the eye disc thereby demonstrating that So is not required for the initiation of eya expression ( Fig 8A; S6 Fig ) . By 96hrs AEL eya expression weakens , is expressed in fewer discs , and is found in smaller and smaller populations of cells over time ( Fig 8B–8D; S6 Fig ) . By 168hrs AEL the overwhelming majority of so1 discs have completely lost eya expression within the retinal field ( Fig 8E; S6 Fig ) . This analysis confirms that Eya protein is indeed lost over the course of larval eye development . To determine whether the loss of Eya protein could be due to increased cell death in retinal progenitors , as suggested by previous studies , we conducted a temporal examination of cell death in so1 mutants and find that retinal progenitors undergo significant cell death over the course of larval eye development ( Fig 8F–8J ) . Using an antibody against Dcp-1 , a marker of cell death , we observed increased cell death at 72hrs AEL in a large swathe of cells in the anterior most portions of the eye disc . At this point cell death seems restricted mostly to retinal progenitors outside the endogenous Eya expression domain as indicated by the expression of Ey but not Eya ( Fig 8F and 8K ) . By 96hrs AEL the wave of cell death becomes broader and extends to the posterior margins of the disc to include both retinal progenitors and retinal precursors as indicated by the presence of both Ey and Eya expression ( Fig 8G and 8L ) . It is important to note that 96hrs AEL is the first time point in which we see decreases in the expression of Eya protein . Finally , as development proceeds , the amount of cell death decreases and becomes restricted to the ventral most portions of the disc ( Fig 8H–8J and 8M–8O ) . Although some Eya positive cells do appear to remain outside the population of dying cells it is clear that the majority of Eya expressing cells have been removed by these later time points consistent with the idea that retinal progenitors , and by default retinal precursors , have been cleared by cell death . These data also corroborate the qRT-PCR data from late so1 mutant discs . If the loss of Eya expression in so1 mutants was solely the result of cell death of retinal progenitors then it follows that blocking cell death should restore eya expression to a subset of cells in late so1 mutant discs as those cells would be saved earlier in larval eye development and then proceed to differentiate into retinal precursors and express eya . To test this hypothesis we blocked cell death by expressing P35 , a well-known inhibitor of caspase dependent cell death , with an eya composite enhancer GAL4 driver . We saw no increase in eya expression at 120hrs AEL suggesting that the loss of eya in so1 mutants is not simply the result of a clearing of retinal progenitors ( Fig 8Q ) . However , we do see a significant number of cells still expressing ey indicating the continued presence of retinal progenitors that are not proceeding to differentiate into retinal precursors ( Fig 8P ) . We have previously shown that retinal progenitors within so and eya mutants undergo a cell fate transformation into head epidermis [28 , 38] . It is possible that after the wave of cell death the continued loss of eya expression in so1 mutants may be the indirect result of this homeotic transformation . The non-ocular bristle and antennal selector gene cut ( ct ) and the head capsule selector gene orthodenticle ( otd ) have previously been shown to be de-repressed in eye to head epidermis transformations in eya2 mutants [28] . We therefore examined expression of both genes in so1 mutants . Concomitant with the decrease in eya expression , we saw a de-repression and expansion of both ct and otd throughout the entire eye disc ( Fig 9A–9L ) . The de-repression of both genes initiates at 72hrs AEL in just a few cells of the eye disc but is more pronounced by 96hrs AEL ( Fig 9D , 9E , 9G , 9H , 9J and 9K ) . Most striking is that starting at 120hrs AEL , when we first begin to see discs without any eya expression in the retinal field , ct and otd expression have expanded to cover the entire eye field ( Fig 9F , 9I and 9L ) . Based on the continued presence of ey expression within the same portion of the eye disc it appears that the surviving retinal progenitor cells have undergone a transformation to head epidermis ( Fig 8H ) . Furthermore , when we block cell death in the mutants , ct and otd are still expressed in the majority of cells within the disc again supporting the idea that these cells have undergone a cell fate transformation ( Fig 9M ) . We believe it is this ongoing cell fate transformation that is blocking the continued expression of eya resulting in a loss of Eya protein in late stage so1 mutant discs . However , additional studies are needed to fully determine if , and possibly to what extent , So might be required for the maintenance of eya expression later in larval eye development . Finally , to ensure that the Eya protein we observed in so1 mutants is not the result of residual levels of So protein activity we examined eya expression in so3 null mutant clones . Consistent with the analysis of so1 discs , we found multiple so3 null clones in which Eya protein was still present ( Fig 10A–10H , yellow arrows ) . We did , however , observe that the majority of large clones spanning the middle of the eye field contained no Eya protein ( Fig 10A–10H , green arrows ) . The adult retinas of these animals often contain large patches of head epidermis protruding through the middle of the eye field ( Fig 10I–10L , green arrows ) . We predict that these patches of head cuticle correspond to the clones in the disc that lack eya expression and thus are the result of a cell fate transformation . Together our lacZ reporter and cDNA rescue analyses suggest that a single 1181bp genomic fragment composed of three cis-regulatory elements is capable of controlling all eya expression in the developing retina . Furthermore , our combinatorial rescue analysis in eya1 mutants suggests that although enhancer elements 1 and E sit adjacent to each other within the eya locus , these two elements are functioning as independent cis-regulatory elements . Additionally , we find that spacing between elements within the composite enhancer is critical for proper function . When fragments 1 and 2 are located adjacent to each other as is the case in eya2 mutant animals these enhancers can no longer function to provide eya expression early in larval development leading to an adult no-eye phenotype . Finally , we find that loss of eya expression in so loss-of-function mutants is progressive and likely the result of increased cell death and a cell fate transformation . Although our data cannot rule out the possibility that So is required for the maintenance of eya expression during larval eye development it is clearly not required for its initiation . Given our identification of multiple independently functioning cis-regulatory elements within the eya locus and the potential differential requirement for So in its activation at later stages of eye development , eya regulation over the course of eye development is likely to be dynamic and require the input of different combinations of RD members and signaling pathways at different times and in different cell types for overall proper temporal and spatial expression .
Members of the retinal determination network play crucial roles in specification , pattern formation , cell fate choice and proliferation during Drosophila compound eye development . As such their regulation and gene expression is often highly temporally and spatially dynamic allowing for the proper differentiation of the multiple cell types necessary for the proper function of the compound eye [40] . eyes absent is a core member of this network and provides a key example of this type of complex gene expression . In this report we have identified several enhancers that cooperate to regulate temporal and spatial expression of eya in the developing retina ( Figs 2 and 11 ) . It is not uncommon for a single expression pattern to be controlled by multiple enhancers [39–44] . We find that a single enhancer module , comprised of three distinct and separable cis-regulatory elements , is responsible for the correct temporal and spatial expression of eya ( Figs 3 and 4 ) . Furthermore , the three elements ( 1+E+2 ) that comprise the composite enhancer regulate eya at specific times during retinal development ( Figs 3 and 11 ) . For example , enhancer E controls early eya expression while enhancer 1 is responsible for the bulk of late eya transcription ( Fig 3 ) . Having separate cis-regulatory elements control eya expression at different times during development is consistent with the idea that RD genes are dynamically regulated temporally and spatially to insure distinct expression patterns necessary for the differentiation of specific retinal cell types over the course of eye development [40] . In the context of temporal expression we propose that these enhancers function additively ( Figs 3 , 5 and 11 ) . Additive control of gene expression levels has been described in many organisms including Drosophila . In the Drosophila embryo a set of proximal and distal enhancers controls the expression patterns of the hunchback ( hb ) and knirps ( kni ) gap genes [41–44] . The expression level of each of these two genes appears to be the sum of the levels that are driven by the individual enhancers [45] . Although additional experimentation to measure the specific contribution of each of the elements in terms of transcriptional output of eya is required , the results of our cDNA fusion rescue experiments support a model for these elements functioning additively as the overall size of the eye increases when enhancers #1 and E are combined and complete rescue occurs when all three elements ( #1+E+2 ) are placed together ( Figs 4 and 5 ) . It has been shown that deleting enhancer E leads to a loss of eya expression early in development when it is normally needed to promote tissue specification and cell proliferation . We find that this early loss of eya expression is a major , but not the sole , contributor to the complete loss of the compound eye in eya2 mutants . Our results indicate that spacing is critical for the proper function of two cis-regulatory elements within the composite enhancer ( Figs 4 and 6 ) . Specifically , we find that the primary cause for the loss of eya expression in eya2 mutants is the direct juxtaposition of two flanking cis-regulatory elements ( 1 and 2 ) rather than the deletion of the intervening regulatory element ( E ) . Placement of a neutral sequence between these two elements recapitulates normal spacing in the genome , restores their ability to drive a transcriptional reporter , and rescues the no-eye phenotype of eya2 mutants ( Fig 6 ) . The eve locus provides a parallel example to what we observe in the eye with eya . The eve stripe 2 and stripe 3 enhancers are separated by 1 . 7kb of neutral genomic sequence . When these enhancers are placed directly adjacent to each other the expression pattern driven by both enhancers is altered . Normal expression is restored when a short 160bp sequence is inserted between the two enhancers suggesting that without correct spacing improper short-range interactions between cis-regulatory elements can lead to abnormal expression patterns [46] . In the eya2 mutant , enhancers 1 and 2 are directly juxtaposed to each other . Since expression of eya is lost in all undifferentiated cells we propose that inappropriate short-range repression between the two enhancers is likely inactivating both elements . We have not investigated the minimal spacing requirements for the eya retinal enhancers but based on the results from [46] , the distance is likely to be relatively short . It has been widely assumed that So plays a role in regulating eya in cells undergoing eye specification . This was based in part on the loss of eya expression in so loss-of-function mutants [29] as well as the presence of So binding sites within the eya locus ( including enhancer E ) and the ability of So to bind to the eya locus [11 , 33 , 34] . In fact it is the presence of predicted So binding sites which first led us to explore which DNA elements are controlling eya expression in the developing retina . When placed within so mutant backgrounds we found , however , that the eya enhancers were still active ( Fig 7 ) . This led us to re-examine Eya protein expression in so loss-of-function mutants and we find that Eya expression is lost progressively over the course of larval eye development ( Fig 8 ) . Although these results clearly demonstrate that So is not required for the initiation of eya expression they do not rule out a role for So in the maintenance of its expression . The progressive loss of eya could be the consequence of a requirement for So in the regulation of eya later in larval eye development . Our initial analyses of the eya enhancers would partially support this model of regulation . Enhancer 2 , which is bound by So , functions within photoreceptors late in eye development and therefore would be a promising candidate for regulation by So ( Figs 3 and 11 ) . Conversely , enhancer E , which contains a predicted So binding site , is the cis-regulatory element responsible for the bulk of early not late eya expression ( Figs 3 and 11 ) . It seems unlikely that So is regulating eya through this enhancer given that So is not required for the initiation of eya expression . Two additional enhancers ( PSE and enhancer 4 ) which we find to function redundantly to the composite enhancer both contain predicted So binding sites and a larger DNA fragment containing the PSE was found to be bound by So . Like enhancer 2 , both of these enhancers function in photoreceptors and cone cells later in eye development and as such might be good candidates for the maintenance of eya by So ( Figs 3 and 11 ) . In our assays these elements seem to function redundantly to the composite enhancer therefore it is likely if these enhancers are regulated by So it is in a manner more similar to that of a shadow enhancer [44] to ensure robust eya expression . Interestingly , bioinformatic conservation analysis on these enhancer elements would suggest that a requirement for So in the regulation of eya might not be conserved across Drosophila species . Analysis of the composite enhancer shows that the bulk of conservation lies only within enhancers 1 and E ( S7A Fig ) . There is no sequence conservation within enhancer 2 and the So binding sites in enhancers E , PSE , and 4 are also not conserved ( S7A-S7C Fig ) . However , a stretch of sequence conservation in the PSE is present immediately adjacent to the predicted So binding site ( S7B Fig ) . Previous studies on so1 and eya2 mutants support an alternative model in which the loss of eya in so mutants could be the result of a combination of increased cell death of retinal progenitors [17 , 18] and a progressive cell fate transformation from retinal progenitor to head epidermis [35 , 38] . And indeed we observe both phenomena occurring simultaneously in so loss-of-function mutants ( Figs 8–10 ) . Our re-examination of the so mutants showed eya expression slowly terminates as the tissue is gradually altering its fate ( Fig 8 ) . The state of the cell and/or tissue is an underappreciated idea that needs to be considered when attempting to establish regulatory relationships between transcription factors and putative downstream targets . A wealth of expression data and evidence of molecular interactions may not be sufficient , in all cases , to conclude that a gene is under the control of the DNA binding protein in question .
The following fly strains were used in this study: ( 1 ) eya1 , ( 2 ) eya2 , ( 3 ) so1 , ( 4 ) FRT42D so3/CyO , ( 5 ) FRT42D Ubi-GFP/CyO , ( 6 ) y w eyflp , ( 7 ) w1118 , ( 8 ) w;; ey-GAL4 , ( 9 ) UAS-soVP16 , ( 10 ) y1 M ( vas-int . Dm ) ) [2]ZH-2A w*; PBac ( y+-attP-3B ) VK00033—BL24871 , ( 11 ) y1 M ( vas-int . Dm ) ZH-2A w*; PBac ( y+-attP-9A ) VK00019—BL24866 , ( 12 ) so1 , UAS-P35 , ( 13 ) w1118; eya composite enhancer-GAL4 . Loss-of-function clones were generated with the following genotype: y w eyflp; FRT42D so3/FRT42D Ubi-GFP . All crosses were conducted at 25°C . BL = Bloomington Drosophila Stock Center The following antibodies were used: ( 1 ) mouse anti-Eya ( 1:5 , DSHB ) , ( 2 ) mouse anti-β-galactosidase ( 1:250 , Promega ) , ( 3 ) chicken anti-β-galactosidase ( 1:800 , Promega ) , ( 4 ) rat anti-Elav ( 1:100 , DSHB ) . ( 5 ) rabbit anti-Dcp-1 ( 1:100 , Cell Signaling Technologies ) . DSHB = Developmental Studies Hybridoma Bank . Fluorophore-conjugated secondary antibodies and phalloidin-fluorophore conjugates were obtained from Jackson Immuno Research Laboratories and Life Technologies . Imaginal discs were prepared as described previously in [47] . For dissections performed at specific time intervals , adult flies were placed in egg laying chambers and allowed to lay for 30–60 minutes on agar plates . Individual embryos were then transferred to individual microcentrifuge tubes with approximately 200ul of standard fly media . The tubes were then placed at 25°C and aged for the appropriate amount of time . Eye-antennal discs were photographed on a Zeiss Axioplan II compound microscope . For scanning electron microscopy , adult flies were serially incubated in 25% ethanol , 50% ethanol , 75% ethanol , 100% ethanol , 50% ethanol: 50% hexamethyldisilazane ( HMDS ) , and then 100% HMDS , coated with gold-palladium , and viewed with a JEOL 5800LV SEM . For light microscopy of adult heads , flies were photographed on a Zeiss Discovery Microscope . 3mL of Drosophila Kc167 cells ( approximately 1X107 cells/mL ) were transfected with a total of 400ng of plasmid DNA using the Qiagen Effectene Transfection Reagent ( Cat . No . 301427 ) . For each transfection , mt-GAL4 ( 136ng ) was transfected along with the indicated UAS responder plasmids ( 64ng each ) and ARE-luciferase ( 132ng ) . UAS-renilla ( 0 . 26ng ) was also included in the transfection mix as a control for transfection efficiency . The plasmids were diluted in 98μL of Buffer EC , then 3 . 2μL of the Enhancer Solution was added to the dilution . The solution was incubated at room temperature for 5min . 10μL of Effectene Transfection Reagent was added to the dilution and the solution was incubated at room temperature for an additional 10min . The transfection solution was mixed with 600μL of Hyclone SFX Insect Culture Media ( Cat . No . SH30278 . 02 ) and added drop-wise to the plated cells . Following transfection the cells were incubated at 25°C for 20hr . Protein production was then induced by the addition of 1mM CuSO4 . Following induction cells were incubated at 25°C for an additional 24hr before harvesting for determination of luciferase activity . The luciferase activity was assayed using the Promega Dual Luciferase Reporter Assay System ( Cat . No . E1910 ) and a Promega GloMAX 20/20 Luminometer ( Model No . E5311 ) . Cells were collected by centrifugation at 500g for 2min . The supernatant was removed and the pellet was re-suspended in 500μL of Passive Lysis Buffer ( PLB ) at the working concentration . The cells were lysed in the PLB for 20min at room temperature . 20μL of cell lysis solution was added to 100μL of Luciferase Assay Reagent II and mixed by pipetting for 10sec . The light output of the solution was measured in the luminometer once a second for 10s and the average output over the time period was recorded . This was the activity of the luciferase enzyme—the experimental result . 100μL of Stop and Glo Reagent was then added to the tube and mixed briefly by vortexing . The light output of the solution was once again measured , and the results were recorded as the output from Renilla enzyme—the transfection efficiency control . These two measurements were performed for each of three separate plates of independently transfected cells for each plasmid combination . The Relative Luciferase Units ( RLUs ) for each combination of plasmids were calculated by dividing the experimental light output ( Luciferase ) by the transfection efficiency control ( Renilla ) for each of the three independent transfections . Error bars in Fig 1 represent standard deviation The target sequence for So and So-VP16 consists of five copies of the ARE element ( GGT GTC AGG TTG CTC GAG ) that is reported in [23 , 48] placed upstream of the luciferase gene within the pGL3 vector ( Promega , catalog #E1751 ) . For lacZ reporter analysis individual genomic fragments illustrated in Fig 2 were amplified from w1118 genomic DNA and cloned into either p-lacZ . attB or pg-lacZ . attB plasmids ( Konrad Basler , University of Zurich , Switzerland ) . Genomic fragment sequences are provided in S1 Table . Cloning strategies and primer sequences are listed in S2 Table . RED refers to standard restriction enzyme digestion and ligation into a multiple cloning site . Gateway refers to the Life Technologies Gateway Recombination Cloning system . For the cDNA enhancer fusion rescue assay , a pg-eya RB+3’UTR cDNA . attB plasmid was created by modifying an existing pg-RFP . attB plasmid ( derived from pg-lacZ . attB ) . The eya RB+3’UTR cDNA was first amplified by PCR from an existing pUAS-eya RB+3’UTR plasmid as an EcoRI-NdeI fragment and cloned into a pg-RFP . attB plasmid . Portions of the Gateway cloning cassette and hsp70 minimal promoter were then amplified from pg-RFP . attB as an EcoRI fragment and cloned ahead of the eyaRB+3’UTR cDNA . Primer sequences are listed in S2 Table . Putative enhancers ( Fig 2A ) were amplified from the appropriate p . lacZ . attB plasmid and cloned into the new pg-eya RB+3’UTR cDNA . attB plasmid using Gateway recombination cloning ( Life Technologies ) . Gateway 5’ att primer sequence: 5`-GGG GAC AAG TTT GTA CAA AAA AGC AGG CTC AAC-3`and Gateway 3’ att primer sequence: 5`-GGG GAC CAC TTT GTA CAA GAA AGC TGG GTC CTA-3` Enhancer 2 minimal fragment and enhancer 1+5bp+2 fragment were synthesized by IDT and flanked by Gateway att sequences for recombination into the pg-lacZ . attB and pg-eya RB+3’UTR cDNA plasmids . Enhancer 1 was amplified from the p-eya-enhancer 1 . lacZ . attB plasmid with the following primers: 5`primer: 5`-ATA ATA AAG CTT ACT ACA CCT CGT ACC AAA TTC TCG G-3`and 3`primer: 5`-CCT GCT CAA CTC AAA TGG CCA GTT TCG TCT CC-3`Enhancer 2 was amplified from the p-eya enhancer 3 . lacZ . attB plasmid using the following primers: 5’ primer: 5`-GGA GAC GAA ACT GGC CAT TTG AGT TGA GCA GG-3`and 3’ primer: 5`-ATA ATA GGT ACC TCA ACT GAT TCG ACT TGG TCG-3`PCR products were combined together using Gibson Assembly ( New England Biolabs ) . Gateway recombination sequences were then added to the 5`and 3`ends of the product using the following primers: 5`primer: 5`-GGG GAC AAG TTT GTA CAA AAA AGC AGG CTC AAC ACT ACA CCT CGT ACC AAA TTC TCG G-3`and 3`primer: 5`-GGG GAC CAC TTT GTA CAA GAA AGC TGG GTC CTA TCA ACT GAT TCG ACT TGG TCG AAA AGC-3`The resulting fragment ( enhancer 1+2 ) was cloned into the pDONR201 plasmid and shuttled into pg-lacZ . attB and pg-eyaRB+3’UTR cDNA . attB plasmids using Gateway recombination cloning ( Life Technologies ) . Enhancer 1 minimal fragment was amplified from the pg-eya enhancer 1 minimal . lacZ . attB plasmid using the following primers: 5`primer: 5`-AAA TAT TTG GAT ATG TGG GGG AAA GGG-3`and 3’ primer: 5`-ATA ATA GAA TTC GGC CAG TTT CGT CTC CTC TTT TGC-3` ( adds an EcoRI site ) . The spacer fragment was amplified from the pg-eya intron 1-1 . lacZ . attB plasmid using the following primers: 5`primer: 5`-ATA ATA GAA TTC TGA AAG ATC TCA ATT AGC TAA CCG-3` ( adds an EcoRI site ) and 3’ primer: 5`-ATA ATA TCT AGA CAA CTG CTA CCA TTT TGG CCA TTT C-3` ( adds a XbaI site ) . Enhancer 2 was amplified from the p-eya enhancer #2 . lacZ . attB plasmid using the following primers: 5’ primer: 5`-ATA ATA TCT AGA ATT TGA GTT GAGCAGGTCAGTTAATATTAC-3` ( adds a XbaI site ) and 3’ primer: 5`-TCA ACT GAT TCG ACT TGG TCG-3`The three fragments were ligated together to generate 1+spacer+2 . The following primers were then used to amplify this product , which was then cloned into the p-lacZ . attB plasmid as a HindIII-KpnI fragment . 5’ primer: 5`-ATA ATA AAG CTT AAA TAT TTG GAT ATG TGG GGG AAA GGG-3` ( adds a HindIII site ) and 3’ primer: 5`-ATA ATA GGT ACC TCA ACT GAT TCG ACT TGG TCG-3` ( adds a KpnI site ) . The pg-eya RB cDNA+3’UTR . attB plasmid ( see above ) was digested with HindIII and KpnI resulting in a plasmid missing the Gateway cassette , hsp70 promoter and a portion of the eya RB cDNA . Into this plasmid was cloned the 1+spacer+2 region ( see above ) as a HindIII-KpnI fragment resulting in a p-eya 1+spacer+2 eya RB cDNA+3’UTR ( partial ) . attB plasmid that is still missing the hsp70 minimal promoter and a portion of the eya RB cDNA . These pieces were amplified as a single fragment from pg-eya RB cDNA+3’UTR . attB using the following primers: 5’ primer: 5`-TCG AAT CAG TTG AGG TAC CTC TAG AGC-3` ( adds a KpnI site ) and 3’ primer: 5`-CCA GAG CCG GCG GTA CCC ACA CTG-3` ( adds a KpnI site ) . This fragment was cloned into p-eya 1+spacer+2 eyaRB cDNA+3’UTR ( partial ) . attB as a KpnI fragment to yield the final p-eya 1+spacer+2 eyaRB cDNA+3’ UTR . attB plasmid . Enhancer 2 and the composite enhancer were amplified from the p-eya enhancer 2 . lacZ . attB and p-eya composite enhancer . lacZ . attB plasmids respectively ( primer sequences are listed in S2 Table ) . The 3’ primer adds 40bp of genomic sequence downstream of enhancer 2 and the transcriptional start site to ensure the entire endogenous promoter region was included . These 40bp were omitted from the above plasmids since a hsp70 minimal promoter is included within the plasmid . Gateway recombination sequences were added to the ends of each construct and the fragments were cloned into the pg-lacZ . attB plasmid that lacks a hsp70 promoter ( Konrad Basler , University of Zurich , Switzerland ) using standard Gateway Recombination Cloning . All lacZ reporter and cDNA fusion constructs were stably integrated into the pBAC ( y+-attP-3B ) VK00033 third chromosome landing site using PhiC31-mediated integration . Proper site-specific integration was confirmed by PCR with attP/attB primers and the correct sequence of the construct was confirmed . The composite enhancer-lacZ construct was also inserted into a second landing site on the third chromosome for comparison: PBac ( y+-attP-9A ) VK00019 . The genomic region surrounding the eya1 deletion [31] was amplified from genomic DNA of the eya1 stock ( BL-3631 ) . Genomic DNA from the same region was amplified from w1118 . The following primers were used to amplify the area surrounding the deletion: 5`primer: 5’-TTC CCG CTG GTG ACT TAC TG-3’ and 3`primer: 5’-GTT GTG AGG GAG CTG TCT GG-3’ The 5’ primer sits 2683 bp upstream of the eya RB transcriptional start site and the 3’ primer sits 702bp into the first intron . Q5 high-fidelity DNA polymerase ( New England Biolabs ) was used for the amplification . The PCR product was purified using GeneJet PCR Purification Kit ( Thermo-Fisher #K0701 ) . The amplified region from w1118 is approximately 4kb while it is just over 2kb in the eya1 mutant stock . Twelve sequencing primers were used to sequence the amplified genomic region in both directions . Primer sequences are listed within S3 Table . The eya1 deletion is 1826bp in size: it begins 581bp upstream of the 5’ start of the eya2 deletion and extends 344bp into the 5’ UTR of the eyaRB transcript . There are an additional 11bp that do not correspond to the published genomic region . BL = Bloomington Drosophila Stock Center qPCR was performed as previously described [49] . For each experiment three biological replicates were analyzed once . For each biological replicate , approximately 50 eye-antennal imaginal discs from wandering 3rd instar larvae were dissected in PBS and immediately placed into a microcentrifuge tube containing 200ul of RLT buffer with β-mercaptoethanol ( Qiagen #79216 ) . The tissue was disrupted with a pestel for 1 minute . After disruption an additional 150ul of RLT buffer with β-mercaptoethanol was added to the tube and the sample homogenized using a QIAshredder column ( Qiagen #79654 ) . After homogenization total RNA was isolated using the Qiagen RNeasy Mini Kit ( Qiagen #74101 ) . 100-200ng of total RNA was reverse transcribed to cDNA using the SuperScript III First Strand Synthesis System with oligo ( dT ) primers ( Invitrogen ) . qPCR was performed on a Roche LightCycler 480 using SYBR Green I Master Mix ( Roche ) . For each experiment , target genes were analyzed on biological triplicate samples and normalized to rp49 . 3–4 serial dilutions of pooled cDNA were used to determine primer amplification efficiencies for each target gene . In eya1 and eya2 rescue experiments , primers specific to the endogenous eya RA and eya RB transcripts were used . Roche LightCycler 480 Software ( Version 1 . 5 ) was used to calculate cycle threshold values and melting curves for each reaction . Relative expression and standard error was calculated using Relative Expression Software Tool ( REST ) [50] . Error bars generated by REST analysis reflect standard error determined by a confidence interval centered on the median , allowing representation of asymmetric tendencies in the data . Primers were designed using A plasmid Editor ( ApE ) or Fly Primer Bank [51] . Primer sequences are listed in S4 Table . Examination of the eya locus ( both strands ) for predicted So binding sties was performed using the following reported sequences: GTAANYNGANAYC [52] , GTAANYNGANAYG [52] , GGTATCA [53] , GATATCA [53] , TGATAC [54] , TGATAC [32] , CGATAC [32] , ATTGATATCAAT [55] , and TTGATATCAA [55] . | Activation of a gene requires interactions between enhancer and promoter elements . It has been known for some time that transcription of a gene expressed in a complex pattern or in multiple tissues is regulated by an array of enhancers . Recent studies have also demonstrated that multiple enhancers can regulate a single expression pattern within a single tissue . In this study we asked how the expression pattern of eyes absent ( eya ) is regulated at the level of the enhancer in the developing retina . We found that several adjacently positioned enhancer elements function cooperatively to control temporal and spatial expression of eya and that the spacing between two of these cis-regulatory elements is important to their function . This study shows the importance of enhancer cooperation and architecture in regulating complex and dynamically changing expression patterns . | [
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"organisms"
] | 2016 | Retinal Expression of the Drosophila eyes absent Gene Is Controlled by Several Cooperatively Acting Cis-regulatory Elements |
Checkpoint signaling requires two conserved phosphatidylinositol 3-kinase-related protein kinases ( PIKKs ) : ATM and ATR . In budding yeast , Tel1 and Mec1 correspond to ATM and ATR , respectively . The Tel2-Tti1-Tti2 ( TTT ) complex connects to the Rvb1-Rvb2-Tah1-Pih1 ( R2TP ) complex for the protein stability of PIKKs; however , TTT-R2TP interaction only partially mediates ATM and ATR protein stabilization . How TTT controls protein stability of ATM and ATR remains to be precisely determined . Here we show that Asa1 , like Tel2 , plays a major role in stabilization of newly synthesized Mec1 and Tel1 proteins whereas Pih1 contributes to Mec1 and Tel1 stability at high temperatures . Although Asa1 and Pih1 both interact with Tel2 , no Asa1-Pih1 interaction is detected . Pih1 is distributed in both the cytoplasm and nucleus wheres Asa1 localizes largely in the cytoplasm . Asa1 and Pih1 are required for proper DNA damage checkpoint signaling . Our findings provide a model in which two different Tel2 pathways promote protein stabilization of Mec1 and Tel1 in budding yeast .
Chromosomes are constantly challenged by exogenous and endogenous threats . The repair of damaged chromosomes is therefore crucial for maintaining genome stability [1–3] . Improper DNA damage response induces genomic instability , resulting in cancer development . The cellular responses to DNA damage consist of DNA repair and checkpoint signaling [4 , 5] . Activation of checkpoint signaling depends on two evolutionarily conserved phosphatidylinositol 3-kinase ( PI3K ) -related protein kinases ( PIKKs ) : ATM and ATR [4 , 5] . In the budding yeast Saccharomyces cerevisiae ATM and ATR correspond to Tel1 and Mec1 , respectively [4] . ATM/Tel1 responds primarily to DNA double-strand breaks ( DSBs ) [6] , whereas ATR/Mec1 recognizes various types of DNA lesions with single-stranded DNA ( ssDNA ) [7] . ATM is recruited to DSB ends and activated by interacting with the Mre11 complex consisting of Mre11-Rad50-Xrs2 ( Nbs1 in human ) [8 , 9] . ATR forms a stable complex with ATRIP ( equivalent to Ddc2 in budding yeast ) [10–13] , which recruits ATR to sites of DNA damage by interacting with replication protein A ( RPA ) -coated ssDNA [14–17] . Once activated , ATM and ATR phosphorylate checkpoint mediators ( for example , MDC1 in humans and Rad9 in budding yeast ) that create a docking site for the effector kinases , such as Chk2 in human and Rad53 in budding yeast [4 , 5] . Interaction with checkpoint mediators allows ATM/Tel1 and ATR/Mec1 to extensively phosphorylate the effector kinases , thereby promoting full activation of checkpoint responses [4 , 5] . The ATM- and ATR-mediated checkpoint response is under another layer of control besides protein-protein interaction at sites of DNA damage . Several lines of evidence have indicated that the conserved Tel2-Tti1-Tti2 ( TTT ) complex interacts with and controls protein maturation and stabilization of ATM and ATR family proteins [18–23] . Consistent with this notion , TTT has been shown to play a role in DNA damage response in multiple organisms , including budding yeast [18 , 20–22 , 24–28] . ATM and ATR family proteins also control telomerase recruitment to telomeres [29–33] . TTT has been implicated in telomere length control in budding yeast cells as well as nematodes [34–37] . TTT connects to the R2TP complex that interacts with the conserved Hsp90 chaperone [23 , 38–40] . The R2TP complex consists of four proteins including AAA-type ATPase Rvb1 and Rvb2 , Tah1 and Pih1 in budding yeast [38] . The R2TP complex is conserved in eukaryotes; Rvb1 , Rvb2 , Tah1 and Pih1 correspond to RUVBL1 , RUVBL2 , PRAP3 and PIH1D1 , respectively , in mammals [41] . Rvb1 and Rvb2 associate with each other as a double hexamer to interact with Pih1 in budding yeast and mammalian cells [41] . Recent evidence indicates that Tel2 is constitutively phosphorylated by casein kinase 2 and its phosphorylation mediates Tel2-Pih1 interaction in budding yeast and mammalian cells [23 , 40] . Thus , TTT cooperates with the Hsp90 chaperone pathway through the R2TP complex . However , the TTT-R2TP connection does not fully explain TTT-mediated protein stabilization of ATM and ATR . Disruption of Tel2-Pih1 interaction had a clear impact on the protein stability of mTOR and SMG1 but only partially affected the protein stability of ATM and ATR in mouse cells [23] . How TTT promotes the protein stability of ATM and ATR has yet to be defined . The essential Tra1 protein is homologous to human TRRAP , a protein component of histone acetyltransferase complexes [42] . A combination of serial proteomic approaches has identified a Tra1-containing protein complex , called ASTRA , as a potential chromatin-remodeling factor [43] . In the ASTRA complex , Tra1 interacts with Asa1 , the Rvb1-Rvb2 complex and the Tel2-Tti1-Tti2 complex [43] . Although Tra1 does not exhibit protein kinase activities , it shares some structural similarities to PIKKs [42] . Indeed , TTT is required to maintain proper expression of Tra1 protein [28 , 44] . Like tel2 mutations , the asa1-1 mutation decreases the level of Tel1 and Tor1 protein [37] . Consistent with defective Tel1 and Tor1 expression , asa1 mutant cells exhibit telomere length defects and sensitivities to rapamycin [37] . Thus , Asa1 has been implicated as a functional partner of TTT in PIKK biogenesis . At this moment , however , it is not known whether Asa1 is incorporated into the TTT-R2TP module . In this study we show that the TTT complex regulates Mec1 and Tel1 protein stability , and that the Rvb1-Rvb2 complex is essential for proper Mec1 and Tel1 protein expression . We further show that Asa1 controls stability of newly synthesized Mec1 and Tel1 protein whereas Pih1 is required for Mec1 and Tel1 protein stability primarily at high temperatures . Tel2 and Pih1 are localized to both the nucleus and the cytoplasm whereas Asa1 is predominantly located in the cytoplasm . Although both Asa1 and Pih1 interact with Tel2 , no Asa1-Pih1 interaction is detected even at high temperatures . Our results support a model in which two different Tel2-mediated pathways control protein stability of Mec1 and Tel1 .
We adapted an improved auxin-inducible degron ( AID ) system [45 , 46] to further delineate Tel2 function . In this system , target proteins are destroyed by auxin-mediated protein degradation and transcription of target genes is repressed by using the tetO promoter [46] . Tel2-aid protein was largely depleted within one hour after the addition of indole-3-acetic acid ( IAA ) and doxycycline ( Dox ) ( Fig 1A ) . TEL2 is essential for cell proliferation [34] . Correspondingly , tel2-aid mutants ceased cell proliferation in the presence of IAA and Dox ( Fig 1B ) . Tel2 depletion did not lead to cell-cycle stage specific arrest ( S1 Fig ) , supporting the view that the TTT pathway regulates protein expression of all PIKKs including Tor1 and Tra1 in budding yeast . We first examined the effect of Tel2 depletion on expression levels of Mec1 and Tel1 protein ( Fig 1C ) . Cells expressing FLAG-tagged Mec1 or Tel1 proteins from the respective endogenous promoter were treated with IAA and Dox , and subjected to immunoblotting analysis with anti-FLAG antibodies . Mec1 and Tel1 expression was reduced to less than 15% of the initial level at 6 hr after treatment with IAA and Dox in tel2-aid tagged cells but not in untagged cells ( Fig 1C and S2 Fig ) . Quantitative reverse transcription PCR ( qRT-PCR ) analysis showed that Tel2 depletion does not significantly affect mRNA levels of MEC1 and TEL1 ( Fig 1D ) . Extended nocodazole treatment did not decrease levels of Mec1 or Tel1 protein ( S3 Fig ) , supporting that neither prolonged cell-cycle arrest nor proliferation defect affects expression levels of Mec1 and Tel1 . Mec1 and Tel1 both control the DNA damage checkpoint although Mec1 plays a predominant role [4] . Activation of the DNA damage checkpoint pathway is correlated with phosphorylation of the downstream kinase Rad53 [4] . We examined the effect of Tel2 depletion on Rad53 phosphorylation after DNA damage ( Fig 1E and S4 Fig ) . Cells were arrested with nocodazole and treated as above to deplete Tel2 and thereafter exposed to methyl methanesulfonate ( MMS ) . Cells were then analyzed by immunoblotting to monitor Rad53 phosphorylation status . DNA damage-induced Rad53 phosphorylation was significantly decreased after Tel2 depletion . IAA/Dox treatment by itself did not affect damage-induced Rad53 phosphorylation ( S5 Fig ) . Thus , Tel2 plays a key role in activation of DNA damage checkpoint signaling in budding yeast . We addressed whether Tel2 depletion impairs protein stability of newly-synthesized Mec1 and Tel1 ( Fig 1F and S6 Fig ) . To monitor protein stability , we used tel2-aid cells carrying the GAL-FLAG-MEC1 or GAL-FLAG-TEL1 plasmid . We expressed FLAG-tagged Mec1 or Tel1 from the GAL1 promoter at an expression level similar to the endogenous level . We first depleted Tel2 using the AID system and then transiently induced the expression of Mec1 or Tel1 from the GAL1 promoter . After glucose and cycloheximide addition ( transcription and translation shut-off ) , we tracked the abundance of Mec1 and Tel1 protein expression to determine the effect of Tel2 depletion on protein stability . Half-lives of newly-synthesized Mec1 and Tel1 protein after transcription and translation shut-off were estimated ~100 min before Tel2 depletion but became ~50 min after Tel2 depletion . Although Tel2-aid was not as stable as tubulin , Tel2-aid protein was present in the presence of cycloheximide if cells were not treated with IAA/Dox . We next examined whether Tel2 depletion affects protein stability of pre-synthesized Mec1 and Tel1 ( Fig 1G and S7 Fig ) . Cells were initially grown in galactose to express FLAG-Mec1 or Tel1 protein at an expression level similar to the endogenous level and then treated with glucose to repress the GAL1 promoter . The culture was maintained in glucose for six hours and treated with IAA/Dox or mock-treated for one hour . Cells were subsequently exposed to cycloheximide . We monitored the levels of Mec1 and Tel1 protein Tel2-aid after cycloheximide treatment . Tel2 depletion was found to have no significant impact on protein stability of pre-synthesized Mec1 and Tel1 . Half-lives of pre-synthesized Mec1 and Tel1 proteins were estimated longer than 2 hr; pre-synthesized Mec1 and Tel1 proteins are in a stable state compared with newly-synthesized ones . Together , these results suggest that Tel2 controls protein stability of newly-synthesized Mec1 and Tel1 but does not play an apparent role in the maintenance of pre-synthesized Mec1 and Tel1 , supporting the current view that the Tel2-Tti1-Tti2 ( TTT ) complex promotes protein maturation and regulates functions of ATM and ATR [18 , 21 , 22] . Studies of mammalian cells have provided the model in which the TTT complex interacts with the Rvb1-Rvb2-Tah1-Pih1 ( R2TP ) complex to recruit the HSP90 chaperone machinery for the proper folding and stabilization of PIKKs [22 , 23] . We thus addressed if the R2TP complex mediates TTT-dependent stabilization of Mec1 and Tel1 proteins in budding yeast . We first examined whether the Rvb1-Rvb2 complex controls protein stability of Mec1 and Tel1 as TTT does ( Fig 2 ) . Both RVB1 and RVB2 are essential for cell proliferation [47 , 48] . We generated an rvb2-aid allele to examine the role of Rvb1-Rvb2 complex in protein stabilization of Mec1 and Tel1 . Rvb2-aid was depleted within two hours after IAA and Dox treatment ( Fig 2A ) . Accordingly , Rvb2 depletion decreased cell proliferation ( Fig 2B ) . No cell-cycle specific arrest resulted from Rvb2 depletion ( S8 Fig ) [49] . As found for Tel2 depletion , Rvb2 depletion lowered endogenous expression levels of Mec1 and Tel1 ( Fig 2C ) and impaired Rad53 phosphorylation after MMS treatment ( Fig 2D and S9 Fig ) . The Rvb1-Rvb2 complex associates with chromatin remodeling complexes and controls transcription of numerous genes [43 , 50] . Previous genome-wide analyses , however , did not identify MEC1 and TEL1 as a transcriptional target gene of Rvb1-Rvb2 complex [49] . We confirmed that Rvb2 depletion does not affect mRNA levels of MEC1 and TEL1 by qRT-PCR analyses ( Fig 2E ) . These results are consistent with the model in which the Rvb1-Rvb2 complex collaborates with the TTT complex and contributes to protein stabilization of Mec1 and Tel1 . We were not able to carry out the experiment using the GAL-FLAG-MEC1 or GAL-FLAG-TEL1 plasmid , since RVB1 and RVB2 are required for induction of the GAL promoters [49] . We next examined if Pih1 controls expression levels of Mec1 and Tel1 ( Fig 3 ) . We note that PIH1 is not essential for cell proliferation . Deletion in PIH1 had very minor effects on protein expression levels of Mec1 and Tel1 protein; no apparent or very minor ( ~10% ) decrease was observed for Mec1 or Tel1 protein , respectively ( Fig 3A ) . Pih1/PIH1D1 has been proposed to connect the TTT complex to the Rvb1-Rvb2 complex [23 , 40] . However , Rvb1-Tel2 interaction was still detectable even in the absence of Pih1 ( Fig 3B ) . We confirmed that Tel2 interacts with Pih1 by co-immunoprecipitation analysis ( Fig 3C ) . Thus , although the TTT-R2TP axis exists in budding yeast , Pih1-independent mechanisms appear to tether TTT to the Rvb1-Rvb2 complex . Since Asa1 has been implicated as a functional partner of TTT in PIKK biogenesis [37] , we next explored the link of Asa1 to TTT-mediated Mec1 and Tel1 protein stabilization . We first examined whether Asa1 interacts with Mec1 and Tel1 by co-immunoprecipitation analysis . Mec1 and Tel1 were co-immunoprecipitated with Asa1 only when cells carried ASA1-myc and MEC1-HA or TEL1-HA , indicating that Asa1 interacts with Mec1 and Tel1 ( Fig 4A ) . We addressed whether Asa1 regulates protein expression of Mec1 and Tel1 at a post-translational level . Similar to Tel2 and Rvb2 , Asa1 is essential for cell proliferation [51] . We thus constructed an asa1-aid allele and determined the effect of Asa1 depletion on Mec1 and Tel1 protein levels . Asa1 was depleted within one hour after treatment with IAA and Dox ( Fig 4B ) , and Asa1 depletion impaired cell proliferation ( Fig 4C ) . Asa1 depletion did not result in cell-cycle stage specific arrest ( S10 Fig ) . As found for Rvb2 and Tel2 depletion , Asa1 depletion decreased the endogenous protein levels of Mec1 and Tel1 ( Fig 4D ) but did not affect the transcript levels ( Fig 4E ) . Asa1 depletion was found to impair Rad53 phosphorylation after DNA damage ( Fig 4F and S11 Fig ) . We addressed whether Asa1 depletion impairs protein stability of newly-synthesized Mec1 and Tel1 ( Fig 4G ) . asa1-aid cells , carrying the GAL-FLAG-MEC1 or GAL-FLAG-TEL1 plasmid , were cultured and analyzed as were tel2-aid cells ( see Fig 1 ) . Asa1 depletion was found to reduce levels of newly-synthesized Mec1 and Tel1 protein ( Fig 4G ) . We next examined whether Asa1 depletion has impact on stability of pre-synthesized Mec1 and Tel1 ( Fig 4H ) . There was no apparent effect of Asa1 depletion on pre-synthesized Mec1 and Tel1 proteins ( Fig 4H ) . Thus , like Tel2 , Asa1 appears to control protein stability of newly synthesized Mec1 and Tel1 . Asa1 is highly conserved in eukaryotes [43] , although its molecular function is unknown . Since Rvb1-Tel2 interaction occurs in the absence of Pih1 ( see Fig 3B ) , we considered the possibility that Asa1 mediates the interaction between TTT and the Rvb1-Rvb2 complex ( Fig 5 ) . asa1-aid cells expressing HA-tagged Tel2 or myc-tagged Rvb1 were treated with or without IAA and Dox . Cells were then subjected to co-immunoprecipitation and subsequent immunoblotting analysis . Unexpectedly , however , Asa1 depletion did not affect Rvb1-Tel2 interaction ( Fig 5A ) . We then examined whether Asa1 associates with either the TTT or the Rvb1-Rvb2 complex . Rvb2 depletion disrupted Asa1-Tel2 interaction ( Fig 5B ) whereas Tel2 depletion did not affect Asa1-Rvb1 interaction ( Fig 5C ) . These results show that Asa1 interacts with the Rvb1-Rvb2 complex rather than the TTT complex . To address the possibility that Asa1 associates with the R2TP complex , we examined whether Pih1 and Asa1 interact with each other . No apparent interaction between Asa1 and Pih1 was detected ( Fig 5D ) although both Asa1 and Pih1 are connected to Tel2 ( Figs 3C and 5B ) . TTT recognizes PIKKs for protein stabilization [18 , 21 , 22] . We next addressed whether Asa1 contributes to TTT recognition of Mec1 and Tel1 . We investigated the effect of Asa1 depletion on Tel2-Mec1 and Tel2-Tel1 interaction ( Fig 5E ) . Two-hour incubation with IAA and Dox largely eliminated Asa1 expression but did not lower the expression levels of Mec1 and Tel1; ( Fig 5E; see also Fig 4B and 4D ) . We note that two-hour Asa1 depletion in this experiment might not be as complete as six-hour depletion used in Fig 5A . Asa1 depletion was found to decrease interaction of Tel2 with Mec1 and Tel1 ( Fig 5E ) . Reduction in Tel2-Tel1 interaction was more apparent than that in Tel2-Mec1 interaction ( Fig 5E ) . These results suggest that Asa1 interacts with the Rvb1-Rvb2 complex and stimulates TTT to recognize Mec1 or Tel1 protein . We explored the role of Pih1 in Mec1 and Tel1 protein stability ( Fig 6 ) . Although PIH1 is not essential for cell proliferation , pih1 deletion confers temperature-sensitive growth defects ( Fig 6A ) [40] . We therefore tested a possibility that Pih1 contributes to Mec1 and Tel1 protein stabilization at high temperatures . We examined the effect of pih1Δ mutation on Mec1 and Tel1 protein levels after transferring from 30 to 37°C ( Fig 6B ) . Deletion of PIH1 decreased expression levels of Mec1 and Tel1 proteins at 37°C ( Fig 6B ) although it did not significantly affect mRNA levels ( Fig 6C ) . We further examined the effect of pih1Δ mutation on DNA damage checkpoint response . The pih1Δ mutation conferred a defect in Rad53 phosphorylation after MMS treatment at 37°C although no apparent phosphorylation defect was observed at 30°C ( Fig 6D and S12 Fig ) . Treatment with cycloheximide was found to stabilize Mec1 and Tel1 proteins at high temperatures ( S13 Fig ) probably because ubiquitin becomes limiting after translation inhibition [52] . We were therefore unable to use cycloheximide to monitor protein stabilization of Mec1 and Tel1 at high temperatures . Instead we took advantage of the fact that mRNAs are short-lived and most half-lives are 30 min or shorter [53 , 54] . If transcripts were absent , the effect of translation would be essentially eliminated . As mentioned above , transcription was shut off for 6 hr to generate pre-synthesized Mec1 and Tel1 proteins . We thus addressed whether Pih1 contributes to stabilization of pre-synthesized Mec1 and Tel1 proteins at high temperatures ( Fig 6E and S14 Fig ) . Wild-type and pih1Δ cells carrying the GAL-FLAG-MEC1 or GAL-FLAG-TEL1 plasmid were grown in the presence of galactose to activate the GAL1 promoter at 30°C and then incubated with glucose to repress the GAL1 promoter . Cells were cultured in glucose for 6 hr to allow protein stabilization of Mec1 and Tel1 . After the incubation with glucose , cultures were transferred to 42°C or retained at 30°C . Deletion of PIH1 decreased the expression levels of Mec1 and Tel1 after a shift from 30 to 42°C ( Fig 6E ) . No apparent effect on Mec1 and Tel1 expression was detected at 30°C ( S15 Fig ) , supporting the findings that Pih1 is dispensable for proper Mec1 and Tel1 expression at 30°C ( see Fig 3A ) . We confirmed that mRNAs from GAL-FLAG-MEC1 or GAL-FLAG-TEL1 were decayed out before the transfer from 30°C to 42°C ( S16 Fig ) . These results are consistent with the idea that Pih1 controls protein stability of mature Mec1 and Tel1 proteins primarily at high temperatures . We next investigated the effect of Asa1 depletion on pre-synthesized Mec1 and Tel1 proteins at 42°C ( Fig 6F and S17 Fig ) . asa1-aid cells carrying the GAL-FLAG-MEC1 or GAL-FLAG-TEL1 plasmid were grown in the presence of galactose to activate the GAL1 promoter at 30°C and then incubated with glucose to turn off the promoter and allow protein maturation for 6 hr . Cultures were treated with IAA and Dox or mock-treated for one hour and then transferred to 42°C or retained at 30°C . Asa1 depletion did not significantly affect protein stability of pre-synthesized Mec1 and Tel1 proteins at 42°C as found at 30°C ( Fig 6F and S18 Fig; see Fig 4H ) . Transcripts from the GAL-FLAG-MEC1 or GAL-FLAG-TEL1 construct were essentially at the background level before the temperature shift ( S19 Fig ) . Thus , Asa1 does not appear to play a major role in Mec1 and Tel1 stabilization at high temperatures although it remains possible that Asa1 plays a minor or overlapping role . Asa1-Pih1 interaction was undetectable even at 42°C ( Fig 5D ) . These findings suggest that Asa1 and Pih1 control protein stability of Mec1 and Tel1 at different levels . Mec1 and Tel1 are nuclear proteins although some Mec1 and Tel1 proteins are present in the cytoplasm [12 , 55] . To further dissect Asa1 and Pih1 functions , we compared cellular localization of Asa1 , Pih1 and Tel2 ( Fig 7 ) . Cellular fractionation analysis indicated that Pih1 and Tel2 both exist in both nuclear and cytoplasmic fractions ( Fig 7A and 7B ) . By contrast , Asa1 was largely localized in the cytoplasm ( Fig 7C ) . Together , our results support the model in which the Asa1 and the Pih1 pathways contribute differently to stabilization of protein kinases Mec1 and Tel1 ( Fig 7D ) .
The TTT complex is a key component to ensure proper protein levels of PIKKs including ATM and ATR [18–21] . The R2TP complex , consisting of AAA-ATPase Rvb1 and Rvb2 as well as Tah1 and Pih1 , is highly conserved from yeast to humans [41] . Previous studies have demonstrated that casein-kinase-mediated Tel2 phosphorylation promotes Tel2-Pih1 interaction , thereby connecting TTT to R2TP for stabilization of PIKKs [23 , 40] . However , mechanisms other than the TTT-R2TP pathway appear to control TTT-dependent functions , because defective Tel2-Pih1/PIH1D1 interaction has much less impact on the stability of ATM and ATR than complete loss of Tel2 function does [23] . In this study we have provided evidence indicating that two different pathways , the Tel2-Pih1 and the Tel2-Asa1 pathway , contribute to the quality control of Mec1 and Tel1 proteins in budding yeast . Like Tel2 , Asa1 plays a major role in proper Mec1 and Tel1 protein expression . In contrast , Pih1 is primarily required for Mec1 and Tel1 protein stabilization at high temperatures . Asa1 is largely located in the cytoplasm whereas Pih1 is distributed throughout the cell . It has been shown that Tel2 preferentially recognizes newly synthesized ATM and ATR under non-stress conditions [22] . Our results suggest the model in which the Tel2-Asa1 pathway promotes protein folding of newly synthesized Mec1 and Tel1 in the cytoplasm whereas the Tel2-Pih1 pathway stimulates protein refolding during heat stress . Studies of mammalian TTT complex have demonstrated that TTT regulates DNA damage signaling as well as ATM and ATR protein stability [18 , 21 , 22] . In this work we applied an auxin-induced protein degradation ( AID ) system and confirmed that the TTT pathway is critical for DNA damage checkpoint in budding yeast as well , providing a unified view that TTT-mediated control is conserved from yeast to humans . Depletion of Tel2 , Rvb2 and Asa1 caused nearly complete defects in damage-induced Rad53 phosphorylation although there were detectable levels of Mec1 and Tel1 proteins . One explanation could be that the TTT pathway not only stabilizes Mec1 and Tel1 protein but also facilitates interaction of Mec1 and Tel1 with other checkpoint proteins . It has been shown that Tel2 ( HCLK2 ) is required for efficient ATR-TopBP1 interaction and TopBP1-mediated ATR activation in human cells [27] . Supporting this view , previous studies have shown that low levels of Tti1 delocalize Tra1 and Mec1 outside of the nucleus [28] . The R2TP complex is found in organisms from yeast to humans; R2TP consists of Rvb1 , Rvb2 , Tah1 , and Pih1 in budding yeast [41] . Like Tel2 depletion , Rvb2 depletion had a high impact on Mec1 and Tel1 protein expression . The Rvb1-Rvb2 complex interacts with and regulates chromatin-modeling complexes; therefore , dysfunction affects transcription of numerous genes [50] . In humans , knockdown of Rvb1/RUVBL1 or Rvb2/RUVBL2 affects mRNA levels of PIKKs [56] . Rvb2 depletion was not found to affect mRNA levels of MEC1 and TEL1 , supporting the idea that Tel2 and Rvb1-Rvb2 constitute a pathway for protein stabilization in budding yeast . The observation that Rvb2 depletion causes defective Rad53 phosphorylation is consistent with a model in which the Rvb1-Rvb2 complex acts in the TTT-mediated PIKK stabilization pathway . However , the observed decreased Rad53 phosphorylation could result at least in part from defective DNA damage repair . Ino80- and Swr1-chromatin remodeling complexes , containing the Rvb1-Rvb2 complex , have been implicated in chromatin remodeling at sites of DNA damage or DNA damage checkpoint signaling in budding yeast [50 , 57] . Although our results show that the Rvb1-Rvb2 complex modulates the TTT-Asa1 pathway as well , the exact role of the Rvb1-Rvb2 complex in this pathway remains to be determined . Newly synthesized polypeptide chains must fold and assemble into specific three-dimensional structures in order to become fully functional . In many cases efficient folding depends on assistance from proteins known as molecular chaperones [58] . Several lines of evidence show that TTT acts as a co-chaperone for Hsp90 . Tah1 has been suggested to connect TTT to the Hsp90 chaperone [23 , 39] . At this moment it is not clear whether Asa1 collaborates with Hsp90 in budding yeast . Previous systematic approaches identified Pih1 and Tah1 as an Hsp90 interacting protein but did not pick Asa1 out [38] . Hsp90 may interact only transiently or weakly with the TTT-Asa1-Rvb1-Rvb2 complex in budding yeast although it is formally possible that the TTT-Asa1-Rvb1-Rvb2 complex acts independently of Hsp90 protein . Tel2 has been shown to recognize ATM and ATR in an Hsp90-dependent manner in human cells [22] . We found that Tel2 interacts with Mec1 and Tel1 in an Asa1-dependent manner . Asa1 might mediate Hsp90-chaperone functions in collaboration with the Rvb1-Rvb2 complex . Tel2 has been shown to interact with the N-terminal HEAT repeat region of ATM and mTOR in vitro [18] . Since the sequence similarity at the N-terminal region of PIKKs is relatively low compared with that at the C-terminal catalytic domain [59] , the TTT pathway is expected to process PIKKs with different efficiencies . We found that Asa1 depletion had a more significant impact on Tel2-Tel1 interaction than Tel2-Mec1 interaction . Since Mec1 and Tel1 do not share significant amino acid sequence similarities in the N-terminal region , TTT could interact with Mec1 and Tel1 with different affinities . However , Asa1 might make TTT a good fit for Mec1 and Tel1 . We have provided evidence indicating that Asa1 forms a complex with TTT , which is different from the TTT-R2TP complex in budding yeast . It thus seems likely that the TTT-Asa1 pathway operates separately from the Tel2-Pih1 pathway although we cannot exclude the possibility that these two pathways act redundantly . Pih1 appears to connect TTT to the Rvb1-Rvb2 complex in budding yeast [23 , 40] but Pih1 does not appear to exist in the TTT-Asa1-Rvb1-Rvb2 complex . Budding yeast may contain another yet-to-be identified protein that mediates interaction of TTT with the Rvb1-Rvb2 complex . Alternatively , the Rvb1-Rvb2 complex may exert a different mode in which it interacts directly with TTT . In mammals , Tel2-Pih1/PIH1D1 interaction does not fully contribute to protein stabilization of ATM and ATR . Therefore , TTT has been suggested to connect ATM and ATR to Hsp90 independently of PIH1D1 . Casein kinase 2 phosphorylates Tel2 in fission yeast as well [60] . Curiously , however , Tel2 phosphorylation is dispensable for TTT-mediated PIKK biogenesis [60] . Pih1 and Tah1 homologs have not been identified in the fission yeast Schizosaccharomyces pombe [60 , 61] . Thus , TTT appears to control protein stability of ATM and ATR family proteins through several different mechanisms in eukaryotes ( Fig 7D ) . Asa1 is conserved from yeast to humans; an Asa1 homolog has been identified in fission yeast as well [43] . It is interesting to see whether other eukaryotes utilize the Tel2-Asa1 pathway to regulate protein stability of ATM- and ATR-related protein kinases .
Strains carrying the improved AID system were generated as described [46] . To prepare the AID host strain , the tTA-TetR’-SSN6-OsTIR1 cassette ( designated as tetR’-SSN6 ) was integrated into a his3 strain isogenic to the KSC006 strain [62] by using pST1760 [46] . The HIS3 marker of tetR’-SSN6 was replaced with LEU2 by using pHL3 [63] . Cells were then backcrossed . The mini-AID tag was fused at the N-terminus or both N- and C-termini for the tel2-aid or asa1-aid construct , respectively . The full-length AID fragment was attached at the C-terminus to construct the rvb2-aid . All the AID construct promoters were replaced with the tetO cassette . The MEC1-FLAG or TEL1-FLAG construct ( YIp-MEC1-FLAG or YIp-TEL1-FLAG ) was converted from HA-MEC1 or HA-TEL1 construct [8 , 12] , respectively , by PCR , NgoMIV restriction digestion and re-ligation . The MEC1-FLAG or TEL1-FLAG construct was integrated into its own locus after digesting with PshAI or RsrII , respectively . MEC1-FLAG cells were as resistant to DNA damaging agents as wild-type cells ( S20 Fig ) . The telomere length of TEL1-FLAG cells was very similar to that of wild-type cells ( S21 Fig ) Gene disruption and C-terminal epitope tagged alleles were generated by PCR-based methods [64–66] . To express HA-tagged Rad53 protein , cells were transformed with YCp-RAD53-HA . The YCp-GAL-FLAG-MEC1 ( GAL-FLAG-MEC1 ) plasmid was generated from YCp-GAL-FLAG-TEL1 ( GAL-FLAG-TEL1 ) after replacing with a PCR-generated NgoMIV-SalI-FLAG-MEC1 fragment . The YCpT-RAD53-HA and the YCp-GAL-FLAG-TEL1 plasmid have been described [55] . The FLAG epitopes were fused to the N-terminus of MEC1 or TEL1 at the same location as YIp-MEC1-FLAG or YIp-TEL1-FLAG , respectively . All of the strains used in this study are listed in S1 Table . Oligonucleotides used for plasmid and strain construction are listed in S2 Table . To deplete AID-tagged protein , cells were treated with 250 μM 3-Indoleacetic acid ( IAA; SIGMA ) and 10 μg/ml doxycycline ( Dox; Enzo Life Science ) [45 , 46] . Galactose ( 2% ) medium contained glucose ( 0 . 5% for Mec1 and 0 . 3% for Tel1 ) to express FLAG-Mec1 or -Tel1 from the GAL1 promoter at the endogenous level , respectively . To monitor Rad53 phosphorylation , cells were incubated with nocodazole ( 15 μg/ml ) for 2 hr to synchronize at G2/M and then treated with 0 . 1% MMS for 30 min . Cells were cultured at 30°C unless specified . Cells were treated with 10 μg/ml cycloheximide to block translation [67] . Total RNA was extracted by hot acidic phenol method as described [68] . 20 μg of RNA was treated with 10 units of DNase I ( Clontech ) in the presence of 20 units of RNaseOUT ( Invitrogen ) at 37°C for 20 min . DNase I was inactivated by incubation with 25 mM EDTA at 80°C for 2 min . cDNA was synthesized using ProtoScript II First Strand cDNA Synthesis Kit ( New England BioLab ) according to the manufacturer’s instruction . Real-time PCR was performed as previously described [55] . PCR amplification from MEC1 and TEL1 transcript was normalized using that from ACT1 transcript . PCR primers are listed in S2 Table . Cellular fractionation , immunoprecipitation and immunoblotting were performed as described [55] . Anti-AID antibodies were generated after immunizing rabbits with a synthetic peptide ( DGAPYLRKIDLRMYK ) or obtained from Dr . Kanemaki . | We investigated the mechanisms underlying the stability of ATM/ATR-related protein kinase Mec1 and Tel1 , which control the DNA damage response in budding yeast . To this end , we applied genetic approaches in combination with a new version of the auxin-inducible degradation ( AID ) system . Our data are consistent with the model in which two separate pathways regulate protein stability of Mec1 and Tel1 , and contribute to proper DNA damage response in budding yeast . | [
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] | 2017 | Two separate pathways regulate protein stability of ATM/ATR-related protein kinases Mec1 and Tel1 in budding yeast |
Epithelial to mesenchymal transition ( EMT ) is an important event during development and cancer metastasis . There is limited understanding of the metabolic alterations that give rise to and take place during EMT . Dysregulation of signalling pathways that impact metabolism , including epidermal growth factor receptor ( EGFR ) , are however a hallmark of EMT and metastasis . In this study , we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M . We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial ( EGFR_E ) and mesenchymal ( EGFR_M ) networks . Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes . Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed . Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates . The signaling genes from the AKT , RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype . Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT . This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes , but this phenomenon is cell-specific and does not follow a simple trend .
Epithelial to mesenchymal transition ( EMT ) is a developmental process where polarized epithelial cells transition to an invasive mesenchymal-like phenotype through molecular reprogramming that leads to degradation of the extra-cellular matrix ( ECM ) and the loss of cell polarity . Following recruitment to specific sites at distant locations within the developing embryo , the mesenchymal cells may revert back to the epithelial phenotype by a process known as mesenchymal to epithelial transition ( MET ) , thereby seeding new epithelial tissues [1] . Although EMT is fundamental for several developmental processes and wound healing , dysregulation of EMT may cause cancer cells to initiate metastasis and form secondary tumors at distant sites [1–3] . EMT is induced by a number of distinct molecular processes [1] . These include the binding of several growth factors , including the platelet derived growth factor ( PDGF ) , insulin-like growth factor ( IGF ) , neuregulin and epidermal growth factor ( EGF ) to their cognate cell-surface receptors , leading to receptor activation [4] . This activates downstream signaling pathways that regulate the control of specific transcription factors , cell-surface proteins and microRNAs [5] . EMT is also involved in reorganization and expression of cytoskeletal proteins and production of ECM-degrading enzymes [1] . This series of events leads to increased expression of mesenchymal markers like N-cadherin and vimentin and decreased expression of epithelial markers such as E-cadherin [6] . Binding of EGF to its cognate epidermal growth factor receptor ( EGFR ) family has been shown to stimulate EMT in breast cancer cells [7 , 8] , leading to altered expression of E-cadherin and vimentin [8 , 9] . Activated EGFR signaling suppresses E-cadherin expression either by promoting its endocytosis [10] or by enhancing the expression of transcription factors ( TFs ) like Snail and Twist [11 , 12] . As a result , the cells may transition from epithelial to mesenchymal phenotype with spindle like morphology [8] . EGFR regulates mammary gland development and in certain aggressive breast cancer cells has been shown to regulate invasion and migration [8] . The most common signaling cascades activated downstream of EGFR are PI3K/Akt , Ras/Raf/Mek and DAG/IP3 and CaM signaling , that affect cell cycle progression , inhibition of apoptosis , angiogenesis , tumor cell motility , and metastases ( Fig 1 ) [13 , 14] . EMT is likely to impact metabolism , but the effects are not as widely studied as cancer metabolism [15 , 16] Cancer cells exhibit a shift of ATP generation from oxidative phosphorylation to aerobic glycolysis known as the Warburg effect [17] . This leads to a higher rate of glycolysis in cancer cells . Cancer cells also tend to show enhanced glutamine metabolism which has been shown to contribute cancer cell migration [18] . Signaling pathways have often been associated with metabolic consequences , but can themselves be influenced by metabolism . Interestingly , up-regulated glycolysis has been linked with higher AKT signaling in cancer cells [19 , 20] . However , the mechanistic manner in which metabolism is affected during EMT is unknown . Computational approaches such as Constraint-based modeling and analysis ( COBRA ) techniques are very useful in analysis of the complex biological networks like signaling networks [21–23] . Prior efforts of modeling of signaling cascades include modeling of the EGFR pathway [21 , 22 , 24–26] , TLR signaling [27–29] , JAK-STAT signaling [23] , MAPK pathway [30] and interleukin 1 signaling [31] . COBRA techniques mainly focus on the use of physio-chemical and biological constraints and are sparsely dependent on kinetic data that has limited availability . Protocols for the generation of biochemical networks and computational algorithms/methods for querying these networks are now well established [32 , 33] . They involve conversion of biological data ( e . g . genomic , metabolic , and regulatory ) to a mathematical reaction format . This allows better definition of regulatory changes associated with specific events such as EMT and exploration of metabolic alterations associated with the process . For example , how altered EGFR signaling is propagated to a metabolic phenotype can be investigated using COBRA methodology . In this study , we built a computational signaling network of EGFR to query how the expression of signaling genes can affect metabolic alterations during EMT in human breast epithelial cells . An EGFR signalling network was reconstructed ( EGFR_SN ) and was constrained with the transcriptomics data of the breast epithelial cell line D492 and its mesenchymal counterpart D492M [34] to form EGFR_E and EGFR_M networks . D492 is an E6/E7 viral oncogene immortalized human breast epithelial basal cell line with stem cell like properties that differentiates into both myoepithelial and luminal cells and has the ability to undergo branching morphogenesis when grown in a 3D reconstituted basement membrane matrix [35] . The 3D co-culture of D492 cells with human endothelial cells led to establishment of mesenchymal cells with spindle-like morphology called D492M . The D492M cells have high expression of N-cadherin and vimentin and low expression of E-cadherin that is typical for cells that have undergone EMT [34] . The EMT specific signaling network of D492 and D492M enabled us to investigate differential gene expression of EGFR signalling genes . This was subsequently extended to other breast epithelial cell lines and their mesenchymal counterparts . Through extensive literature review , the EGFR_SN network was linked to metabolic genes that were likely to be affected . The differential flux values in EGFR_E and EGFR_M allowed the assessment of how the altered signaling affects metabolic gene expression and metabolism in D492 and D492M cells . Increased flux within the AKT and RAS/MAPK signaling pathways was predicted in D492 as compared to D492M . The in silico predicted increase in flux of the AKT pathway induced higher glycolytic activity in D492 cells . This suggested that there may be an EMT-related decrease in glycolysis in D492M as compared to D492 cells , which was confirmed in vitro by glucose uptake and lactate secretion measurements . Comparative analysis of EGFR signaling networks in three other breast epithelial cell lines showed that regulation of signaling pathways are cell specific and follow no simple trend .
In order to capture how altered EGFR signaling is propagated through metabolic pathways in the breast epithelium , we built a constraint based EGFR network , EGFR_SN . To generate EGFR_SN , the EGFR pathway map ( Reactome ID: R-HSA-177929 ) was downloaded from the Reactome database [36] . Several modifications such as incorporation of gene-protein reaction ( GPRs ) rules/association , additions of modifiers , inhibitors and activators and removal of dead ends were made to the Reactome pathway to make it feasible for analysis with the COBRA methodology ( methods section ) . Due to the incorporated GPRs , experimental data ( e . g , gene expression , proteomic , fluxomic data ) can now be mapped onto EGFR_SN , thereby relating genomic information to the reactions in the network . The resulting reconstructed network EGFR_SN accounts for 182 reactions , 216 genes , 152 reacting species , 11 inhibitors and 2 activators . The 182 reactions were divided between 83 internal reactions and 99 exchange reactions . Exchange reactions were added in order to remove dead-ends in the network and acquire feasibility . The number of exchange reactions added here is large compared to what is typically present in metabolic networks , where exchange reactions are added mainly to allow for accumulation of metabolites and secretion of wastes . Internal reactions represent connections between internal signaling components , while the exchange reactions represent connections of the system boundary with the environment . Fig 2 shows a sub-network of EGFR_SN representing AKT signaling . Microarray gene expression profiles of the human breast epithelial cell line D492 and the mesenchymal like D492M were used to constrain the EGFR_SN network to build an EMT specific signaling model . D492 can be used as a model for studying EMT for which biological data is available: mRNA , micro-RNA , cell phenotypic data , growth curves etc . [34 , 37] . D492 has previously been used in determining the role of microRNAs in EMT [37] , studies related to branching morphogenesis [38] and more recently it was used to investigate the role of EGFR as a tumor suppressor [39] . To derive epithelial and mesenchymal specific signaling networks , gene expression data from D492 and D492M was mapped onto the EGFR_SN network . The pipeline is described in Fig 3 . Differentially regulated ( up-regulated and down-regulated ) genes in the two cell lines led to two different signaling networks: EGFR_E and EGFR_M for D492 and D492M respectively , as described in the methods section . We next studied how the flux in the EGFR_M network could be modified so that it became similar to the EGFR_E flux , in order to identify how the EMT process could be reversed . This was achieved by using an optimization algorithm similar to the MOMA algorithm which is frequently used to study perturbations in metabolic networks [41] . The algorithm searches for a flux distribution in EGFR_M , which most closely resembles the average flux values obtained previously for the EGFR_E model with random sampling , with minimum relaxation in the reaction bounds in EGFR_M while maintaining steady state conditions , see the methods section for more details . The algorithm highlighted five reactions whose bounds needed to be relaxed such that the flux distribution of EGFR_M is close to that of EGFR_E . These included three internal reactions: 1 ) phosphorylation of the MAP2K dimer by RAF , 2 ) phosphorylation of MAPKs by MAP2Ks ( RAF/MAPK pathway ) , 3 ) PIP2 conversion to PIP3 by PIK3 ( AKT pathway ) , and two exchange reactions , belonging to the RAF/MAPK pathway . Through GPRs we identified that there are 22 genes associated with the reactions predicted above ( S1 Table ) . However , among these 22 genes , MAPK1 , NRAS , HRAS and EGFR genes were overexpressed in D492 as compared to D492M and the inhibitor PTEN was overexpressed in D492M . Based on these predictions and by analysing the flux differences between EGFR_E and EGFR_M , we hypothesize that increased AKT and RAS/MAPK signaling in D492 epithelial cells promotes their transition to the D492M mesenchymal like phenotype . However , after attainment of a mesenchymal state , AKT and RAS/MAPK signaling is reduced and alternative pathways such as CaM signaling gets activated which may be involved in the maintenance of the mesenchymal state . We also hypothesize that up-regulation of MAPK1 , NRAS , HRAS and EGFR and down-regulation of PTEN inhibitor in EGFR_M may lead to its transformation into EGFR_E . Interestingly , most of the in silico predicted targets from our study have previously been implicated to play a role in EMT . The activation of MAPK1 protein has been shown to induce EMT in MCF10 breast epithelial cells by phosphorylation and consequent stabilization of Twist1 [42] . In another study , the silencing of MAPK1 led to increased expression of E-cadherin and a decrease in vimentin and Snail expression in human cervical cancer cells [43] , suggesting its role in the transition from epithelial to mesenchymal phenotype . HRAS and Slug together have been shown to induce the expression of vimentin and enhance cell migration in pre-malignant MCF10A breast epithelial cells [43] . Further , activation of EGFR has been shown to induce the expression of Twist by activating STAT3 , suggesting a prominent role of EGFR in EMT [11] . In a recent study where mesenchymal cells were generated from D492 by overexpressing HER2 ( ErBb2 ) [39] , subsequent overexpression of EGFR promoted mesenchymal to epithelial transition . In light of this finding , and based on our in silico predictions , we made EGFR overexpressing D492M cell line ( D492MEGFR ) ( method section ) . D492MEGFR cells showed higher phosphorylation of AKT and ERK1/2 ( Fig 5 ) , although reversal of mesenchymal to epithelial phenotype was not observed ( S1 Fig ) . Thus , we hypothesize that for complete reversal of mesenchymal to epithelial phenotype , the MAPK1 , NRAS and HRAS genes may need to be overexpressed in addition to EGFR , along with PTEN inhibition in D492M . Likewise , maintaining expression of the MAPK1 , NRAS , HRAS and EGFR in epithelial cells while suppressing the expression of PTEN is expected to inhibit EMT . At present , a number of EGFR inhibitors have been approved by the FDA for cancer treatment . These include Cetuximab and Panitumumab that are monoclonal antibodies against EGFR , and Erlotinib , Gefitinib and Lapatinib which are specific tyrosine kinase inhibitors against EGFR . All these compounds are prescribed against advanced , late-stage or metastatic cancers [44 , 45] . The findings we present here suggest that inhibition of EGFR may contribute to or increase the risk of EMT in cancers of epithelial origin . Indeed , the overexpression of HER2 in D492 cells has recently been shown to suppress EGFR expression and induce EMT [39] . Our predictions also indicate that PTEN inhibition can help maintain the epithelial phenotype , thereby preventing EMT and metastasis . However , recent findings suggest that loss of PTEN function may promote tumor progression in a mouse model [46] and EMT in human colon cancer cells [47] , highlighting the fact that further research on the role of PTEN in EMT is needed . Taken together , these above results demonstrate the EGFR_SN network can be used to study in detail the differences in EGFR signaling between epithelial and mesenchymal cells during EMT and identify gene targets which could possibly be used to hinder or even revert EMT . We next studied how changes in AKT signaling could influence metabolic gene expression in EMT . The AKT pathway was chosen since it has previously been shown to affect various metabolic pathways , e . g . glycolysis and fatty acid metabolism in cancer cells [19 , 48] . To reflect how alterations in the AKT signaling pathway may affect metabolism , a literature based survey was conducted to identify the connections between this pathway and its target metabolic genes . This suggested that active AKT signaling induces the expression of metabolic genes involved in glycolysis , fatty acid metabolism and purine and pyrimidine metabolism , while it suppresses the expression of metabolic genes involved in gluconeogenesis ( Table 1 ) . The ratio of average flux in each signaling reaction of the AKT pathways in the EGFR_M and EGFR_E network was used to identify differences in relative metabolic gene expression in the D492 and D492M cells . The higher flux observed in the AKT pathway in the EGFR_E network suggested increased expression of genes belonging to glycolysis , fatty acid and purine/pyrimidine metabolism in D492 in comparison to D492M ( Table 1 ) . Higher expression of glycolytic genes suggested higher glycolytic activity in the D492 cells . To test these in silico predictions , we compared the predicted expression of the metabolic genes with their corresponding relative expression values in the microarray data set [34] and compared relative expression of metabolic genes in D492 and D492M . Up- and down-regulated metabolic genes were identified based on differential expression and significance measurements , as analyzed by SAM [49] , included in the microarray dataset . A cut-off of 0 . 05 on the significance measure was used . The relative gene expression of 13 out of 15 metabolic genes ( 86 . 6% ) affected by AKT signaling were in agreement with the predicted in silico expression ( Table 1 ) . Since the experimental expression values of the metabolic genes were not used to generate the EGFR_E and EGFR_M networks , this data corresponds to an independent validation set for the in silico predictions . However , the gene expression of 2 out of 15 ( 13 . 3% ) of the metabolic genes ( GLUT1 and SREBF1 ) from the microarray data and in silico predictions were not in agreement . This suggests an alternate level of regulation that metabolic genes may encounter during EMT , in addition to the direct regulation by molecular signaling pathways . For accuracy measure we did not take into account metabolic genes which did not have any detectable expression values ( denoted NA ) . Ultimately , we observed that the expression of most of the metabolic genes directly affected by AKT signaling during EMT was correctly predicted in our D492 model system . Gene expression measurements of metabolic genes are not necessarily a quantitative predictor of their metabolic activity . We therefore tested our in silico prediction that glycolytic activity is higher in D492 than in D492M by measuring the proliferation rates and glucose consumption and lactate secretion rate to estimate glycolytic activity in vitro . Cells with a higher rate of proliferation may have higher nutrient and energy requirements and consequently greater metabolic demand [59] . Cell proliferation assays showed that the D492 cells had a higher growth rate than D492M ( Fig 6A ) . Spent medium of D492 and D492M cells was analyzed to measure glucose and lactate levels . Higher glucose levels and lower lactate levels in cultured supernatants of the D492M cells indicated a lower rate of glucose consumption and lactate secretion rates in D492M cells ( Fig 6B ) . This indicates a lower glycolytic rate in D492M cells compared to D492 and suggests a shift in the glycolytic capacity of the cells in response to EMT and is in agreement with the in silico predictions that indicated higher gene expression of glycolytic enzymes in D492 cells . We conclude that this EMT related decrease in aerobic glycolysis appears to be driven by an overall decrease in the expression of glycolytic enzymes due to down-regulated AKT signaling in D492M cells . To the best of our knowledge , this is the first report that demonstrates that in silico network predictions can be used to study the influence of a molecular signaling pathways , such as AKT , on the metabolic outcome during EMT in breast epithelial cells . Our findings are in agreement with a previous study on human non-small cell lung carcinoma ( NSCLC ) cells [60] which demonstrated a decrease in aerobic glycolysis during EMT but are in contrast to results obtained for MCF7 breast epithelial cells that have undergone EMT [61] . Results from the previous section showed that the metabolic phenotype could be accurately predicted by in silico analyses of the changes in the expression of AKT signaling genes . Further , we investigated how these changes in AKT signaling , that impact the expression of metabolic genes ( Table 1 ) , are propagated through other metabolic pathways during EMT . Based on RECON2 [62] , we have also built an EMT metabolic network ( MODEL1602080000 ) . This metabolic network was built by constraining RECON2 with microarray gene expression data [34] of metabolic genes during EMT in D492 and D492M . RECON2 is a global human metabolic reconstruction that has been used previously to investigate regulation of metabolism in diseases like obesity and diabetes [40 , 62] . In this study , we constrained our EMT metabolic network with the metabolic genes that were predicted to be regulated by changes in AKT signaling ( Table 1 ) . This led to the formation of epithelial metabolic ( Met_E ) and mesenchymal metabolic ( Met_M ) networks , specific for AKT signaling regulated metabolism during EMT ( described in methods section ) . Metabolic differences between the Met_E and Met_M models were identified based on differences in their relative flux span ( methods section ) . Reactions carrying higher flux in Met_E compared to Met_M included reactions that are involved in N-glycan metabolism , Glycolysis , Fatty acid synthesis , Fatty acid oxidation , nucleotide interconversion and pentose phosphate pathway . Reactions carrying higher flux in Met_M involved Glutathione , glycerophospholipid , and inositol phosphate metabolism . Alteration of metabolic pathways , including N-glycan , glutathione metabolism , glycolysis , fatty acid and purine metabolism that we observed from our constructed Met_E and Met_M metabolic networks , have been shown to play an important role in the regulation of EMT [19 , 48 , 57 , 60 , 63 , 64] . However , the details of the mechanism are still unknown . A list of the metabolic reactions similarly predicted to be affected by the AKT pathway in the Met_E and Met_M networks are provided in the supplementary file ( S2 File ) . In conclusion , this method was able to predict metabolic pathways that may be affected downstream upon activation of AKT signaling in breast epithelial cells during EMT . This method extends the approach of associating metabolic phenotype with regulation of signaling pathways . Further , it also suggests the possibility of determining the metabolic regulation in cases that are limited by the availability of metabolomic data or the gene expression data of metabolic genes . Although these predictions are context specific in relation to AKT signaling , the integration of other signaling pathways affected during EMT may give a more coherent picture of altered metabolism . We next studied whether a general trend of higher flux in the reactions of the AKT and RAS pathways ( downstream of EGFR ) was observed in other human breast epithelial cell lines , when compared to their mesenchymal counterparts . We mapped the microarray transcriptomic datasets for the three human breast epithelial cell lines ( HMLE , MCF-7 and MCF-10A ) onto the EGFR_SN network similar to the method used for the D492 cells ( methods section ) . Comparisons between cell lines were done in terms of the ratio between flux in the mesenchymal network vs flux in the corresponding epithelial network ( Fig 7 ) . Numerical values of the fluxes within AKT , RAS and CaM pathways in HMLE , MCF-7 and MCF-10A are given in supplementary file ( S3 File ) . Higher flux through the reactions in the AKT pathway was observed in both the D492 and HMLE epithelial cells , suggesting that the HMLE cells may have similar metabolic phenotype as D492 ( Fig 7 ) . In contrast , the mesenchymal counterparts of MCF7 and MCF10A cells had higher flux in the AKT pathway ( Fig 7 ) , suggesting that cells that have undergone EMT may have increased glycolytic activity . This is in agreement with Kondaveeti et al . who have reported an increase in glycolytic activity post-EMT in MCF-7 cells as a result of increased expression of glucose transporters and lactate dehydrogenase [61] . The flux in the RAS/MAPK pathway was higher in the D492 and the MCF7 epithelial cells than in their mesenchymal counterparts . Analysis of DAG/IP3 and CaM pathway showed that D492M , MCF7 mesenchymal cells and those of HMLE which have undergone EMT due to induction of Twist , had higher flux as compared to their epithelial counterpart ( Fig 7 ) . Thus , no general pattern was observed in the flux distributions between the epithelial and mesenchymal networks for the different breast epithelial cell lines . Induction of EMT by different factors ( viral induction of SNAIL , SLUG , TWIST , miR374a or TGFβ1 treatment ) also seemed to differentially regulate signaling pathways as was evident in the MCF10A and HMLE cells . For example , the induction of EMT in HMLE cells by overexpression of Slug and Twist resulted in different flux patterns in the RAS/MAPK and DAG/IP3 and CaM pathways ( Fig 7 ) . Similar effect was seen by Deshiere et al . , where they have shown that TGFβ1 treatment and CK2b silencing activate divergent signaling pathways , that ultimately lead to EMT in MCF-10A cells [65] . Finally , we compared the predicted metabolic phenotypes to the metabolic gene expression data for the MCF7 , MCF10A and HMLE cell lines similar to the method used for the D492 cells , however the gene expression data of metabolic genes were not statistically significant and hence was not included in our study ( S1–S4 Tables ) . In summary , we observed that different cell lines may affect different signaling regulation during EMT . Moreover , variation in the methods of EMT induction may dictate differential regulation of the signaling and metabolic cross talk . Herein we have demonstrated a method to build a stoichiometric model of the EGFR signaling ( EGFR_SN ) network employing COBRA methods that aids in understanding the differential activation of downstream EGFR signaling pathways during EMT . Epithelial and mesenchymal specific EGFR signaling networks were obtained by integrating microarray transcriptomics data of signaling genes from the D492 breast epithelial and mesenchymal cells with EGFR_SN . The epithelial and mesenchymal networks were used to predict the expression of metabolic genes . The predicted expression values were in agreement with transcriptomics data of metabolic genes as well as biochemical data that demonstrated higher glycolytic activity in D492 epithelial cells . Furthermore , signaling genes leading to reversion to the epithelial phenotype ( MET ) via up- regulation in the mesenchymal cells were predicted . Additional in vitro testing would be required to confirm these in silico predictions . Thus , in this study we showed that the metabolic phenotype can be predicted in silico using gene expression profiles of EGFR signaling components . EGFR_SN is not limited in scope to the investigation of EMT . The signalling network could be used to highlight signaling-metabolic crosstalk in different cell types for which metabolic reconstructions exist [66] or for different conditions [66] where EGFR signalling is known to be influential [67] . Furthermore , the network described herein could be expanded to allow for more comprehensive coverage of signalling pathways of relevance to EMT . Signaling networks for platelet derived growth factor ( PDGF ) , insulin like growth factor ( IGF ) , and vascular endothelial growth factor ( VEGF ) for example , could be constructed and co-integrated . The co-integration of these signaling networks regulated by different growth factors would give more comprehensive knowledge of cross talk between signaling and metabolic pathways during EMT . The pipeline developed for D492 was used on other cell models representative of breast epithelial cell lines . We assumed that different breast epithelial cells models would have similar signaling patterns and hence could have a general interpretation of regulation of signaling pathways during EMT . In contrast to our hypothesis , the regulation of signaling pathways showed no general pattern and appeared to be a cell-specific phenomenon . Flux values in the AKT pathway from our network , suggest that there may be an EMT related decrease in aerobic glycolysis in both the HMLE and D492 cell lines , while the opposite was observed in the MCF-7 and MCF-10A breast epithelial cells . There are several possible explanations for this disparity . First , the highly complex nature of the regulation of EMT , which may be differentially regulated by the cellular micro environment or EMT inducing factors as seen in our in silico predictions . Second , our study of EMT signal transduction is primarily based on transcriptional signatures which may or may not necessarily correlate with the translational output ( protein-levels ) [68 , 69] . This last issue might be addressed by co-integrating transcriptomics and proteomics/phospho-proteomics data in order to obtain more accurate models . Such a strategy was recently reported where it was used to reconstruct a metabolic network for predicting of metabolic signatures in diabetes patients [70] . The disparity between cell models suggests considerable heterogeneity of the cell models used for EMT research in general . Finally , these efforts highlight a lack of comprehensive datasets available that accurately describe EMT and ultimately hinder mechanistic understanding of the genotype phenotype relationship underlying EMT . The direct link between regulation of signaling pathways and the consequent metabolic phenotype may be of clinical interest , as metabolically based therapeutics to combat cancer EMT could be masked by inaccurate metabolic understanding .
The EGFR pathway network was downloaded from the Reactome database which is curated and peer reviewed [71] . The EGFR pathway was then converted from SBML to COBRA format for further analyses . This conversion is based on the stoichiometric coefficients of the reacting species provided in the Reactome pathway and also requires setting constraints on each reaction in the form of lower and upper bounds , which determine the minimum and maximum allowable reaction rates ( fluxes ) , respectively [33] . Flux in a signaling network is defined as the rates of phosphorylation , de-phosphorylation , dimerization , or binding of proteins . The COBRA model was represented by an m by r stoichiometric matrix S , where m denotes the number of network components ( metabolites , proteins , and complexes ) and r the number of network reactions . Reactions within the network were mass-balanced . The system was assumed to be at steady state , which means that the fluxes v = ( v1 , … , vm ) satisfy the equations Sv = 0 . The upper and lower bounds of all the internal reactions were set to 1000 and zero , respectively . A lower bound of zero was used since all the reactions are irreversible . The initial COBRA model contained many dead ends and was infeasible due to network gaps [72] . Dead-ends represents those reacting species which are either only produced or only consumed in the network , leading to blocked reactions , i . e . reactions unable to carry flux . To remove the dead ends and obtain a feasible model , exchange reactions were introduced allowing uptake and secretion of components across the system boundary . By adding exchange reaction for all reacting species , a feasible model was obtained . In this model , the network topology becomes irrelevant since all demands on the network can be met by the exchange reactions . To avoid this situation , an optimization algorithm , ‘relax_rxns’ ( S1 Dataset ) was developed that enabled a feasible steady state network , while minimizing uptake/secretion ( exchange ) of the dead-end molecules . The methodology is similar to the one used by Vardi et al . [21] . First , the lower bounds of all the internal reactions were set to 1 to force removal of blocked reactions ( reactions with zero flux ) and consequently removal of the dead-end species . Exchange reactions were then added for every reacting species in the network , initially with all uptake and secretion rates set to zero . The optimization algorithm returns a minimal set of exchange reactions that need to be present in order to remove all dead ends . These reactions were included in the final model , but the other exchange reactions were removed . The optimization problem was formulated as follows: minimize∑j∈Rryj ( 1 ) Sv=0 ( 2 ) lj-nj≤vj≤uj+pjj∈Rr ( 3 ) li≤vi≤uii∈Rn ( 4 ) pj≤Myj , nj≤Myjj∈Rr ( 5 ) pj≥0 , nj≥0j∈Rr ( 6 ) yj∈{0 , 1}j∈Rr ( 7 ) The decision variables are v , the flux values in individual reactions , pj and nj which represent the amount of relaxation of upper and lower bounds for reaction j , respectively and binary variables yj which indicate whether reaction j is relaxed or not . The objective is to minimize the number of reactions that are relaxed ( 1 ) . The steady state mass balance constraints are represented by ( 2 ) , Rr is the set of reactions that are to be relaxed ( 3 ) with the corresponding upper and lower bounds set to zero . The set Rn represents all the remaining reactions ( 4 ) with the corresponding upper bounds set to 1000 and lower bounds set to 1 . The value of the constant M in ( 5 ) was set to 1000 . The optimization model was implemented in Matlab ( Mathworks , Natick , MA , USA ) using the CVX modeling language [73 , 74] and solved using the Gurobi solver [75] . The version of CVX used in this study supports binary variables as those in constraint ( 7 ) . The signaling network from section 4 . 1 was extended to include modifiers , activators and inhibitors . Modifiers are phosphorylated protein entities that further phosphorylate downstream targets . In the original Reactome pathway , the modifiers were not included as reacting species in reactions , such that they were not connected to downstream targets . For this reason modifiers were included as the reacting species in their target reactions . We included the modifiers in the same way as described by Dasika et al . [22] . Briefly , modifier mod1 acts as a modifier for the transition of A to B . In order to avoid unambiguous stoichiometry , we added mod1p , as a product of mod1 during the reaction as shown below . Activators and inhibitors are responsible for positive and negative regulation of the reaction , respectively . Activators and inhibitors were included in the model via gene-protein reaction rules as described in the next section . The original Reactome signaling network described the transmission of signal , in terms of activation or inhibition of the subsequent downstream entities . This did not include any enzymatic reactions or the gene-protein rules required to map gene expression data [33] . The signaling network was therefore modified to include GPRs by associating each network reaction with genes encoding modifiers , activators and inhibitors . Reactions not having any information of modifiers , activators or inhibitors were not designated with GPRs . For the GPR generation , we employed UniProt IDs of the protein entities within the Reactome pathway to identify genes , which were then associated with reactions using Boolean logic . Multiprotein complexes were represented with an ‘AND’ operator , while isoforms were represented by an ‘OR’ operator to create a corresponding Boolean rule . Wherever an inhibitor was involved , the corresponding genes were prefixed by a ‘NOT’ operator in the GPR . Exchange reactions were also assigned GPRs . If a reaction is catalysed by a modifier and inhibited by an inhibitor , the presence of modifier will activate the reaction , while an inhibitor will inhibit the reaction . GPRs for all the reactions are provided in the S1 File in the sheet named “GPRs” . The blank rows denote that they were not assigned any GPRs . Fig 8 below , illustrates examples of GPR generation . The resulting network is referred to as EGFR_SN . A spreadsheet containing all the network reactions , reacting species , modifiers , inhibitor , activators , and GPRs in different sheets is provided in the supplementary file ( S1 File ) . All the models used in this study are provided in the supplementary dataset ( S1 Dataset ) . Gene expression data for the D492 and D492M cell lines was obtained from Sigurdsson et al [34] . The microarray expression data for the HMLE , MCF-7 and MCF-10A cell lines was obtained from NCBI GEO [76] , GEO IDs: GSE52593 [77] , GSE43495 [78] , GSE58252 [79] , GSE39358 [80] , and GSE28569 [65] . Illumina/Affimetrix IDs within the microarray data were mapped with the Uniprot IDs of the genes in the EGFR_SN network using the Python API of bioDBnet , biological DataBase network . Mapped gene expression of the EGFR signaling genes for each cell line is provided in the supplementary material S4 File with sheets named after each cell line . Negative values indicate higher gene expression in epithelial cells and positive values indicate higher gene expression in mesenchymal cells . Integration of expression data with the EGFR_SN network and subsequent analysis was performed in Matlab . The cut-off value on the relative fold change between epithelial and mesenchymal cell lines , to determine up- and down-regulated genes , depended on the relative expression of housekeeping genes and the statistical significance ( p-value ≤ 0 . 05 ) of the fold change . Accordingly , a cut-off of ≥ 2 was considered for D492 cells , 0 . 5 for MCF7 and MCF10A cells and 0 . 3 for HMLE cells . Since GPRs link genes with reactions , up- and down-regulated genes identified consequently up-regulated and down-regulated reactions . The change in the expression value of each gene in the EGFR_SN signaling network was used to define the upper and lower flux bounds of its associated reactions , such that up-regulated reactions were allowed to have higher flux values and down-regulated reactions were allowed to have lower flux values . Since , the upper bounds on individual fluxes are essentially infinite , up-regulation in an epithelial model was simulated by downregulating the corresponding reaction in its mesenchymal counterpart , and vice versa . Flux bounds of the up-regulated reactions in D492 were constrained by an arbitrary factor one-hundredth of the initial bounds in D492M and vice versa . Similarly , flux bounds of the activated and inhibited reactions were constrained . Activation in the epithelial model was simulated by downregulating the corresponding reaction in its mesenchymal counterpart and vice versa , while inhibition was simulated by downregulating the corresponding reaction in the same model . This led to the formation of EGFR_E and EGFR_M networks for D492 and D492M respectively . Random sampling was used to obtain flux distributions in the networks [81] using the COBRA toolbox [32] , technical details of which can be found in Supplementary methods ( S1 Methods ) . Flux differences in individual reactions in the mesenchymal and epithelial networks were quantified in terms of fold changes , vM ( i ) / vE ( i ) where vM ( i ) and vE ( i ) represent the average flux in reaction i for the mesenchymal and epithelial networks , respectively . Reactions which had vM ( i ) / vE ( i ) greater than 1 carried higher flux in the mesenchymal network . A ratio below 1 indicates higher flux in the epithelial network . A literature based survey provided evidence of whether a metabolic gene is positively or negatively regulated by AKT signaling ( S5 Table ) . The prediction of the metabolic gene expression derives from whether EGFR_E or EGFR_M have higher flux in AKT signaling . The optimization algorithm of section 4 . 1 was modified by replacing the objective function ( 1 ) by minimizeα‖v–vE‖+ ( 1-α ) ∑j∈Rryj ( 1a ) where v and yj represents the decision variables as before , vE are fixed values representing the mean flux distribution of EGFR_E , obtained from random sampling and || . || represents the Euclidean norm . The upper and lower bounds for each flux correspond to the values from the EGFR_M network . The algorithm returns a set of reactions in EGFR_M whose bounds can be relaxed in order to obtain a flux distribution that resembles that of EGFR_E . We constrained RECON 2 [62] using the microarray data from Sigurdsson et al . [34] to generate an EMT metabolic network ( submitted to Biomodels: MODEL1602080000 ) . This reconstruction consists of all the metabolic reactions encoded in both the D492 and D492M cell lines . This EMT metabolic network has information on GPRs connecting each reaction with the genes of the enzymes catalyzing the reaction . Since GPRs associate genes with corresponding reactions , the metabolic genes predicted to be up-regulated in epithelial cells due to AKT signaling ( Table 1 ) led to the identification of up-regulated reactions in D492 and similarly were determined in D492M . This information was then used to define the upper and lower flux bounds of the affected reactions in the EMT metabolic network to form Met_E ( metabolic epithelial ) and Met_M ( metabolic mesenchymal ) networks ( Fig 9 ) . Up- and down-regulation of the Met_E and Met_M models was simulated as described in section 4 . 4 . Specifically , up-regulation in an epithelial metabolic model was simulated by downregulating the corresponding reaction in its mesenchymal counterpart , and vice versa . Flux bounds of the up-regulated metabolic reactions due to AKT signaling in D492 were constrained by an arbitrary factor , one-hundredth of the initial bounds in D492M and vice versa . The flux values through each reaction in both the models were determined through random sampling method and the relative flux span , sM ( i ) / sE ( i ) was used to quantify the flux differences between the networks . Here , sM ( i ) and sE ( i ) represent the average flux in reaction i for the mesenchymal and epithelial cells , respectively . For proliferation assays and spent medium analysis , 1 . 5 × 104 D492 or D492M cells were cultured in 48-well plates ( Costar ) in 200μL H14 medium as previously described [35] . Spent medium was collected and cells were fixed in ice-cold methanol at 0 h , 24 h , 48 h and 72 h . To estimate proliferation , cells were stained with crystal violet , washed thoroughly with water , dissolved in 30% acetic acid and read in a spectrophotometer at 570 nm . The observed growth rates were 0 . 0276 h−1 for D492 and 0 . 0161 h−1 for D492M . The glucose and lactate concentrations in the spent medium were measured in an ABL90 blood gas analyser ( Radiometer , Brønshøj , Denmark ) . Glucose uptake and lactate secretion per cell were calculated for each cell line as described in [82] , based on the ABL90 measurements and growth rates . Vectors used for viral production were acquired from Addgene , pBABE-EGFR and empty backbone ( #11011 , #1764 , respectively ) and were used as provided . Phoenix HEK293 cells were used for retroviral ( EGFR ) virus production , using Arrestin transfection ( Life Technologies ) . D492M cells were transduced overnight with viral supernatant containing 8 μg/ml Polybrene ( Sigma-Aldrich ) . EGFR and empty backbone cells were selected using 2 μg/ml puromycin ( Life Technologies ) . Total RNA was isolated using TRI-Reagent solution ( Ambion ) and reverse transcribed using SuperScript IV ( Invitrogen ) . The resulting cDNA was used for Real-Time Quantitative Reverse Transcription PCR , in Maxima Probe/ROX qPCR Master Mix ( Thermo Scientific ) with primer pairs and probes for EGFR ( Hs00540086_m1 , Life Technologies ) , ZEB1 ( Hs00232783_m1 , Life Technologies ) and GAPDH ( Hs99999905_m1 , Life Technologies ) . Experiments were done in triplicates on 7500 Real Time PCR System ( Applied Biosystems ) . EGFR mRNA levels were normalized to GAPDH and relative mRNA differences were calculated using the 2ΔCt method . Proteins were isolated using RIPA lysis buffer supplemented with protease and phosphatase inhibitors ( Life Technologies ) . For Western blot analysis 5 μg of protein lysates were loaded per lane on NuPage 10% Bis-Tris gels ( Life Technologies ) in 2- ( N-morpholino ) ethanesulfonic acid ( MES ) running buffer ( Life Technologies ) . Samples were denatured using 10% mercaptoethanol at 95°C for 10 minutes before loading . Samples were transferred to Immobilon FL PVDF membranes ( Millipore ) and blocked in Li-cor blocking buffer for 1 hour . Primary antibodies were incubated overnight at 4°C and secondary IRDye antibodies were incubated at room temperature for 1 hour ( Licor ) . The following primary antibodies were used for Western blotting: Actin antibody ( Abcam , ab3280 ) , EGF Receptor ( Cell Signaling , CS#4267 ) , Phospho-Akt ( Ser473 ) ( Cell Signaling , CS#4060 ) , Phospho-p44/42 MAPK ( Erk1/2 ) ( Thr202/Tyr204 ) ( Cell Signaling , CS#4370 ) , N-Cadherin ( BD Biosciences , 610921 ) , E-Cadherin ( BD Biosciences , 610182 ) and Cytokeratin 14 ( Abcam , ab15461 ) . Near-infrared fluorescence visualization was measured using Odyssey CLx scanner ( Li-Cor , Cambridge , UK ) . | The epidermal growth factor receptor ( EGFR ) signaling cascade is one of the key signaling pathways that are involved in the induction of Epithelial Mesenchymal Transition ( EMT ) and tumor metastasis . These signaling cascades often affect metabolic fate in tumor cells and control their progression . Here we demonstrate a method to build a mathematical model of the EGFR signaling cascade and use it to study signaling in EMT and how signaling affects metabolism . The model was used to obtain a list of potential signaling and metabolic targets of EMT . These targets may aid in the understanding of the molecular mechanisms that underlie EMT and metastasis . Our results further highlight the heterogeneity of cell models used to study EMT and support the idea of cell specific anti-cancer interventions . | [
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] | 2016 | EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT |
Parasitic nematodes of humans and livestock cause extensive disease and economic loss worldwide . Many parasitic nematodes infect hosts as third-stage larvae , called iL3s . iL3s vary in their infection route: some infect by skin penetration , others by passive ingestion . Skin-penetrating iL3s actively search for hosts using host-emitted olfactory cues , but the extent to which passively ingested iL3s respond to olfactory cues was largely unknown . Here , we examined the olfactory behaviors of the passively ingested murine gastrointestinal parasite Heligmosomoides polygyrus . H . polygyrus iL3s were thought to reside primarily on mouse feces , and infect when mice consume feces containing iL3s . However , iL3s can also adhere to mouse fur and infect orally during grooming . Here , we show that H . polygyrus iL3s are highly active and show robust attraction to host feces . Despite their attraction to feces , many iL3s migrate off feces to engage in environmental navigation . In addition , H . polygyrus iL3s are attracted to mammalian skin odorants , suggesting that they migrate toward hosts . The olfactory preferences of H . polygyrus are flexible: some odorants are repulsive for iL3s maintained on feces but attractive for iL3s maintained off feces . Experience-dependent modulation of olfactory behavior occurs over the course of days and is mediated by environmental carbon dioxide ( CO2 ) levels . Similar experience-dependent olfactory plasticity occurs in the passively ingested ruminant-parasitic nematode Haemonchus contortus , a major veterinary parasite . Our results suggest that passively ingested iL3s migrate off their original fecal source and actively navigate toward hosts or new host fecal sources using olfactory cues . Olfactory plasticity may be a mechanism that enables iL3s to switch from dispersal behavior to host-seeking behavior . Together , our results demonstrate that passively ingested nematodes do not remain inactive waiting to be swallowed , but rather display complex sensory-driven behaviors to position themselves for host ingestion . Disrupting these behaviors may be a new avenue for preventing infections .
Passively ingested gastrointestinal parasitic nematodes of humans and livestock are a significant health and economic problem . Human-infective nodular worms in the genus Oesophagostomum are a growing health concern in endemic regions of Africa , where they can cause abdominal pain , weight loss , diarrhea , and death [1–3] . Passively ingested parasites of livestock result in decreased production and economic loss worldwide . For example , Haemonchus contortus is an important parasite of ruminants that causes gastrointestinal distress , anemia , edema , and death in livestock [4] . In the United States alone , over 2 . 7 million goats and 2 . 6 million sheep are infected with H . contortus [5] . Infections with these parasites can be cleared using anthelmintic drugs , but frequent administration has led to increased drug resistance [6–9] . Although the host immune response to infection with passively ingested nematodes is well-studied [10–12] , remarkably little is known about the behaviors of the parasites themselves . A better understanding of the behaviors exhibited by the environmental life stages of these parasites could facilitate the development of new strategies for preventing infections of humans and livestock , such as the use of targeted traps or repellents . Parasitic nematodes that actively invade hosts by skin penetration are known to engage in sensory-driven host seeking [13] . For example , the human hookworms Ancylostoma duodenale and Necator americanus , and the dog hookworm Ancylostoma caninum , are relatively inactive in the absence of sensory stimuli but show increased activity in the presence of heat , CO2 , and/or skin extract [14–16] . Hookworms also migrate robustly toward a heat source [14 , 17] . The human , non-human primate , and canine threadworm Strongyloides stercoralis , and the rat parasites Strongyloides ratti and Nippostrongylus brasiliensis , also respond robustly to host-emitted sensory cues . They are active in the absence of sensory stimuli [18] , and show robust attraction to a wide variety of odorants emitted by human skin and sweat [18–20] . S . ratti is also known to be attracted to blood serum , and S . stercoralis to blood serum , sweat , and heat [19 , 21 , 22] . The sensory behaviors of passively ingested nematodes are much less understood . Some passively ingested worms are capable of responding to environmental sensory cues such as temperature , humidity , and odorants [13] . For example , H . contortus uses temperature and humidity cues to migrate vertically through grass in response to changes in environmental conditions [23 , 24] . Because passively ingested worms do not actively invade hosts , it has often been assumed that they do not host seek and do not respond to host-emitted sensory cues . However , we recently showed that H . contortus is attracted to some host-emitted odorants , raising the possibility that it can use olfactory cues to position itself in the vicinity of potential hosts [18] . Since many hosts develop immunity to passively ingested worms following repeated infection [25 , 26] , behaviors that expose these parasites to new hosts may be important for parasite propagation . Here , we use the passively ingested gastrointestinal murine parasite H . polygyrus ( also called H . bakeri [27 , 28] ) as a model system for studying the sensory behaviors of passively ingested gastrointestinal nematodes , and for testing the hypothesis that passively ingested nematodes engage in host seeking . As a mouse parasite , H . polygyrus is one of the only passively ingested nematodes that can be easily maintained in the lab [29 , 30] . H . polygyrus is only infective as developmentally arrested iL3s , which are analogous to Caenorhabditis elegans dauers ( S1 Fig ) [31] . H . polygyrus iL3s were thought to primarily reside in host feces and infect when mice , which are coprophagic , eat infested feces [32 , 33] . However , H . polygyrus iL3s can also attach to mouse fur and be ingested during grooming [34] . H . polygyrus iL3s were previously shown to nictate [33 , 34] , a behavior where the iL3 stands on its tail and waves its head [13] , which may increase the probability of being swallowed during coprophagy or of becoming attached to mouse fur [34] . Once inside the host , the nematodes grow to adulthood and reproduce in the host intestine . H . polygyrus eggs then exit the host in feces and develop there into iL3s capable of infecting new hosts . The fact that H . polygyrus develops on feces and infects mice from feces raises the question of whether H . polygyrus iL3s engage in environmental navigation using either host-emitted or environmental sensory cues , or whether they simply remain on feces and wait to be ingested . While this question had not been investigated thoroughly , H . polygyrus iL3s were previously found to be attracted to mouse urine and skin lipids , suggesting they are capable of responding to at least some host sensory cues [34] . However , the extent to which H . polygyrus iL3s engage in sensory behaviors that increase the likelihood that they will be swallowed by hosts remained unclear . To address this question , we conducted a large-scale quantitative analysis of the unstimulated and odor-stimulated behaviors of H . polygyrus . We found that H . polygyrus iL3s were active in the absence of odor stimulation . In addition , they were attracted to host fecal odor . While they showed robust attraction to fresh feces , they showed reduced attraction to aged feces and ultimately migrated off their original fecal source to engage in environmental navigation . H . polygyrus iL3s were attracted to skin odorants as well as fecal odorants , suggesting that they are capable of migrating toward hosts as well as new host fecal sources . In addition , H . polygyrus iL3s showed experience-dependent olfactory plasticity , such that some host-emitted odorants were repulsive to iL3s cultured on feces but attractive to iL3s cultured off feces . Olfactory plasticity was also observed in the ruminant parasite H . contortus , and may be a general mechanism that enables passively ingested iL3s to shift from dispersal behavior to host-seeking behavior . Our results suggest that passively ingested nematodes disperse from feces and engage in host seeking to position themselves where they are likely to be ingested by new hosts .
Parasitic nematodes are known to vary in their environmental navigation strategies: some are cruisers that actively navigate toward hosts; some are ambushers that are less active and primarily attach to passing hosts; and some use an intermediate strategy [13] . To gain insight into the movement strategy used by H . polygyrus , we first examined the unstimulated movement of H . polygyrus iL3s , and compared their movement to that of S . stercoralis and S . ratti iL3s , which are known to be cruisers [18] . Using a dispersal assay in which iL3s were allowed to migrate on an agar surface in the absence of applied sensory stimulation for 1 hour , we found that H . polygyrus iL3s and S . ratti iL3s dispersed to a similar extent , whereas S . stercoralis iL3s dispersed more than either rodent parasite ( Fig 1A ) . These results demonstrate that H . polygyrus iL3s are active in the absence of sensory stimulation and are capable of exhibiting a movement strategy resembling that of a cruiser . The increased movement of S . stercoralis iL3s relative to H . polygyrus and S . ratti iL3s may reflect the larger habitats of humans relative to nesting rodents [18]; since nesting rodents spend more time near their fecal deposits than do humans , non-human primates , and dogs , S . stercoralis iL3s may need to disperse farther into the environment to successfully locate a host . Dispersal behavior reflects both crawling speed and other parameters such as crawling trajectory and tendency to pause during crawling . To gain more insight into the navigational strategy used by H . polygyrus iL3s , we tracked their crawling speed using automated worm tracking [35] . We found that H . polygyrus iL3s crawled more slowly than S . ratti iL3s , while S . stercoralis iL3s crawled much more rapidly than the rodent parasites ( Fig 1B ) . The ability of H . polygyrus iL3s to disperse to the same extent as S . ratti iL3s despite their slower crawling speed suggests that H . polygyrus iL3s exhibit more linear and/or continuous movement than S . ratti iL3s . We also evaluated the nictation behavior of H . polygyrus . Many skin-penetrating and passively ingested iL3s engage in nictation , a common ambushing behavior , as a means of increasing host contact . By standing up on a surface , nictating iL3s are more likely to touch and then transfer onto a passing host , or to be swallowed by a foraging host [13] . We assayed the nictation behavior of H . polygyrus , and compared it to that of S . ratti and S . stercoralis , using “micro-dirt” agar chips with near-microscopic pillars as an artificial dirt substrate ( S2 Fig ) [36] . The pillars on the agar surface minimize surface tension , allowing the iL3s to stand . We found that all three of the species showed similarly low nictation frequencies: only ~20–30% of the tested iL3s nictated during the assay period ( Fig 1C ) . The low nictation frequencies of S . ratti and S . stercoralis are consistent with a cruising navigational strategy [18] . The similarly low nictation frequency of H . polygyrus , combined with its active crawling behavior , suggests that it also behaves more like a cruiser than an ambusher . These results demonstrate that passively ingested iL3s do not remain inactive waiting to be swallowed by passing hosts . Rather , like skin-penetrating iL3s , they engage in environmental navigation . If passively ingested iL3s utilize active strategies to position themselves in optimal locations for host ingestion , one strong prediction is that the species that infect coprophagic hosts ( e . g . , mice ) will be attracted to host feces . We examined the response of H . polygyrus iL3s to fresh fecal odor using a chemotaxis assay in which the iL3s could smell but not make contact with the feces . We found that H . polygyrus iL3s were strongly attracted to fresh mouse feces ( Fig 2A and 2B ) . Moreover , they preferred mouse feces to gerbil or rabbit feces ( Fig 2B and 2C ) , indicating that they can distinguish host from non-host feces . By contrast , S . stercoralis and S . ratti iL3s were neutral to host feces ( Fig 2A ) [18] . The different responses of H . polygyrus and Strongyloides iL3s to fecal odor are understandable in the context of their different lifestyles . Although the pre-infective larvae of both H . polygyrus and Strongyloides inhabit host feces , H . polygyrus iL3s can infect hosts from feces while skin-penetrating iL3s must migrate off feces and onto host skin [13 , 30] . Thus , attraction to host feces would likely be ecologically advantageous for H . polygyrus iL3s but not Strongyloides iL3s . In addition , we found that H . polygyrus iL3 were more attracted to fresh feces than aged feces ( Fig 2D ) , suggesting that the iL3s use olfaction to identify favorable fecal sources . In contrast , they did not show a preference for feces from uninfected versus infected hosts ( Fig 2D ) , suggesting that they are attracted to fresh host feces regardless of the infection status of the host . Attraction of H . polygyrus iL3s to fecal odor may cause some of the iL3s on a fresh fecal source to remain there , and may draw iL3s from fecal sources that have become suboptimal due to age , desiccation , or other conditions . The robust attraction of H . polygyrus iL3s to fecal odor raised the question of whether the iL3s leave feces under normal conditions . To address this question , we performed two different fecal dispersal assays , the first to assess short-term dispersal over the course of hours and the second to assess long-term dispersal over the course of days . In the short-term dispersal assay , iL3s were placed on fresh feces in the center of an agar surface . The frequency with which the iL3s migrated off the feces and onto the agar was then quantified . We found that on average , 50% of the iL3 population left the fresh feces; in some trials , over 80% of the iL3s left the feces ( Fig 2E ) . These results demonstrate that even for iL3s on fresh feces , which are presumably a favorable fecal source , a substantial portion of the iL3 population migrates off of the feces and engages in environmental navigation . In the long-term dispersal assay , a fresh fecal pellet from an infected animal was collected , and one-half of the pellet was placed in the center of an agar surface . The frequency with which the nematodes migrated off of the feces and onto the agar was then quantified each day for a period of 10 days . Thus , this assay examined H . polygyrus dispersal in the more natural context of fecal aging . We found that nearly all of the nematodes remained on the feces until day 5 . On day 5 , by which time the nematodes had developed into iL3s [29] , over 80% of the nematodes migrated off the feces ( Fig 2F ) . By day 10 , nearly 100% of the nematodes had migrated off of the feces ( Fig 2F ) . In the same assay , we also examined nictation behavior and found that nictation occurs primarily on day 5 ( S3 Fig ) , at the time when the majority of the population migrates off of the feces ( Fig 2F ) . Together , these results argue against the possibility that some members of the iL3 population are ambushers while others are cruisers , and suggest instead that nearly all H . polygyrus iL3s ultimately engage in cruising behavior . Our results show that H . polygyrus iL3s will eventually leave their original fecal source and migrate toward new fecal sources to position themselves for ingestion during coprophagy . However , H . polygyrus iL3s can infect during grooming [34] , raising the question of whether they also migrate toward hosts by detecting host-emitted olfactory cues . To investigate this possibility , we examined the responses of H . polygyrus iL3s to a large panel of odorants that included compounds found in mammalian skin and sweat using a chemotaxis assay ( S4 Fig ) [18] . We found that H . polygyrus iL3s showed robust attraction to 6 of the 35 odorants tested: 2-butanone; 2 , 3-butanedione; geranyl acetone; 3-methyl-1-butanol; 2-methyl-1-butanol; and 3-heptanol ( Fig 3 ) . In contrast , CO2 was repulsive for H . polygyrus iL3s ( Fig 3 ) . All of the attractive odorants are emitted from mammalian skin , feces , and/or urine [18 , 37–41] . Notably , 2-methyl-1-butanol , 3-methyl-1-butanol , and geranyl acetone are present in skin microbiota [42 , 43] and are known attractants for skin-penetrating nematodes [18] . Attraction to these odorants could drive migration of H . polygyrus iL3s toward hosts . To gain insight into how the olfactory preferences of H . polygyrus iL3s differ from those of other iL3s that engage in environmental navigation , we compared the odor-driven behaviors of H . polygyrus to those of 7 other nematode species: the skin-penetrating human-parasitic nematode S . stercoralis , the skin-penetrating rat-parasitic nematodes S . ratti and N . brasiliensis , the passively ingested ruminant-parasitic nematode H . contortus , the actively invading entomopathogenic nematodes Heterorhabditis bacteriophora and Steinernema carpocapsae , and the free-living bacterivorous nematode C . elegans . This comparison revealed that H . polygyrus responds differently to the odorant panel than the other species ( S5A Fig ) , consistent with previous studies demonstrating that parasitic nematodes show species-specific olfactory preferences [18 , 44 , 45] . Moreover , cluster analysis of the 8 species based on their olfactory preferences revealed that parasitic nematodes that infect the same hosts have more similar olfactory preferences than parasitic nematodes that infect different hosts ( S5B Fig ) [18 , 44 , 45] . In contrast , parasitic nematodes that infect different hosts but share the same mode of infection do not respond similarly to odorants . In particular , H . polygyrus and H . contortus are both passively ingested but infect different hosts , and their olfactory preferences are dissimilar ( S5B Fig ) . Thus , olfactory preferences appear to be determined primarily by host range rather than infection mode . The fact that distantly related species that target the same host respond similarly to odorants strongly suggests that parasitic nematode olfactory behavior has evolved to mediate specific parasite-host interactions . iL3s that have migrated off feces likely face a greater ethological drive to search for new hosts or fecal sources than iL3s that have remained on feces . We therefore wondered whether iL3s that have migrated off feces might exhibit different behaviors than iL3s on feces . To test this possibility , we compared the unstimulated migration of iL3s cultivated on feces to those of iL3s that had been removed from feces and maintained in dH2O for 1 week . We found that the off-feces iL3s dispersed to a greater extent than the on-feces iL3s ( Fig 4A ) , demonstrating that the unstimulated activity of H . polygyrus iL3s is subject to experience-dependent modulation . The greater dispersal of off-feces iL3s was not due to changes in crawling speed ( Fig 4B ) ; thus , the difference in dispersal reflects a difference in navigational strategy rather than motility . In addition , the nictation rate of on-feces vs . off-feces iL3s was unchanged ( Fig 4C ) , demonstrating that removal from feces results in a specific change in crawling behavior . The increased dispersal of off-feces iL3s likely increases the probability of encountering a new host or fecal source . To further elucidate the effects of recently experienced environment on H . polygyrus behavior , we compared the olfactory preferences of on-feces vs . off-feces iL3s to a subset of mammalian odorants . The odorant panel was selected to include attractive , neutral , and repulsive odorants . We found that on-feces and off-feces iL3s responded differently to 2 of 8 tested odorants: CO2 and benzaldehyde . Both odorants were repulsive for iL3s on feces but attractive for iL3s off feces ( Fig 5A ) . For both on-feces and off-feces iL3s , CO2-response valence , i . e . whether CO2 was repulsive or attractive , was consistent across concentrations ( S6 Fig ) . CO2 is a critical host cue for many parasites , including many parasitic nematodes [13]; it is present at high concentrations in both exhaled breath and feces . Benzaldehyde is found in skin , breath , urine , and feces [18] . Thus , the olfactory responses of H . polygyrus iL3s to some host-associated odorants are subject to experience-dependent modulation as a result of recently experienced environmental conditions . We then examined the relationship between cultivation environment and sensory behavior in more detail by investigating the time course of the change in CO2- and benzaldehyde-response valence . We found that CO2-response valence changed gradually over the course of days when iL3s were removed from feces ( Fig 5B ) . Moreover , culturing iL3s under high CO2 conditions prevented the shift in CO2-response valence following removal from feces . While iL3s cultured off feces at ambient CO2 ( ~0 . 04% CO2 [46] ) were attracted to CO2 , iL3s cultured off feces at high CO2 ( 2 . 5% CO2 ) were repelled by CO2 ( Fig 5C ) . Thus , CO2-response valence is regulated by environmental CO2 levels . Benzaldehyde-response valence also changed gradually over the course of days upon removal from feces and was also determined by environmental CO2 levels ( Fig 5D and 5E ) . These results suggest that the level of environmental CO2 acts as a general regulator of olfactory behavior . Given that feces emit high levels of CO2 [39] , H . polygyrus iL3s may use environmental CO2 levels to signal the presence or absence of feces , with the result that exposure to high CO2 levels mimics the effects of exposure to feces . Experience-dependent olfactory plasticity may be a mechanism that enables iL3s on feces to disperse from the feces , and iL3s that have been off feces for a prolonged period to instead migrate toward new hosts or fresh fecal sources . Our finding that H . polygyrus iL3s exhibit experience-dependent olfactory plasticity raised the question of whether this behavior is unique to H . polygyrus or shared with other parasitic nematode species . To distinguish between these possibilities , we examined the CO2-evoked behaviors of H . contortus , S . stercoralis , and the skin-penetrating human-parasitic hookworm Ancylostoma ceylanicum cultured on versus off feces . We found that like H . polygyrus iL3s , H . contortus iL3s show experience-dependent plasticity in their response to CO2 . In the case of H . contortus , CO2 is neutral for iL3s cultured on feces but attractive for iL3s cultured off feces ( Fig 6A ) . Since H . contortus iL3s are long-lived [47 , 48] , sometimes surviving in the environment for up to 8 months [48] , we examined the CO2-evoked behavior of off-feces iL3s over the course of 5 weeks . We found that CO2 changed from neutral to attractive after 1 week , and then remained attractive in subsequent weeks ( Fig 6A ) . Thus , CO2 remains a strong attractant for H . contortus iL3s that have been removed from feces for prolonged periods . Our results demonstrate that experience-dependent olfactory plasticity is not unique to H . polygyrus , but also occurs in other passively ingested nematodes . Experience-dependent modulation of CO2 response may enable H . contortus iL3s to first migrate off feces and then navigate toward grazing hosts , which emit high concentrations of CO2 in their exhaled breath . In contrast to the passively ingested nematodes , the skin-penetrating nematodes tested did not show experience-dependent modulation of their CO2-evoked behavior . Both S . stercoralis iL3s and A . ceylanicum iL3s were repelled by CO2 when cultured both on and off feces ( Fig 6B and 6C ) . The lack of flexibility in their CO2-evoked behavior may reflect the fact that CO2 attraction would likely not facilitate host finding by skin-penetrating worms , since very low levels of CO2 are given off by the skin [49] . CO2 avoidance may function as a dispersal mechanism to drive skin-penetrating iL3s off host feces; attraction to other sensory cues , such as skin and sweat odorants , may then drive the iL3s toward potential hosts [13] . Thus , the ability to exhibit flexible responses to CO2 may be a specific behavioral adaptation of passively ingested but not skin-penetrating nematodes .
Here we conducted the first large-scale quantitative behavioral analysis of H . polygyrus iL3s . We found that H . polygyrus iL3s were active even in the absence of sensory stimulation ( Fig 1 ) . These results argue against the classical notion that passively ingested iL3s remain stationary and wait to be swallowed , and suggest instead that these iL3s actively navigate their environments . We previously showed that H . contortus iL3s are less active than S . ratti and S . stercoralis iL3s [18] . However , the similar dispersal behaviors and nictation rates of H . polygyrus and S . ratti ( Fig 1 ) suggest that some passively ingested nematodes are as active as skin-penetrating nematodes despite their passive mode of infection . Our examination of the olfactory preferences of H . polygyrus iL3s revealed that they are attracted to fecal odor as well as mammalian skin and sweat odorants ( Figs 2 and 3 ) . These results suggest that passively ingested iL3s engage in odor-driven host seeking to position themselves near hosts or host feces , where they are likely to be ingested . Consistent with the attraction of H . polygyrus iL3s to both fecal odor and host odorants , H . polygyrus iL3s have been shown to infect hosts either from feces during coprophagy or from fur during grooming [30 , 32–34] . Thus , active migration toward new hosts or fecal sources may be a critical but often overlooked aspect of the environmental stage of the H . polygyrus life cycle . The robust attraction of H . polygyrus iL3s to fecal odor could serve to keep some of the iL3s on favorable fecal sources , or to direct them away from suboptimal fecal sources toward more favorable sources . However , we found that even when iL3s are placed on fresh feces , which is presumably a favorable fecal source , approximately half of the population migrates off of the feces within an hour ( Fig 2E ) . Moreover , we found that nearly all iL3s eventually leave their original fecal source to engage in environmental navigation ( Fig 2F ) . These results suggest that all H . polygyrus iL3s are capable of engaging in environmental navigation , and that if they are not ingested with feces shortly after reaching the iL3 stage , they will leave their original fecal source and disperse into the environment . Once in the environment , they use olfactory cues to migrate toward hosts or new fecal sources ( Fig 7 ) . At the population level , this behavioral flexibility may help to ensure maximal infection rates . Remaining on a known fecal source can in some cases be beneficial: if that fecal source is in or near a nest , the iL3s may encounter hosts by remaining in the nest . However , this behavioral strategy also carries risk: many mice forage and deposit feces far from their nests , in locations where the iL3s are less likely to encounter a mouse using a “sit-and-wait” strategy [34] . Under these circumstances , first dispersing from feces and then using host-emitted sensory cues to migrate toward new hosts or fecal sources is likely to be essential to continue the life cycle . Thus , maximal parasite survival may be achieved when iL3s that do not immediately encounter a host actively disperse in search of hosts . In future studies , it will be interesting to determine whether nematodes that exit the host early in an infection cycle show different dispersal behavior than nematodes that exit the host late in an infection cycle , or whether nematodes that emerge from hosts with a heavier worm burden show different dispersal behavior than nematodes that emerge from hosts with a lighter worm burden . What is the mechanism that drives some iL3s to migrate off feces , and subsequently toward new hosts or fecal sources ? We speculate that olfactory plasticity may function as this mechanism . We have shown that H . polygyrus iL3s display experience-dependent olfactory plasticity: some odorants are repulsive to iL3s that have been cultured on feces but attractive to iL3s that have been cultured off feces for a week ( Fig 5 ) . Repulsion of iL3s from odorants such as CO2 and benzaldehyde , which are emitted by host feces [18 , 39] , may cause the iL3s to migrate off of their original fecal source and disperse into the environment . Once the iL3s have been in the environment for multiple days , these odorants become attractive , likely driving the iL3s toward new hosts or fecal sources . The shift from repulsion to attraction for both CO2 and benzaldehyde response is mediated by environmental CO2 levels ( Fig 5C and 5E ) . When iL3s are removed from feces but cultured in the presence of high CO2 , they remain repelled by both CO2 and benzaldehyde . However , when iL3s are removed from feces and cultured at ambient CO2 , they become attracted to CO2 and benzaldehyde . These results suggest that environmental CO2 levels may be used as a proxy for the presence or absence of feces . We found that like H . polygyrus iL3s , H . contortus iL3s show experience-dependent modulation of their CO2-evoked behavior . H . polygyrus iL3s showed a shift in their CO2 response from repulsive to attractive following removal from feces ( Fig 5 ) , while H . contortus iL3s showed a shift in their CO2 response from neutral to attractive ( Fig 6A ) . Thus , in both cases , CO2 attraction is likely to be observed in nature in iL3s that have migrated off of feces and are engaging in environmental navigation . In contrast to the passively ingested nematodes tested , the skin-penetrating nematodes tested did not show experience-dependent modulation of their CO2-evoked behavior ( Fig 6B and 6C ) . Thus , experience-dependent plasticity based on the presence or absence of feces may be specific to passively ingested nematodes . The differences in CO2-evoked behavior between passively ingested iL3s and skin-penetrating iL3s are consistent with their different ecologies . Skin-penetrating iL3s infect primarily via the skin , which emits low levels of CO2 [49] , so CO2 attraction may not be beneficial for skin-penetrating iL3s regardless of their cultivation conditions . Passively ingested nematodes infect via the mouth , which emits high levels of CO2 [50] . Thus , in the case of passively ingested nematodes , repulsive or neutral responses to CO2 by iL3s on feces may initially drive them off feces , while subsequent attractive responses to CO2 may drive them toward the mouths of respiring hosts . H . contortus is one of the most economically significant livestock parasites worldwide [5] , and drug resistance resulting from repeated use of anthelmintic drugs is already a major challenge in combatting infections [9] . Our finding that the olfactory responses of H . contortus are experience-dependent could facilitate the development of odor-based traps or repellents that could be used in combination with grazing management interventions [51 , 52] to prevent nematode infections . The circuit mechanisms that drive experience-dependent valence changes in passively ingested nematodes remain to be determined . In C . elegans , CO2-response valence is also subject to experience-dependent modulation: adults cultured at ambient CO2 are repelled by CO2 , while adults cultured at high CO2 are attracted to CO2 [53] . Both CO2 attraction and CO2 repulsion by C . elegans are mediated by the BAG sensory neurons in the head and a group of downstream interneurons . The CO2-evoked activity of these interneurons is subject to experience-dependent modulation , enabling them to generate opposite behavioral responses to CO2 [53] . Since sensory neuroanatomy is generally conserved across nematode species [13] , similar circuit mechanisms may operate in passively ingested parasitic nematodes to regulate CO2-response valence . The molecular mechanisms that drive experience-dependent valence changes in passively ingested nematodes are also not yet known . In C . elegans , CO2-response valence is regulated by neuropeptide signaling [53] . However , CO2-response valence in C . elegans changes over the course of hours [53] , while CO2-response valence in passively ingested parasitic nematodes changes over the course of days ( Figs 5B and 6A ) . Thus , the valence change in parasitic nematodes could involve changes in gene expression and/or neuronal wiring , which occur on a slower timescale than neuropeptide signaling [54–59] . Elucidating the mechanisms that operate in passively ingested nematodes to control olfactory valence will require the development of genetic engineering techniques for these species , which have so far remained intractable to molecular genetic manipulation [60] . Targeted mutagenesis using the CRISPR-Cas9 system has now been achieved in Strongyloides species [60–61] , and may be applicable to other types of parasitic nematodes in the future . Entomopathogenic nematodes and skin-penetrating nematodes also show olfactory plasticity , but in response to changes in their prior cultivation temperature [62] . In addition , the entomopathogenic nematode Steinernema scapterisci shows age-dependent olfactory plasticity in its response to CO2: CO2 changes from a repulsive cue in young iL3s to an attractive cue in older iL3s [62] . Thus , olfactory plasticity may be a general feature of parasitic nematode behavior that enables iL3s to modulate their sensory responses based on internal or external conditions so as to increase their chances of encountering a host . Passively ingested nematodes comprise a group of human and livestock parasites whose behaviors have remained elusive . Increased drug resistance [6–9] necessitates the development of new strategies for their control . Our results suggest that passively ingested nematodes engage in robust and dynamic odor-driven host-seeking behaviors . A better understanding of these behaviors may lead to new strategies for preventing infections .
H . polygyrus was passaged in mice , S . stercoralis was passaged in gerbils , and A . ceylanicum was passaged in hamsters . All procedures and protocols were approved by the UCLA Office of Animal Research and Oversight ( Protocol 2011-060-13B ) , which adheres to the standards of the AAALAC and the Guide for the Care and Use of Laboratory Animals . Heligmosomoides polygyrus ( also called Heligmosomoides bakeri [27] ) was generously provided by Dr . Raffi Aroian ( University of Massachusetts Medical School ) . Strongyloides stercoralis ( UPD strain ) was generously provided by Dr . James Lok ( University of Pennsylvania ) , Ancylostoma ceylanicum ( Indian strain , US National Parasite Collection Number 102954 ) was generously provided by Dr . John Hawdon ( George Washington University ) , and Haemonchus contortus was generously provided by Dr . Anne Zajac ( Virginia-Maryland College of Veterinary Medicine ) . Male or female C57BL/6 mice for propagation of H . polygyrus were obtained from the UCLA Division of Laboratory Animal Medicine Breeding Colony . Male Mongolian gerbils for propagation of S . stercoralis and male Syrian golden hamsters for propagation of A . ceylanicum were obtained from Envigo . H . contortus was not propagated in our laboratory . H . polygyrus was serially passaged in C57BL/6 male or female mice as described [30] and maintained on fecal-charcoal plates as described [18] . Briefly , mice were inoculated with ~150 iL3s administered in 100 μL ddH2O by oral gavage . Feces infested with H . polygyrus were collected between days 10 and 60 post-inoculation . Feces were obtained by placing mice overnight on wire cage bottoms above damp cardboard , and collecting the pellets from the cardboard the following morning . Fecal pellets were mixed with dH2O and autoclaved charcoal granules to make fecal-charcoal plates . Plates were stored at room temperature until use . iL3s used for behavioral analysis were collected from fecal-charcoal plates using a Baermann apparatus [63] . iL3s cultured “on feces” were collected from fecal-charcoal plates on day 14 ( with day 0 being the day of fecal collection ) and tested immediately; iL3s cultured “off feces” were collected from fecal-charcoal plates on day 7 , incubated in dH2O for 7 days at room temperature , and tested on day 14 . For the odorant chemotaxis assays in Fig 3 , iL3s were either collected from fecal-charcoal plates on days 7–14 and tested immediately or collected on days 7–14 and stored for up to 10 days in dH2O at 4°C prior to testing ( storage at 4°C in dH2O is a standard cultivation condition for H . polygyrus [30] ) . In all cases where differences were observed following storage in dH2O at 4°C , the data from iL3s stored at 4°C in dH2O was excluded from the analysis . For the “off feces” time course in Fig 5 , iL3s were collected from fecal-charcoal plates on day 7 , incubated in dH2O for the indicated number of days , and then tested . For assays involving iL3s cultured at 2 . 5% CO2 either on or off feces , iL3s were collected from fecal-charcoal plates on day 7 . iL3s for the on-feces condition were placed onto new fecal-charcoal plates containing autoclaved feces , stored in a CO2 incubator with 2 . 5% CO2 for 7 days , and collected from the fecal-charcoal plates using a Baermann apparatus immediately prior to testing . iL3s for the off-feces condition were incubated in dH2O in a CO2 incubator with 2 . 5% CO2 for the indicated number of days and then tested . H . contortus was maintained on fecal-charcoal plates as described [18] . Plates were stored in an incubator at 23°C until use . iL3s used to test CO2 response in Fig 6A were either cultured on fecal-charcoal plates for up to 9 weeks and then tested immediately; or removed from feces , stored in dH2O for up to 5 weeks , and then tested . Notably , iL3s maintained on feces and tested immediately showed a neutral response to CO2 regardless of their age , demonstrating that the CO2 attraction of off-feces iL3s was due to their removal from feces and not their age . S . stercoralis was serially passaged in male Mongolian gerbils and maintained on fecal-charcoal plates as described [18] . Briefly , gerbils were inoculated with ~2 , 250 iL3s in 200 μL sterile PBS by subcutaneous injection . Feces infested with S . stercoralis were collected between days 14 and 45 post-inoculation . Feces were harvested and mixed with autoclaved charcoal granules to make fecal-charcoal plates as described above . Plates were stored in an incubator at 23°C until use . iL3s used to test CO2 response in Fig 6B were cultured on fecal-charcoal plates until day 7; they were then either tested immediately , stored in BU saline [64] for 1 week and then tested , or stored in BU saline for 2 weeks and then tested . A . ceylanicum was serially passaged in male Syrian golden hamsters and maintained on fecal-charcoal plates as described [18] . Briefly , hamsters were inoculated with ~100 iL3s in 100 μL sterile ddH2O by oral gavage . Feces infested with A . ceylanicum were collected between days 14 and 45 post-inoculation . Feces were harvested and mixed with autoclaved charcoal granules to make fecal-charcoal plates as described above . Plates were stored in an incubator at 23°C until use . iL3s used to test CO2 response in Fig 6C were cultured on fecal-charcoal plates until day 10; they were then either tested immediately , stored in BU saline [64] for 1 week and then tested , or stored in BU saline for 2 weeks and then tested . Short-term dispersal assays without feces ( Figs 1A and 4A ) were performed essentially as described [18] . For each trial , ~50–100 iL3s were placed on a 10-cm chemotaxis plate [65] on a vibration-reducing platform and allowed to disperse for either 1 hour ( Fig 1A ) or 10 minutes ( Fig 4A ) in the absence of applied sensory stimuli . The number of iL3s in the outer zone of the plate ( the region that excludes a 4-cm-diameter circle at the center of the plate ) was then determined . For short-term fecal dispersal assays ( Fig 2E ) , fresh fecal pellets were collected the morning of the assay from uninfected animals . One fecal pellet ( ~0 . 03 g ) was placed in the center of a 10-cm chemotaxis plate . 15–40 iL3s were pipetted onto the pellet . The plates were then placed on a vibration-reducing platform for 1 hour . The number of iL3s either on the feces , off the feces but within a 4-cm-diameter circle around the feces ( zone 1 ) , or outside a 4-cm-diameter circle around the feces ( zone 2 ) was then determined ( Fig 2E ) . iL3s were not visible when they were on the fecal pellet , so the number of iL3s remaining on the feces at the end of the assay was determined by subtracting the number of iL3s in zones 1 and 2 from the total number of iL3s added to the feces . Note that for all dispersal assays , the outermost zone included the walls of the plate , which functioned as a trap such that most of the iL3s that crawled onto the walls of the plate remained there for the duration of the assay . Long-term fecal dispersal assays ( Fig 2F ) were performed by first collecting fresh feces from infected animals; feces were collected as described above , but from a 4-hour collection period . Feces were collected from host animals that were each infected with ~75 iL3s . Individual fecal pellets of similar size were cut in half; one-half of a fecal pellet was then placed on each chemotaxis assay plate and incubated at room temperature . Every 24 hours ( within a 3-hour window ) , the number of animals that had migrated out of the feces and onto the chemotaxis plate was quantified . After quantification , fecal pellets were transferred to fresh chemotaxis plates . On day 10 , the fecal pellets were dissociated and the number of iL3s remaining in the fecal pellet was quantified . These numbers were then used to calculate the total number of worms that started out on each fecal pellet , and the cumulative percentage of worms that migrated off the fecal pellet each day . Nictation rates were also determined for each day by counting the number of worms observed to be nictating on the fecal pellet at each time of observation . These numbers were used , in combination with the number of worms remaining on the fecal pellet for each day ( calculated as described above ) , to calculate the percentage of worms nictating on the fecal pellets at each time of observation ( S3 Fig ) . Automated tracking was performed as described [18] . For each recording session , 10–15 iL3s were placed on a chemotaxis plate and allowed to acclimate for 10 minutes . iL3 movement was then captured for 20 seconds using an Olympus E-PM1 digital camera attached to a Leica S6 D microscope . WormTracker and WormAnalyzer [35] were used to quantify crawling speed . WormTracker and WormAnalyzer settings were previously described [18] . The nictation assays shown in Figs 1C and 4C were performed essentially as described ( S2 Fig ) [18 , 62] . Briefly , agar chips for nictation assays were made from polydimethylsiloxane ( PDMS ) molds [36] . Chips were approximately 3 cm x 3 . 5 cm and contained near-microscopic posts that allowed the iL3s to stand . Chips were made using 4% agar dissolved in ddH2O . Once the agar had solidified , chips were placed at 37°C for 2 hours followed by room temperature for 1 hour . 10–20 iL3s were transferred to the center of the chip in a 5 μL drop of dH2O and allowed to acclimate for 10 minutes . Individual iL3s were then monitored for 2 minutes , and the number of iL3s that nictated during the observation period was recorded . Nictation was defined as an iL3 raising at least half of its body off the plate for at least 5 seconds ( S2 Fig ) . Chemotaxis assays were performed on chemotaxis plates as described [18 , 44] . For fecal and odorant chemotaxis assays , 2 μL 5% sodium azide was placed into the center of each scoring region . For fecal chemotaxis assays , feces were obtained from an overnight fecal collection . For assays involving feces from uninfected vs . infected animals ( Fig 2D , right ) , feces were obtained from a 4-hour fecal collection . The feces were then incubated for 3 days at room temperature in a 10-cm Petri dish on filter paper moistened with 1 mL ddH2O to prevent desiccation . For assays involving fresh vs . aged feces ( Fig 2D , center ) , feces were obtained from a 4-hour fecal collection and stored in a 10-cm Petri dish without filter paper . “Fresh feces” refers to feces that were used on the day of collection , while “aged feces” refers to feces that were incubated in the Petri dish for 1 day . For all fecal assays , the feces were moistened to a paste with ddH2O . 0 . 5-cm squares of filter paper were affixed to the lid of a chemotaxis plate using double-stick tape . 0 . 25 g fecal paste was placed onto one of the filter paper squares , and either 50 μL ddH2O ( for normal fecal chemotaxis assays ) or 0 . 25 g of feces ( for fecal competition chemotaxis assays ) was added to the other square . For odorant chemotaxis assays ( S4 Fig ) , 5 μL odorant was pipetted into the center of one scoring region and 5 μL control ( paraffin oil , ddH2O , or ethanol ) was pipetted into the center of the other scoring region . Liquid odorants were tested undiluted . Solid odorants were dissolved to test concentrations as follows: tetradecanoic acid , indole , and 3-methylindole were diluted 0 . 05 g in 2 . 5 mL ethanol; octadecanoic acid was diluted 1 g in 80 mL ethanol; L-lactic acid was diluted 0 . 05 g in 2 . 5 mL ddH2O; and ammonia was purchased as a 2 M solution in ethanol . ddH2O was used as a control for L-lactic acid; ethanol was used as a control for tetradecanoic acid , octadecanoic acid , indole , 3-methylindole , and ammonia; and paraffin oil was used as a control for all other odorants . For CO2 chemotaxis assays , gases were delivered at a rate of 0 . 5 mL/min through holes in the plate lids as previously described [18 , 44] . Gas stimuli were obtained from Airgas , and consisted of the test concentration of CO2 , 21% O2 , and the balance N2 . Air controls consisted of 21% O2 and 79% N2 . The test concentration of CO2 consisted of 15% CO2 for H . contortus and 10% CO2 for all other species , unless otherwise indicated . For all chemotaxis assays , ~200 iL3s were pipetted onto the center of the chemotaxis plate and allowed to distribute in the stimulus gradient on a vibration-reducing platform for 3 h ( for fecal and odorant chemotaxis assays ) or 1 h ( for CO2 assays ) . The number of iL3s in each scoring region was then quantified and a chemotaxis index was calculated as: ( # iL3s at stimulus–# iL3s at control ) / ( # iL3s at stimulus + control ) . At least two identical assays were always performed simultaneously with the stimulus gradient oriented in opposite directions to control for directional bias due to room vibration or other causes; the pair of assays was discarded if the difference in the chemotaxis indices for the pair of plates was ≥0 . 9 or if either of the plates had <7 iL3s in the scoring regions . For the odorant chemotaxis assays in Fig 3 , significance was calculated relative to a paraffin oil control . Statistical analysis was performed using GraphPad Prism or PAST [66] . For each experiment , the D’Agostino-Pearson omnibus normality test was used to determine whether the data were normally distributed . If the data were normally distributed , parametric tests were used; otherwise , non-parametric tests were used . Graphs show medians and interquartile ranges to accurately depict the distribution and variance in our datasets . The heatmap in S5A Fig was generated using Heatmap Builder [67] . | Many parasitic nematodes infect by passive ingestion when the host consumes food , water , or feces containing infective third-stage larvae ( iL3s ) . Passively ingested nematodes that infect humans cause severe gastrointestinal distress and death in endemic regions , and those that infect livestock are a major cause of production loss worldwide . Because these parasites do not actively invade hosts but instead rely on being swallowed by hosts , it has been assumed that they show only limited sensory responses and do not engage in host-seeking behaviors . Here , we investigate the olfactory behaviors of the passively ingested murine parasite Heligmosomoides polygyrus and show that this assumption is incorrect; H . polygyrus iL3s show robust attraction to a diverse array of odorants found in mammalian skin , sweat , and feces . Moreover , the olfactory responses of H . polygyrus iL3s are experience-dependent: some odorants are repulsive to iL3s cultured on feces but attractive to iL3s removed from feces . Olfactory plasticity is also observed in the ruminant parasite Haemonchus contortus , and may enable iL3s to disperse in search of new hosts or host fecal sources . Our results suggest that passively ingested nematodes use olfactory cues to navigate their environments and position themselves where they are likely to be swallowed . By providing new insights into the olfactory behaviors of these parasites , our results may enable the development of new strategies for preventing infections . | [
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] | 2017 | Experience-dependent olfactory behaviors of the parasitic nematode Heligmosomoides polygyrus |
Hemorrhagic fever with renal syndrome ( HFRS ) and Crimean-Congo hemorrhagic fever ( CCHF ) are common representatives of viral hemorrhagic fevers still often neglected in some parts of the world . Infection with Dobrava or Puumala virus ( HFRS ) and Crimean-Congo hemorrhagic fever virus ( CCHFV ) can result in a mild , nonspecific febrile illness or as a severe disease with hemorrhaging and high fatality rate . An important factor in optimizing survival rate in patients with VHF is instant recognition of the severe form of the disease for which significant biomarkers need to be elucidated . To determine the prognostic value of High Mobility Group Box 1 ( HMGB1 ) as a biomarker for disease severity , we tested acute serum samples of patients with HFRS or CCHF . Our results showed that HMGB1 levels are increased in patients with CCHFV , DOBV or PUUV infection . Above that , concentration of HMGB1 is higher in patients with severe disease progression when compared to the mild clinical course of the disease . Our results indicate that HMGB1 could be a useful prognostic biomarker for disease severity in PUUV and CCHFV infection , where the difference between the mild and severe patients group was highly significant . Even in patients with severe DOBV infection concentrations of HMGB1 were 2 . 8–times higher than in the mild group , but the difference was not statistically significant . Our results indicated HMGB1 as a potential biomarker for severe hemorrhagic fevers .
Viral hemorrhagic fevers ( VHFs ) are characterized by a severe multisystem syndrome and are caused by viruses from several different families . Among them , hantaviruses and Crimean-Congo Hemorrhagic fever virus ( CCHFV ) , members of the Bunyaviridae family , represent important causative agents of VHFs . Hantaviruses are present globally and each hantavirus is closely related to a specific rodent or insectivore natural host . Humans are generally infected by inhaling virus-contaminated aerosols from small mammal’s excreta . Hantaviruses cause two typical syndromes: Hemorrhagic fever with renal syndrome ( HFRS ) in Europe and Asia , and Hantavirus cardiopulmonary syndrome in the Americas . HFRS is an endemic disease in Slovenia , caused by Dobrava virus ( DOBV ) and Puumala virus ( PUUV ) . PUUV usually causes a milder form of the disease , called nephropathia epidemica ( NE ) , whereas DOBV is mainly responsible for the more severe disease with up to 10% case fatality rate . However , clinical severity of HFRS varies greatly . In Slovenia , both severe and mild clinical courses of the disease have been observed , with an overall case fatality rate of 4 . 5% [1 , 2] . CCHFV is the causative agent of Crimean-Congo hemorrhagic fever ( CCHF ) , which is the most widespread tick-borne viral infection . CCHF virus is transmitted by several genera of ixodid ticks to a variety of wild and domestic mammals , which develop a transient viremia without any signs of the illness . Humans are infected with CCHFV through a tick bite or exposure to the blood or other bodily fluids of infected animals or patients [3] . The course of the disease can be extremely severe , with hemorrhaging and case fatality rate between 3% and 30% . Similarly to HFRS , CCHF can also result in a mild , nonspecific febrile illness or an asymptomatic infection [4] . Studies showed that DOBV , PUUV and CCHFV infections cause different intensity of clinical and pathological manifestations in patients , like increased vascular permeability , coagulopathy , thrombocytopenia and hemorrhages . Immuno-pathogenic factors play an important role in the pathogenesis of both HFRS and CCHF . It was shown that the fatal cases of CCHF had an elevated serum cytokine level , weak or absent antibody response and high viral load [5 , 6] . Interferon ( IFN ) response also plays an important role in the pathogenesis of both diseases . Early activation of the type I IFN response was delayed by pathogenic hantaviruses and CCHFV , thereby enabling early viral replication and the spread of infection [7 , 8 , 9] . During the acute phase of HFRS and CCHF , patients show typical laboratory parameters like anemia , leukocytosis , thrombocytopenia , proteinuria , hematuria , elevated liver enzymes and serum creatinine . Severity of the disease is also linked to the levels of different biomarkers and can serve as prognostic indicators . Biological markers like interleukin 6 , pentraxin-3 , idoleamine 2 , 3 dioxygenase , soluble urokinase-type plasminogen activator receptor and GATA-3 were found to correlate with severity of NE [10 , 11 , 12] . C-reactive protein ( CRP ) and procalcitonin ( PCT ) are used particularly for discrimination between bacterial and viral infections and are usually found in high levels in patients with systemic bacterial infections and were also highly elevated in HFRS and CCHF patients [13 , 14 , 15 , 16] . Another proinflammatory marker connected to prognosis of hemorrhagic fevers is High Mobility Group Box 1 ( HMGB1 ) . HMGB1 is a non-histone nucleosomal protein that binds and bends DNA . It functions as a nuclear remodeling factor [17 , 18] . HMGB1 is usually found in the cell nucleus and can be secreted into the extracellular milieu passively from necrotic cells or actively by activated immune cells [19 , 20] . HMGB1 was linked to play a potential role in sepsis [21] , hemorrhagic shock , trauma [22] and several infectious viral diseases [23 , 24 , 25] . Allonso and coworkers showed that HMGB1 can be used as a biomarker for severe dengue prognosis and high HMGB1 levels were correlated with increased vascular permeability , intensity of symptoms and incidence of secondary infection [26 , 27] . Although different clinical studies were conducted , complete immunopathogenesis of aforementioned VHFs is still unknown . The aim of our study was to investigate the role of HMGB1 as a prognostic marker for HFRS and CCHF .
The study was approved by Republic of Slovenia National Medical Ethics Committee ( 69/03/12 and 30/04/15 ) . Collecting of CCHF serum samples was part of the CCH Fever network ( Collaborative Project ) supported by the European Commission under the Health Cooperation Work Programme of the 7th Framework Programme ( grant agreement no . 260427 ) . All participants gave an oral and written informed consent . The study was conducted according to the principles expressed in the Declaration of Helsinki . Altogether , 128 serum samples of patients with hemorrhagic fever were included in this study . HFRS patients were hospitalized in different Slovenian hospitals and clinical diagnosis was confirmed with serological or molecular tests as described elsewhere [1] . From 72 HFRS patients included in the study , 24 were infected with DOBV and 48 were infected with PUUV . Additionally , we have analyzed 29 serial samples from 7 HFRS patients . Fifty-six patients from Kosovo with confirmed CCHF were also included in the study . CCHFV infection was confirmed as described before [6] . For each patient a detailed medical chart was collected and significant laboratory parameters were analyzed . Additionally , serum samples of 61 healthy donors were tested . Blood samples from healthy donors were processed as patients’ samples . Concentration of HMGB1 was measured with commercially available HMGB1 capture enzyme-linked immunosorbent assay ( ELISA ) kit ( Chondrex , USA ) according to the manufacturer’s instructions . Samples were measured in duplicate . Statistical analyses were performed using GraphPad Prism version 6 for Windows ( GraphPad Software , San Diego , CA , USA ) . To analyze the normal distribution of data the D’Agostino-Pearson normality test was performed . Differences between groups were calculated using the non-parametric Mann-Whitney U test and Kruskal Wallis analyses . All statistical tests were two-tailed . A p-values below 0 . 05 was considered statistically significant .
To determine HMGB1 dynamic in the acute stage of illness multiple sample per patients were tested . Twenty-nine samples from 7 patients with HFRS were analyzed . Our results showed that in all 7 patients , the concentration of HMGB1 was the highest in the first sample and that the concentration of HMGB1 decreased during the hospitalization ( supplement data , S1 Fig ) . According to the observed kinetic , we have further evaluated the HMGB1 concentration in the first available serum sample of patients with hemorrhagic fever . Seventy-two patients with confirmed HFRS ( 24 infected with DOBV and 48 with PUUV ) and 56 patients with confirmed CCHF were enrolled in the study . Patients’ serum samples were collected during the onset of disease , at the admission to the hospital . Samples of HFRS patients were collected in the mean on 2 . 5 day of hospitalization ( between 2nd and 18th day of illness ) . Specifically , samples of DOBV infected patients were collected in the mean on 2 . 8 day of hospitalization ( between 3rd and 18th day of illness ) and samples of PUUV infected patients were collected in the mean on 2 . 4 day of hospitalization ( between 2nd and 11th day of illness ) . Similar as in HFRS patients , samples of CCHF patients were collected in the mean on 2 . 9 day of hospitalization ( between 1st and 21st day of illness ) . Patients were categorized into two groups: mild and severe , based on disease severity . Fatal patients were included in the severe group . Patients with HFRS were categorized based on clinical data and laboratory parameters as described before [28 , 29] . Twelve patients infected with DOBV were categorized as having the severe form of the disease and 12 as the mild form . Twenty-three PUUV patients were categorized as severe and 25 as mild . Patients with CCHF were categorized based on the classification by Swanepoel et al . [6 , 30] . Twenty-eight were categorized as having the severe form of the disease ( 13 fatal ) and 28 as the mild . To examine whether the demographic influenced concentration of HMGB1 , we have analyzed the age and gender composition of patients with hemorrhagic fevers . Among all included HFRS patients 81% were male and 19% were female . Age distribution among patients with HFRS was between 20 and 49 years . Thirty-nine male patients ( 70% ) and 17 female patients ( 30% ) with CCHF were included in our study . Most CCHF patients were aged between 30 and 39 years ( 18% ) . Age and gender composition of the control group were similar than the study group . Our results indicated no significant difference in concentration of HMGB1 among different age groups ( for HFRS patients p = 0 . 95 and for CCHF patients p = 0 . 82 ) or genders ( for HFRS patients p = 0 . 84 and for CCHF patients p = 0 . 34 ) in patients with hemorrhagic fevers ( supplement data , S2 and S3 Figs ) . To investigate whether HMGB1 concentration is elevated in patients with hemorrhagic fever , we compared HMGB1 levels between 72 patients with HFRS , 56 patients with CCHF and 61 healthy donors . Results show that HMGB1 concentrations were statistically significantly higher in patients with HFRS and CCHF than in healthy donors ( p<0 . 0001 , Fig 1 ) . The median concentrations were 11 . 6–times and 4 . 4–times higher in patients with HFRS and CCHF than in healthy donors ( Table 1 ) . The median concentration of HMGB1 was 2 . 6–times higher in patients with HFRS than in patients with CCHF , but the difference was not statistically significant ( p = 0 . 3 ) . These results indicated that HMGB1 concentration is elevated in patients with hemorrhagic fevers . To determine whether HMGB1 concentrations are higher in the severe form of the disease , we compared the levels in both groups . Concentration of HMGB1 was significantly higher in patients with the severe form of CCHF than in patients with the mild form of the disease ( p = 0 . 04 , Fig 2 ) , although the median HMGB1 concentration was only 1 . 7–times higher in patients with the severe form of the illness than in patients with the mild form of the disease ( Table 1 ) . These results show that patients with severe CCHF had significantly elevated levels of HMGB1 compared to patients with the mild form of the disease . To determine if there is a difference in concentration of HMGB1 between HFRS patients , we compared levels of HMGB1 in patients infected with DOBV and PUUV . The median HMGB1 concentration was 2 . 2–times higher in patients infected with PUUV than in patients infected with DOBV , but the difference was not statistically significant ( p = 0 . 4 , Fig 3a ) . Next , we assessed whether the HMGB1 concentrations were higher in patients with severe HFRS . The median HMGB1 concentration was 2 . 8–times higher in patients with severe DOBV infection than in patients with mild DOBV infection ( Table 1 ) , but the difference was not statistically significant ( p = 0 . 2 , Fig 3b ) . Comparison of patients with severe and mild PUUV infection showed high differences between groups . The median HMGB1 concentration was 19–times higher in patients with severe PUUV infection than in patients with mild PUUV infection and the difference was statistically significant ( p = 0 . 003 , Fig 3c ) . These results clearly showed that patients with severe PUUV infection had the highest HMGB1 concentration ( Table 1 ) .
Multiple studies have aimed to identify prognostic biomarkers for hemorrhagic fever in order to prevent a fatal outcome of the disease . It was found that CRP and PCT are elevated in patients with HFRS and CCHF , but these biomarkers are not specific and not sufficient to predict virus infection severity . However , recently it was shown that HMGB1 can be used as a biomarker for Dengue virus ( DENV ) diagnosis and disease prognosis [27] . In this study we focused our investigation on HMGB1 as a possible prediction biomarker for disease severity of HFRS and CCHF . Our investigation included patients’ samples from the acute phase of the disease , as previous reports and our results showed that HMGB1 concentrations are higher during the first days of the illness [26] . Our results indicated that age and gender distribution have no influence on concentration of HMGB1 in patients with hemorrhagic fevers . Obtained results indicated that concentrations of HMGB1 are higher in patients with HFRS and CCHF than healthy controls , however , they were similar between the two VHFs . Elevated levels of HMGB1 were anticipated in patients with hemorrhagic fevers , since it was shown that HMGB1 has a role in many diseases , including infectious viral diseases [23 , 24 , 25] . It was shown that released HMGB1 can function as a proinflammatory cytokine . According to that , our results indicated that HMGB1 is likely to be involved in the immunopathogenesis of hemorrhagic fevers . To determine the possible role of HMGB1 in the pathogenesis of studied VHFs and to ascertain its probable prognostic value , we compared concentrations of HMGB1 between patients with severe and mild CCHF . Our results demonstrated statistically higher levels of HMGB1 in patients with the severe clinical course . This implies that the level of HMGB1 might play an important role in the immunopathogenesis of CCHF and could probably be used for disease severity prediction . Furthermore , we compared patients with DOBV and PUUV infection , as it is generally believed that DOBV is mainly responsible for more severe HFRS cases . Patients infected with PUUV indicated higher levels ( although not statistically significant ) of HMGB1 than patients infected with DOBV . To determine , if the level of HMGB1 can serve as a prognostic marker in HFRS , we compared concentrations of HMGB1 between patients with severe and mild clinical courses . Patients with the severe DOBV infection had higher HMGB1 levels than patients with the mild course of the disease . Results were not statistically significant , which indicated that HMGB1 is not an appropriate biomarker for disease severity prediction in DOBV infections . On the contrary , our results demonstrated that HMGB1 can serve as a suitable biomarker for prognosis of severe PUU infections , considering that patients with severe PUUV infection had significantly higher levels of HMGB1 than patients with the mild disease form . Our results suggested that higher HMGB1 levels are a general feature of severe forms of viral hemorrhagic fevers . Patients with the severe PUUV infection indicated the highest HMGB1 level among HFRS and CCHF patients , although infection with DOBV and CCHFV usually cause more severe hemorrhagic manifestations than PUUV . One of the possible explanations for high levels of HMGB1 in severe PUUV infected patients could be the inhibition of an early innate immune response . Immunological studies revealed that pathogenic hantaviruses and CCHFV suppress the early IFN-β response . Ability of hantaviruses to inhibit innate immunity vary in their degrees of pathogenicity . It was shown that both DOBV and PUUV inhibit IFN-β induction , but the inhibition reported for PUUV was only 10–30% of IFN responses reduction [31] . On the other hand it was demonstrated that IFN-β and STAT-1 have critical and essential roles in HMGB1 release [32] . Additionally , it was shown that release of HMGB1 is dependent on the activation of the JAK/STAT1 pathway by IFN-β [33] . This could probably explain why HMGB1 levels are higher in patients with the severe PUUV infection , although DOBV causes a more severe HFRS than PUUV . Inhibition of IFN-β by DOBV virus and consequently lower levels of HMGB1 could be the reason why differences between severe and mild DOBV infections were not statistically significant . On the contrary , patients with severe CCHF indicated statistically significantly higher level of HMGB1 than patients with the mild infection , although it was shown that CCHFV suppresses IFN-β promoter mediated gene expression [8] . HMGB1 also plays an important role in hemorrhagic shock as it can be released passively into the extracellular milieu from damaging cells and functions as an inflammatory cytokine [34] . This indicates that abundant bleeding could be one of the possible reasons for elevated HMGB1 levels in patients with severe CCHF . Both , DOBV and CCHFV can cause severe forms of the disease with bleeding manifestations , however the initial manifestation in patients with severe CCHF more commonly progresses to large cutaneous ecchymoses and bleeding from the gastrointestinal and urinary tracts [30] . Nonetheless , HMGB1 has a broad repertoire of immunological functions and is involved in different pathways of immunity , inflammation and cancer progression . Additional in vivo and in vitro studies must be conducted to confirm all roles of HMGB1 in pathogenesis of VHFs . In conclusion , our study is the first to represent that HMGB1 levels are increased during hemorrhagic fevers caused by CCHFV , DOBV and PUUV . The work presented here indicates elevated HMGB1 levels in patients with severe HFRS and CCHF . Our research demonstrated potential use of HMGB1 as a biomarker for severity in PUUV and CCHFV infections . The induction and release of HMGB1 is very complex and is involved in different pathways of the host immune system . Because of immune-mediated response of HMGB1 further immunological studies are needed to clarify , whether HMGB1 can be used as a prognostic marker for hemorrhagic fever outcomes . | Dobrava virus ( DOBV ) , Puumala virus ( PUUV ) and Crimean-Congo Hemorrhagic fever virus ( CCHFV ) are important causative agents of viral hemorrhagic fevers . Clinical severity of Hemorrhagic fever with renal syndrome ( HFRS ) and Crimean-Congo Hemorrhagic fever ( CCHF ) varies greatly from a mild , nonspecific febrile illness to a severe form of the disease with hemorrhaging and high fatality rate . In this study we investigated the role of High Mobility Group Box 1 ( HMGB1 ) as a prognostic marker for the severe form of HFRS and CCHF . The study showed high concentration of HMGB1 in HFRS and CCHF patients in comparison with healthy donors . Even more , higher levels of HMGB1 were measured in patients with the severe form of diseases . Interestingly , patients with severe PUUV infection had the highest concentration of HMGB1 , although infection with DOBV and CCHFV usually causes more severe hemorrhagic manifestations . It is clear that HMGB1 has a broad repertoire of immunological functions and is involved in various pathways of immunity , inflammation and cancer progression , therefore also the induction by infection with PUUV , DOBV or CCHFV can influence different pathways . Nonetheless our research demonstrates a potential use of HMGB1 as an important biomarker for severity in PUUV and CCHFV infection . | [
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] | 2016 | HMGB1 Is a Potential Biomarker for Severe Viral Hemorrhagic Fevers |
Critical dynamics are assumed to be an attractive mode for normal brain functioning as information processing and computational capabilities are found to be optimal in the critical state . Recent experimental observations of neuronal activity patterns following power-law distributions , a hallmark of systems at a critical state , have led to the hypothesis that human brain dynamics could be poised at a phase transition between ordered and disordered activity . A so far unresolved question concerns the medical significance of critical brain activity and how it relates to pathological conditions . Using data from invasive electroencephalogram recordings from humans we show that during epileptic seizure attacks neuronal activity patterns deviate from the normally observed power-law distribution characterizing critical dynamics . The comparison of these observations to results from a computational model exhibiting self-organized criticality ( SOC ) based on adaptive networks allows further insights into the underlying dynamics . Together these results suggest that brain dynamics deviates from criticality during seizures caused by the failure of adaptive SOC .
In the terminology of physics , a system is said to be in a critical state if it is poised on a threshold where the emergent macroscopic behavior changes qualitatively . The hypothesis that the brain is operating in such a critical state is attractive because criticality is known to bring about optimal information processing and computational capabilities [1]–[4] . Recent experimental observations of patterns of neuronal activity exhibiting scale-free distributions , a typical hallmark of phase transitions , provided further evidence for this hypothesis . Bursts of neuronal activity were first shown in reduced preparations in rat brains to follow power-law probability distributions , termed neuronal avalanches [1] , [5] . More recently , neuronal avalanches were also observed in invasive recordings from monkeys and cats , strongly suggesting that criticality is a generic property of cortical network activity in vivo [6]–[8] . Additional evidence for the existence of a critical state in human brain dynamics comes from a recent study by Kitzbichler et al . [9] . Using magnetoencephalography ( MEG ) and functional magnetic resonance imaging ( fMRI ) , the authors found power-law probability distributions of two measures of phase synchronization in brain networks . As confirmed by computational models , these distributions show power-law scaling specifically when those model systems are in a critical state resulting in strong evidence that human brain functional systems exist in an endogenous state of dynamical criticality at the transition between an ordered and a disordered phase . Theory predicts local events to percolate through the system in the form of avalanches of activity at the critical state [10] . Such a critical state requires a homeostatic regulation of activity leading to a balance of excitation and inhibition in order to prevent states where events are either small and local or very large , engaging most of the network . A promising mechanism showing robust self-organized criticality ( SOC ) - the ability of systems to self-tune their operating parameters to the critical state - came from the discovery of network-based mechanisms , which were first reported in [11] and explained in detail in [12] , [13] . These works showed that adaptive networks , i . e . , networks that combine topological evolution of the network with dynamics in the network nodes [14] , can exhibit highly robust SOC based on simple local rules . In computational models it could be shown that realistic local mechanisms based on this adaptive interplay between network activity and topology are sufficient to self-organize neuron networks to a critical state , providing a plausible explanation of how criticality in the brain can be achieved and sustained [15]–[17] . A so far unresolved question concerns the medical relevance of critical brain activity . Diseases in the central nervous system are often associated with altered brain dynamics . It has been hypothesized that the dynamical properties characterizing a critical state may be seen as an important marker of brain well-being in both health and disease [18] . Epilepsy is a malfunction of the brain associated with abnormal synchronized firing of neurons during a seizure [19] . The increased collective neuronal firing during attacks has been speculated to be linked to a pathological deviation away from a critical state [20] . Evidence supporting this idea comes from recent in vitro studies of animal brains . There , application of receptor blockers could drive network dynamics away from its normal state where activity patterns of neuron dynamics deviated from a power-law [6] , [21] . Here , we confirm the previously observed power-law distribution of phase-lock intervals ( PLI ) with a complementary experimental methodology , providing additional evidence for the criticality hypothesis . Furthermore , we present evidence that human brain networks in vivo are not in a critical state during epileptic seizure attacks . Deriving the distribution of PLI from electrocorticogram ( ECoG ) data as an indicator of critical brain dynamics as proposed in [9] , we find that the system deviates from scale-free behavior during seizures . Combined with results from a computational model exhibiting SOC these observations suggest that dynamics of brain networks is typically close to criticality , but departs from the critical state during epileptic seizures . Together these results hint to the failure of adaptive SOC as a cause for seizure generation .
We investigated data sets from ECoG acquired during presurgical monitoring of patients suffering from focal epilepsy . Data were continuously sampled at 200 Hz ( patients 1–7 ) or 256 Hz ( patient 8 ) with the number of channels ranging from 30 to 45 for different patients . The time series recorded from the anatomical site where the epileptic focus was assumed typically included one or more neurographically-identifiable seizure attacks . To test brain dynamics for signatures of criticality we analyzed ECoG activity in different time windows . The data sets were split in intervals of 150 seconds length ( 30000 sample steps at 200 Hz sampling , 38400 in the case of 256 Hz ) with consecutive intervals overlapping by 100 seconds ( 20000 sample steps at 200 Hz , 25600 at 256 Hz ) . Following the approach in [9] , we determined the distribution of phase-locking intervals ( PLI ) as an experimentally accessible indicator of critical brain activity . The length of time windows was chosen to be long enough to give a reliable estimate of the distribution of PLI on the one hand and allow observation of its evolution in time on the other hand . For each of these sets , we calculated phase-lock intervals and determined their cumulative density distributions for scales 2 , 3 and 4 corresponding to frequency intervals 50–25 Hz , 25–12 . 5 Hz and 12–6 Hz for patients 1–7 ( P1–P7 ) and 64–32 Hz , 32–16 Hz , 16–8 Hz for patient 8 . The distributions for all scales closely follow a power-law probability distribution with during pre-ictal time intervals and between −2 and −3 . 5 . Statistical tests based on the Kolmogorov-Smirnov statistic and likelihood ratios [22] showed that the hypothesis of a power-law PLI distribution could not be rejected for most pre-ictal data sets , furthermore a recent comprehensive analysis of various fitting functions applied to PLI distributions had revealed a power-law to be the most likely fit [9] . The apparent robustness of the power-law against exact conditions ( different anatomical regions with varying number of channels ) strengthens the hypothesis of the relevance of a critical state in human brain dynamics . While the PLI distribution followed a power-law in time intervals preceding the seizure onset , a deviation from power-law behavior was observed in intervals containing the seizure attack . Figure 1 shows distributions of PLI derived from a pre-ictal , an ictal and a post-ictal time interval . The probability to find longer PLI increased during attacks thereby destroying the scale-free property of the original distribution . After the seizure this distribution slowly relaxed back to a power-law . In Figure 1 this relaxation is not yet complete in the post-ictal time interval as there is still some residual seizure dynamics in the ECoG recording . The qualitative change away from a power-law distribution during seizures could be observed in all 8 patients and across scales ( Figure 2 ) . Distributions for all consecutive time windows and all scales from patient 1 can be found in the supplementary material ( Figure S1 ) . A more quantitative estimate of the deviation from the pre-ictal state can be obtained by calculating , a measure previously proposed to characterize the divergence from a critical state [17] . This measure captures the deviation from a given empirical distribution from a power-law . The power-law fitted to the first ( pre-ictal ) interval was thereby taken as a reference and subtracted from the cumulative PLI distributions of subsequent time intervals . During time intervals preceding the seizure stayed at low values indicating no significant deviation from a power-law . In time windows containing seizure activity , increased to positive values , which is in agreement with the qualitative assessment from visual inspection showing a divergence from the initial distribution . After seizure attacks , a slow decrease of could be observed suggestive of a relaxation process back toward a power-law distribution ( Figure 3 ) . For obtaining further insights into the underlying dynamics of the power-law probability distribution of PLI and its absence during epileptic seizure attacks , we compared experimental results to a simple computational model exhibiting self-organized criticality . Our numerical results build on a model proposed by Bornholdt and Rohlf [12] . This model robustly self-organizes toward a critical state and is sufficiently simple to allow for an understanding of the underlying mechanism by which this self-tuning is accomplished . Specifically , the adaptive interplay of network dynamics and topology , a mechanism also at work in more elaborate models of SOC in neural networks [15]–[17] , robustly organizes systems parameters , in this case the average connectivity , toward values where the network's state is at a phase transition between ordered and disordered dynamics . For a network with nodes adaptive self-organization ( aSO ) leads the average connectivity to settle at values around 2 . 55 independent of initial conditions ( Figure 4A ) . The frozen component defined as the fraction of nodes that do not change their state along the attractor undergoes a transition at this self-organized connectivity ( Figure 4B ) . In the large system size limit the networks evolve to a critical connectivity where the transition from the frozen to the chaotic phase becomes a sharp step function [12] . The system therefore exhibits a phase-transition with a frozen/ordered phase at lower connectivities and a disordered phase of network dynamics at higher connectivities . Our goal was to compare the distribution of PLI at the self-organized connectivity and at connectivities below and above it . This would correspond to critical dynamics as well as dynamics in the ordered/frozen and disordered phase respectively . We therefore let the network evolve according to the adaptive self-organization ( aSO ) process described in [12] and the methods section ( iterations 0–8000 ) and derived PLI of 100 consecutive iterations at some point when the average connectivity had settled around . There , the distribution of PLI appeared to follow a power-law ( Figure 4C ) . More precisely , statistical tests [22] revealed that the hypothesis of a power-law for the distribution of PLI cannot be rejected at the self-organized connectivity . Next , we switched the aSO off at 8000 iterations , instead adding and deleting links with a certain probability independent of node activity after this point ( iterations 8001–12000 ) . We considered two cases: First , where links were added with probability and deleted with and second , where links were added with and deleted with after each iteration following the first 8000 iterations . In the first case more links were effectively added so that the average connectivity organized to higher values . The second case led to a net decrease in links resulting in a lower average connectivity ( Figure 4C ) . We again derived PLI of 100 consecutive iterations at some time for each of the two cases . In both cases the distribution of PLI deviated from a power-law consistent with a state away from critical dynamics ( Figure 4C ) . The distribution at connectivities corresponding to the ordered phase of network dynamics is shifted towards larger PLI similar to the one observed during epileptic seizure attacks ( bottom right in Figure 4C ) . The close agreement between patient and model data suggests that the deviation from a power-law observed during epileptic seizure attacks indicates a shift of dynamics toward an ordered phase . In the model above this corresponds to the phase of frozen dynamics . It further hints that it is the mechanism of adaptive SOC , the ability to tune system parameters to values where network dynamics is at a phase transition and PLI are distributed according to a power-law , that could fail during epileptic seizure attacks in neuron networks in the brain .
The relevance of critical brain dynamics is currently a heavily debated topic . Indirect evidence for such a state comes from power-law distributed observables in neurophysiological data . Power-laws can arise through various mechanisms such as the combination of two exponential distributions or random extremal processes such as the Omori law for earthquake aftershocks for example [23] . With respect to neural dynamics power-law behavior can be generated by filtering properties of neural tissue [24] . Although various mechanisms can result in an event size distributions exhibiting power-laws [25] , such distributions also arise when a system is in a critical state [10] . The observation of power-laws therefore provides an indication but not a proof of critical dynamics . Conversely , the absence of power-law scaling would provide a strong evidence against criticality . The power-laws observed in neural data are consistent with the hypothesis of neural criticality . The hypothesis is further supported by a ) evolutionary arguments highlighting the advantages of operating in a critical state [26] and b ) the formulation of fairly realistic models [13] , [15]–[17] explaining how a critical state can be reached as a result of well-known neural and synaptic mechanisms . Comparison of experimental data to data from a computational models known to exhibit critical dynamics can provide support for the conclusion that an experimentally observed power-law is a signature for critical dynamics . Recently , the power-law distribution of phase-lock intervals between pairs of neurophysiological time series was shown to be a specific hallmark of dynamic criticality in human brain dynamics [9] . Following the line of arguments outlined above , the authors demonstrated power-law scaling of PLI both in neurophysiological data and also in Ising and Kuramoto model when these systems were tuned to a phase transition . In this work we extend these computational results to a third model known to exhibit SOC [12] . Together these numerical results provide strong support that the observed power-law distribution of PLI is characteristic for a system at a phase transition between ordered and disordered dynamics . Using this indicator on ECoG data , a complementary experimental methodology to [9] , we confirm the previously observed power-law distribution of PLI , providing additional evidence for the criticality hypothesis . Secondly , we show that the critical state is disturbed during epileptic seizure attacks . More precisely , the distribution of the PLI synchronization measure deviates from a power-law , characterizing the critical state of normal neuronal dynamics , during epileptic seizures , providing the first direct evidence of disturbed critical dynamics related to a pathology in vivo . Our findings support the notion of a physiological default state of balanced brain dynamics between regimes of exuberant and frozen activity . Physiological neuronal activity is characterized by intermittent periods of synchronization between different anatomical regions . In terms of dynamical system's theory , such a state corresponds to a critical state at a phase transition between order and disorder . A deviation from this balanced state toward dynamics with pathologically increased times of synchronous activity as observed in epileptic patients leads to a deviation from the physiological critical state resulting in impaired functionality . Optimal information processing capabilities of neuron networks have been related to a critical state before [1] , [26] , [27] . The requirement for such functional capabilities could be differently pronounced in different brain networks and at different times . From this perspective , it is very unlikely that all regions in the brain are tuned to a critical state at all times . Our results in fact show that the goodness of the power-law in the PLI distribution varies between different regions and times ( see for example the rather poor power law of P7 in Figure 2 ) . One could speculate that self-organization to a critical state is differently pronounced in distinct anatomical regions perhaps dependent on distinct functional requirements . A mechanism by which complex networks can self-organize toward a critical state is based on the adaptive interplay between the dynamics on the network , i . e . neuronal activity , and the dynamics of the network , i . e . the shaping of synaptic connections . Through this interaction system parameters can be locally tuned to a state of global criticality [12] , [13] . While the simple model described in this work captures these essential ingredients allowing for an understanding of the underlying concept , more elaborate mechanism can be expected to be at work in real-world neuron networks [15]–[17] , [28] . It is conceivable that physiological neuron networks in the brain tune their parameters to more than one parameter to reach a state of criticality . Besides the average connectivity of the network , the balance between excitation and inhibition , for example , has been shown to be an important parameter to sustain a homeostatically balanced critical state and prevent regimes of overly synchronized activity . The robust mechanism of adaptive SOC allows neuron networks in the brain to maintain close to a critical state characterized by dynamics exhibiting power-law probability distributions even while network dynamics and topology undergo changes . Along this line of arguments the deviation from a power-law distribution of PLI reported here can be interpreted as a shift away from a balanced critical state and to our knowledge constitutes the first proof of impaired critical dynamics related to a pathology in vivo . This observation is supported by experimental results from in vitro studies . The application of receptor blockers in slice preparation of animal brains resulting in an excess of excitation in the network destroyed the power-law distributed avalanches of neuronal activity and led to increased avalanche sizes corresponding to a super-critical state [5] , [6] . Analogously , human tissue removed from epilepsy patients exhibited abnormally regulated avalanches with periods of hyperactivity [29] . In summary , experimental results from in vitro experiments [5] , [6] and in vivo observations presented here combined with insights from computational models based on adaptive SOC [12] , [15]–[17] , [28] suggest the failure of the adaptive interplay between neuron activity and network topology to lead to the deviation from a critical state . There pathological , in the case of epilepsy overly synchronized , activity patterns are observed . A deviation from the default critical state towards a dynamical regime with decreased phase-locking is also conceivable . For instance in neurodegenerative diseases with impaired neuronal connectivity , the deviation from a power-law of PLI could potentially be used to identify and characterize these pathological conditions .
Eight patients undergoing surgical treatment for intractable epilepsy participated in the study . Patients underwent a craniotomy for subdural placement of electrode grids and strips followed by continuous video and ECoG monitoring to localize epileptogenic zones . Solely clinical considerations determined the placement of electrodes and the duration of monitoring . Positions of the electrodes from patients 1–7 can be found in the supplementary material ( Figure S2 ) . All patients provided informed consent . ECoG signals were recorded by the clinical EEG system ( epas 128 , Natus Medical Incorporated ) and bandpass filtered between 0 . 53 Hz and 70 Hz . Data were continuously sampled at a frequency of 200 Hz ( patients 1–7 ) and 256 Hz ( patient 8 , [30] ) . The study protocols were approved by the Ethics Committee of the Technical University Dresden . To derive a scale-dependent estimate of the phase difference between two time series , we follow the approach described in ref . [9] using Hilbert transform derived pairs of wavelet coefficients [31] . We define the instantaneous complex phase vector for two signals and as: ( 1 ) where denotes the -th scale of a Hilbert wavelet transform and its complex conjugate . A local mean phase difference in the frequency interval defined by the -th wavelet scale is then given by ( 2 ) with ( 3 ) being a less noisy estimate of averaged over a brief period of time [9] . Intervals of phase-locking can then be identified as periods when is smaller than some arbitrary threshold which we set to here . We also require the modulus squared of the complex time average , , to be greater than 0 . 5 , limiting the analysis to phase difference estimates above this level of significance . To quantify the deviation from a power-law we defined a measure similar to ref . [17] . measures the difference between the cumulative density distribution of phase-lock intervals and a theoretical power-law distribution obtained from a fit of the experimental data [22] . is calculated from the first time-interval ( 0–150 seconds ) of a data set . For each time-interval of 150 seconds duration , is then subtracted from the cumulative density distribution of PLI , , for each data point corresponding to a phase-lock interval and normalized by the number of data points : ( 4 ) Positive values of indicate a deviation with increased intervals of phase-locking , negative values indicate decreased phase-locking compared to the reference power-law distribution . An influential model explaining how dynamical systems can self-organize towards a critical state was introduced in ref . [12] . The mechanism is based on the adaptive interplay between the dynamics of the nodes in the network ( dynamics on the network ) and the rewiring of the network's topology ( dynamics of the network ) . More precisely , the topology of the network is changed according to the activity of the nodes in the network so that on average active nodes lose links and frozen nodes grow links . This local rewiring leads to a robust evolution towards a critical connectivity where the system is at a phase transition between order and disorder [12] . We first instantiated this model in a network of 1024 randomly interconnected binary elements with states which are updated in parallel and scanned for local rewiring of connections . After 1000 time steps the network's topology was updated by picking one random node and either adding/deleting an incoming link to it depending on its activity during the last 1000 time steps [12] . This process was iterated many time leading the network's topology to evolve towards a critical connectivity of . Our objective was to compare the distribution of phase-lock intervals between the activity of pairs of nodes for different average connectivities to provide a reference for comparable analysis of neurophysiological time series . We therefore monitored states of 20 randomly chosen nodes in a network after it had self-organized to the critical connectivity and derived the distribution of PLI . To organize the network away from we added and deleted links solely based on probability , independent of node activity after 8000 iterations . Dependent on the probalitiy with which links were added/deleted the average connectivity organized to higher/lower values at which we again monitored the states of 20 randomly chosen nodes and derived the distribution of PLI . We found that the probability distribution of phase-lock intervals demonstrated power-scaling specifically when the system was at the self-organized critical connectivity whereas distributions at lower/higher connectivities deviated from a power-law showing periods of increased/decreased phase-locking . | Over the recent years it has become apparent that the concept of phase transitions is not only applicable to the systems classically considered in physics . It applies to a much wider class of complex systems exhibiting phases , characterized by qualitatively different types of long-term behavior . In the critical states , which are located directly at the transition , small changes can have a large effect on the system . This and other properties of critical states prove to be advantageous for computation and memory . It is therefore suspected that also cerebral neural networks operate close to criticality . This is supported by the in vitro and in vivo measurements of power-laws of certain scaling relationships that are the hallmarks of phase transitions . While critical dynamics is arguably an attractive mode of normal brain functioning , its relation to pathological brain conditions is still unresolved . Here we show that brain dynamics deviates from a critical state during epileptic seizure attacks in vivo . Furthermore , insights from a computational model suggest seizures to be caused by the failure of adaptive self-organized criticality , a mechanism of self-organization to criticality based on the interplay between network dynamics and topology . | [
"Abstract",
"Introduction",
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] | [
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] | 2012 | Failure of Adaptive Self-Organized Criticality during Epileptic Seizure Attacks |
A coarse-grain computational method integrates biophysical and structural data to generate models of HIV-1 genomic RNA , nucleocapsid and integrase condensed into a mature ribonucleoprotein complex . Several hypotheses for the initial structure of the genomic RNA and oligomeric state of integrase are tested . In these models , integrase interaction captures features of the relative distribution of gRNA in the immature virion and increases the size of the RNP globule , and exclusion of nucleocapsid from regions with RNA secondary structure drives an asymmetric placement of the dimerized 5’UTR at the surface of the RNP globule .
Electron microscopy of mature HIV-1 shows a condensed ribonucleoprotein ( RNP ) complex [1] packaged within the cone-shaped capsid , which is thought to include genomic RNA , nucleocapsid , integrase , transfer RNA , reverse transcriptase , and other components [2] . It is formed through a complex , multistep process where genomic RNA ( gRNA ) associates with a lattice of Gag polyproteins at the cell surface , which buds from the surface to form an immature virion , followed by proteolytic cleavage of Gag into capsid , nucleocapsid , integrase and other viral proteins , and finally condensation and encapsidation of the mature RNP within the capsid ( Fig 1 ) . Understanding of the supramolecular and molecular details of this RNP is important , since it must undergo large structural transitions over the course of the viral life cycle , including playing a central role in assembly of Gag proteins into new viruses and transition from single-stranded RNA to double-stranded DNA during reverse transcription . The HIV-1 RNP has been studied by many complementary experimental methods . As revealed by SHAPE and other methods , HIV-1 genomic RNA dimerizes and has an extensive secondary structure [4 , 5] , including a well-documented structure at the 5’ untranslated region ( 5’UTR ) [6] . Nucleocapsid coats the gRNA at a density of about 1 nucleocapsid per 11–12 nucleotides and plays key roles as a chaperone in reverse transcription and other processes [7] . A recent study took advantage of the extreme radiation sensitivity of nucleocapsid , which causes formation of bubbles in tomograms , to localize nucleocapsid in the RNP and further show a preference for localization in the dense condensate at the large end of the capsid [8] . Studies on the mode of action of ALLINI compounds ( allosteric integrase inhibitors ) have revealed that integrase is also essential for maturation of the RNP , and crosslinks the RNA in the mature virion [9] . As part of our ongoing work to study the mesoscale structure of HIV virions at various stages of the viral life cycle , we have developed coarse-grain models of the mature HIV-1 RNP to explore its formation and characteristics , integrating experimental results from electron microscopy , structural biology , CLIP-Seq and SHAPE . Coarse-grain models have been instrumental in understanding of mesoscale-level protein interactions in HIV structure and maturation , including models of the structure of immature virion [10] and formation of the cone-shape capsid [11 , 12] . In particular , these studies reveal the process whereby gag protein in immature virions assembles into an imperfect hexagonal lattice . CryoEM studies have further revealed that this hexagonal lattice covers roughly 2/3 of the inner surface of the immature virion , with scattered defects [13] . In the work reported here , we interpret these results with a quasisymmetrical model of gag in immature virions , where the defects correspond to missing gag hexamers at the pentagonal sites in the quasisymmetrical lattice . We then generate models of the mature RNP that explore several alternatives for how the gRNA is deposited onto this immature gag lattice , and how much of this immature virion structure is retained when the RNP matures . Our RNP models include three elements: two copies of 9 kb gRNA , 2000 nucleocapsid proteins , and 140 integrase subunits . The coarse-grain method begins with several assumptions for the structure of the genome within the immature virion , folds the 5’UTR based on experimentally-determined base-pairing interactions , and condenses the RNA through interaction with nucleocapsid and integrase to form a mature RNP .
The coarse-grain modeling method used here builds on previous lattice-based methods for modeling bacterial nucleoids [14] . Briefly , the method begins with an initial geometric model of the gRNA built within a sphere representing the Gag polyprotein lattice in the immature virion , and assigns integrase crosslinking sites based on proximity of the experimentally-determined binding sites . The model is then condensed , driven by the weak attraction of nucleocapsid and gRNA , while being constrained by integrase crosslinks and steric bulk of nucleocapsid . Three initial gRNA configurations were tested , based on three different hypotheses for the capture and maturation of the gRNA . “Self-avoiding Gag” models deposit the two gRNA strands on the inner surface of the immature Gag lattice in a self-avoiding random walk , assuming that integrase crosslinking occurs soon after release of the gRNA . This model explores the hypothesis that local features of the RNA placement on the Gag lattice may be retained as the complex condenses . “Overlapping Gag” models similarly deposit the two gRNA strands on the Gag lattice , but allow them to overlap . “Random” models assume that the RNA is released in the immature virion and is randomly distributed within the available volume before condensation . Integrase is known to adopt multiple oligomeric states [15] , so separate models with integrase tetramers and with integrase dimers were tested , as well as models without integrase . Coarse-grain models of the mature HIV-1 RNP show a uniform , condensed form ( Fig 2 ) . Volumes decrease by roughly a third to a quarter relative to the initial models ( Table 1 ) . The three initial configurations ( Random , Self-avoiding Gag , and Overlapping Gag ) yield similar volumes of the final models . As seen in Fig 2 , the RNP globule easily fits within the experimentally-determined structure of the capsid , and matches closely images of the intact capsid from electron microscopy ( see , for example , [1] ) . Models with integrase tetramers showed the greatest volume , followed by globules with integrase dimers , and models with no integrase were smallest . Control experiments varying the number of dimers and tetramers , and comparing the default square-planar model of integrase with a tetrahedral model , show that the difference in size is a direct consequence of the volume occupied by the integrase ( Table 1 ) . For example , the default square planar model for the integrase tetramer includes two steric spheres with 9 . 2 nm diameter that may overlap , so the total volume of 35 integrase tetramers would be in the range of 14300 to 28500 nm3 . In the Self-avoiding Gag model , adding these values to the model with no integrase gives a range of 66600–80800 nm3 , which is consistent with the model with integrase tetramer at 70500 nm3 . As a control , we also created models using tetrahedral integrase tetramers composed of two dimers , each using the same representation used for the dimer models ( 6 . 5 nm diameter spheres ) . The tetrahedral tetramer model shows a volume of 61200 nm3 , similar to the model with integrase dimers at 61900 nm3 . Finally , we did an experiment that would be consistent with rapid exchange of nucleocapsid , by creating the condensed model of RNP without NC , and then choosing IN positions in the condensed globule . The resultant globule ( 69800 nm3 ) showed a similar volume as the globule with integrase crosslinking throughout condensation ( 70500 nm3 ) . Contact probability plots ( Fig 3 ) reveal subtle differences between the three types of models , and these are further quantified by plotting the average value of the contact probability as a function of the separation of nucleotides within or between chains ( Fig 4 ) . Models built from the Random configurations show a random sprinkling of interactions between all regions of the gRNA , and a similar distribution of contacts between the two chains . Regions immediately adjacent to the diagonal are sparsely populated , showing the lack of organization at small scales , such as plectonemic supercoils or hairpins . Conversely , models with the Self-avoiding Gag configuration show more interaction near the diagonal . This is expected , since the contact probability of a random chain decays less rapidly with loop length when constrained in 2D compared with 3D [16] . Cross-chain interactions , on the other hand , are reduced due to the self-avoiding definition of the model . Models starting from the Overlapping Gag configurations show a reversed nature: the interaction along the diagonal is slightly reduced when compared to models starting with the Self-avoiding Gag configuration , but the cross-chain interactions are enhanced , presumably due to the many points of close proximity between chains in the initial model . As expected , all three models show a strong interaction at the 5’UTR , due to the modeled secondary structure . A subtle band of reduced interaction extends horizontally and vertically from the diagonal at the 5’UTR , indicating that the 5’UTR forms fewer than expected interactions with the rest of the chains . As shown below , this is a consequence of a general exclusion of the 5’UTR from the body of the globule . We also calculated contact probability plots that are averaged over the two strands , which are more indicative of results that may be obtained from a hypothetical high-resolution contact experiment ( Fig 3B ) . The underrepresented band extending from the 5’UTR is distinguishable , but the more global features that differ between the three models are not as distinguishable between the three plots . Given the coarse-grain nature of the model , the hairpin loops in the 5’UTR do not show a typical double helical structure , but rather end up looking more like distorted bobby pins ( see the examples in Fig 5 ) . We noticed early in this study that the secondary structure has a consequence on the global nature of the RNP: during the process of condensation , the 5’UTR is excluded from the body of the globule and often ends up on the surface . We quantified this exclusion with a simple metric , by evaluating the average radial distance of nucleotides in the 5’UTR as compared with the rest of the gRNA and the sites of integrase interaction ( Table 2 ) . The central column of this table shows that the average radius of the bulk of the RNA is fairly constant across the default model and several controls , with models with IN tetramers at just over 18 nm , and models with IN dimers or no IN slightly smaller . The right column shows that the IN-binding sites show a similar trend across the default and control models . The 5’UTR shows different behavior , however , based on the assumptions made for the nature of region . Several control experiments help to identify the cause of the exclusion . We tested two hypotheses: the role of NC , and the role of the secondary structure itself . In the default experiments ( “FullModel” in Table 2 ) , NC is excluded from the 5’UTR ( apart from one site determined experimentally ) , providing less attractive force for the condensation , resulting in eccentric location of the 5’UTR and larger radial average values . When hypothetical models are condensed with NC covering the 5’UTR as well as the rest of the gRNA ( “FullAllNC” in Table 2 ) , the 5’UTR instead is located in the interior of the globule , showing smaller radial average values . This is expected , since the local concentration of nucleocapsid is higher due to the close proximity of the RNA chains when they base pair . When the two RNA chains are treated as separate chains , with no secondary structure , they show a similar behavior with respect to NC . If NC is excluded from the 5’UTR ( “TwoChain” in Table 2 ) , the 5’UTR tends to pack on the surface of the globule , and if NC is equally distributed ( “TwoAllNC” in Table 2 ) , the 5’UTR has the same properties as the rest of the chain . From these experiments , we conclude that NC is the primary cause of the exclusion of the 5’UTR under the assumptions made by our modelling method .
One of the major goals of this work is to create plausible models of the RNP to identify experimental modalities that could distinguish between different hypotheses for the effect of integrase crosslinking on the final form of the RNP . Note that our protocol is not meant to simulate the process of condensation , rather , it is designed to provide a rapid method for generating multiple models that are consistent with the available data defining the nature of the RNP . The three types of models tested here ( Self-avoiding Gag , Overlapping Gag , and Random ) are designed to explore different hypotheses for the initial structure of the gRNA and the possibility that IN crosslinking could trap features of this structure in the condensed RNP . Contact probably plots reveal differences in the arrangement of chains in the mature RNP , with the self-avoiding model showing stronger partitioning of the two chains in different regions of the RNP . The study also reveals that , because the two gRNA chains are identical , these differences would be difficult to quantify in a hypothetical Hi-C type experiment ( Fig 3B ) . Two additional morphological features are revealed in these models , which may be accessible to study by cryoEM or super-resolution microscopy . First , crosslinking by integrase leads to a larger condensed globule . Control experiments revealed that this increase in size is primarily due to the steric bulk of the incorporated integrase subunits . However , the model for integrase used here is very simple , with no constraints on the relative orientation of the gRNA strands that are bound . We might expect that the condensed globule may be larger if integrase is more rigid than modeled here , with reduced mobility in the linker to the RNA-binding C-terminal domain and consequently stronger constraints on the orientation of the gRNA binding sites . Unfortunately , there currently are no convincing models of integrase structure at this stage in viral life cycle , but based on available structures of integrase dimers and intasomes ( see Methods ) , we might expect that the connection to the C-terminal domains is quite flexible . Our control experiments suggest that we would not expect a smaller condensed globule if integrase is found to exchange sites rapidly during the process of condensation . The second emergent feature of the models is the exclusion of the 5’UTR from the bulk of the RNP globule . Control experiments implicate the binding propensity of NC for single-stranded nucleic acids [17] as the cause of this exclusion . Exposure of the 5’UTR might be expected to have functional consequences , for example , by allowing ready access to reverse transcriptase for the initiation of genomic DNA synthesis . The current model includes only gRNA , nucleocapsid and integrase , and secondary structure only in the 5’UTR . There are many opportunities for future studies to explore additional functional features and their emergent effects on RNP globule structure and partitioning . These will include a more detailed study of secondary structure , starting first with the Rev response element and extending to more detailed models as defined by SHAPE data . In addition , other molecules , such as transfer RNA and reverse transcriptase , are known to interact with the gRNA and could have effects on the form and function of the RNP . We are also currently developing methods for generating full atomic models from these coarse-grain representations , for use both in simulation and educational outreach .
A lattice-based approximation of the convex hull was used to estimate the volume of RNP globules , and calculated similarly to previous work [14] . The average radial distance of all beads , or a selected subset of beads , relative to the center of gravity was used as a metric to quantify both compactness of a globule and asymmetry in placement of selected portions of the RNA chains . Contact probability plots were calculated by counting the number of contacts within a given threshold ( here , 10 nm ) within a given sequence bin , across all 25 instances of a particular model . Nine sets of models of the condensed HIV-1 RNP , using the three hypotheses for the starting model , and with integrase tetramers , dimers or no integrase , are available at https://zenodo . org/record/2662964 . Open-source software and input data files for generating models with the self-avoiding Gag hypothesis are available on GitHub at https://github . com/dsgoodsell/HIVnucleoid . | The genome of HIV-1 is composed of two strands of RNA that are packaged in the mature virion as a condensed ribonucleoprotein complex with nucleocapsid , integrase , and other proteins . We have generated models of the HIV-1 ribonucleoprotein that integrate experimental results from multiple structural and biophysical experiments , exploring several hypotheses about the state of the RNA before condensation , and the role of crosslinking by integrase . The models suggest that the 5’UTR , which shows extensive secondary structure , has a propensity to be placed on the surface of the condensed globule , due to reduced binding of nucleocapsid to double-stranded regions within the 5’UTR . This unexpected localization of the 5’UTR may have consequences for the subsequent structural transitions that occur during the process of reverse transcription . | [
"Abstract",
"Introduction",
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] | [
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] | 2019 | Integrative modeling of the HIV-1 ribonucleoprotein complex |
Multiple studies show that tumor suppressor p53 is a barrier to dedifferentiation; whether this is strictly due to repression of proliferation remains a subject of debate . Here , we show that p53 plays an active role in promoting differentiation of human embryonic stem cells ( hESCs ) and opposing self-renewal by regulation of specific target genes and microRNAs . In contrast to mouse embryonic stem cells , p53 in hESCs is maintained at low levels in the nucleus , albeit in a deacetylated , inactive state . In response to retinoic acid , CBP/p300 acetylates p53 at lysine 373 , which leads to dissociation from E3-ubiquitin ligases HDM2 and TRIM24 . Stabilized p53 binds CDKN1A to establish a G1 phase of cell cycle without activation of cell death pathways . In parallel , p53 activates expression of miR-34a and miR-145 , which in turn repress stem cell factors OCT4 , KLF4 , LIN28A , and SOX2 and prevent backsliding to pluripotency . Induction of p53 levels is a key step: RNA-interference-mediated knockdown of p53 delays differentiation , whereas depletion of negative regulators of p53 or ectopic expression of p53 yields spontaneous differentiation of hESCs , independently of retinoic acid . Ectopic expression of p53R175H , a mutated form of p53 that does not bind DNA or regulate transcription , failed to induce differentiation . These studies underscore the importance of a p53-regulated network in determining the human stem cell state .
Embryonic stem cells ( ESCs ) have an unlimited potential to proliferate ( self-renewal ) and the ability to generate and differentiate into most cell types ( pluripotency ) [1] , [2] . The undifferentiated ESC state is regulated by a network of transcription factors , e . g . , OCT4 , SOX2 , NANOG , and KLF4 , and epigenetic modifiers , which promote expression of ESC-specific genes and suppress differentiation [3]–[7] . Exogenous introduction of transcription factors such as OCT4 , SOX2 , NANOG , and KLF4 into murine or human adult cells induces pluripotency by reprogramming these cells into induced pluripotent stem cells ( iPSCs ) , which are functionally and phenotypically similar to ESCs [8] , [9] . The ability of ESCs to self-renew and maintain pluripotency is linked to the ability of these cells to remain in a proliferative state . ESCs progress through an abbreviated cell cycle , leading to rapid cell division [10]–[12] , characterized by a truncated G1 phase , elevated expression of G1-associated cyclins , active cyclin-dependent kinases ( CDKs ) , and low levels of inhibitory cell cycle proteins p21 , p27 , and p57 [13] . During differentiation to embryoid bodies , mouse ESCs ( mESCs ) accumulate in G1 and exhibit a cell cycle lengthened from 8–10 h to more than 16 h , as observed in adult cells [14] . In the creation of iPSCs , multiple studies show more efficient reprogramming of cells with dysfunctional ARF/p53 pathways and increased cellular proliferation , shortening of G1 , and lack of cell cycle checkpoints [15]–[20] . Additional studies identified several small non-coding RNAs that play roles in cell cycle regulation and control of ESC status [21] . MicroRNAs ( miRNAs ) are small , non-coding RNAs of 21–23 nucleotides in length that regulate gene expression , generally at a post-transcriptional level [22] . Specific miRNAs regulate self-renewal , pluripotency , and mESC stability [23] , [24] , and several are differentially expressed in human ESCs ( hESCs ) [25] , [26] . Here , we connect both regulatory arms , e . g . , cell cycle progression and transcription of miRNAs , to tumor suppressor p53 in regulated differentiation of hESCs . Exposure of hESCs to differentiating conditions signals an acetylation switch to stabilize p53 protein . Activation of p53 elongates the G1 phase of the cell cycle by p21 induction , and increases miR-34a and miR-145 , which target specific stem cell factors for repression . These functions of p53 are direct , as ectopically expressed p53 binds these chromatin targets and causes spontaneous differentiation without retinoic acid ( RA ) addition . The combined effects of p53 not only antagonize self-renewal and pluripotency but also have an active role in promoting differentiation of hESCs .
In vitro differentiation of hESCs ( WA09 cells ) , induced by addition of RA and withdrawal of fibroblast growth factor ( FGF ) , is marked by a steady decline in levels of proteins and RNAs associated with pluripotency and self-renewal , e . g . , NANOG , OCT4 , SOX2 , and KLF4 , and increased expression of endodermal marker GATA4 , AFP , ectodermal marker PAX6 , and neural progenitor gene Nestin ( Figures 1A–1C and S1A ) , as previously described [27] . In parallel , p53 protein levels increase significantly but transiently ( Figure 1B and 1C ) without increased TP53 transcription ( Figure 1D ) . In response to RA , induced p53 is nuclearly localized in differentiating cells ( Figures 1E , 1F , and S1B ) , which are identifiable by the loss of homogeneity and elongated nuclei seen in highly p53-expressing cells ( Figure 1E ) . As one of several examples where hESCs differ from mESCs [28] , [29] , p53 is expressed at low levels and nuclearly enriched in hESCs prior to induction ( Figures 1B , 1E , and S1C ) . Transient induction of p53 protein levels during RA-mediated differentiation was also observed in WA01 hESCs ( Figure S1D and S1E ) , and in BMP4-mediated differentiation of hESCs ( data not shown ) . Stress activation of p53 is primarily attributed to post-translational modification of p53 and increased protein stability [30] . During RA-mediated differentiation of hESCs , p53 gained acetylation at residue lysine 373 ( p53K373; Figures 2A , 2B , and S2A ) , and DNA-damage-associated modifications , such as phosphorylation of p53S15 or p53S46 , were not observed ( Figure S2A ) . p53K373 is a known substrate of histone acetyltransferase CBP/p300 [31] , and treatment of differentiating hESCs with CBP/p300 inhibitor circumin [32] led to loss of p53K373ac and p53 stability during differentiation ( Figure 2D ) . Increased p53K373ac occurred in parallel with reduced levels of SIRT1 , at days 1–3 of RA treatment , suggesting that a pool of p53 escapes deacetylation by SIRT1 ( Figure 2C ) . NAD+-dependent histone deacetylase SIRT1 is down-regulated during differentiation of hESCs , as described previously [33]; however , SIRT1 protein levels and p53 interaction recover after differentiation of hESCs ( day 4 , Figure 2C ) , as p53 and p53K373ac are restored to low levels ( Figures 1 and 2A ) . Addition of an inhibitor of SIRT1 activity , nicotinamide [34] , on day 4 of RA treatment , maintained p53K373ac ( Figure 2D ) . These results suggest that an active acetylation/deacetylation switch regulates p53 during differentiation of hESCs . Pluripotent hESCs have low p53 levels ( Figures 1 and S1C ) , similar to somatic cells , where p53 levels are regulated by E3-ubiquitin ligases , ubiquitin modification , and proteasomal degradation [35] , [36] . HDM2 , which is embryonicly lethal when deleted ( as mdm2 ) in mice [37] , and TRIM24 , a negative regulator of p53 identified in mESCs [38] , are associated with p53 in pluripotent hESCs but dissociate after RA addition ( Figure 2E ) . Ubiquitin-modified p53 species are detectable at 0–24 h of RA treatment in the presence of inhibitors of the proteasome , MG132 ( lanes 2 and 4 , Figures 2F and S2B ) and lactacystin ( Figure S2C ) . After RA treatment , ubiquitin-modified p53 species decreased in abundance with time of differentiation: compare ( lanes 5 and 6 , Figures 2F , S2B , and S2C ) . As a positive control for regulated loss of ubiquitin-modified p53 [39] , hESCs were treated in parallel with the DNA-damaging agent adriamycin ( Adr ) ( Figure 2F , lane3 , and Figure S2B and S2C ) . Together , gain of acetylation and loss of ubiquitination transiently increased p53 stability during hESC differentiation . DNA damage of mESCs , where p53 is expressed at high levels and is primarily cytoplasmic , leads to repression of NANOG and spontaneous differentiation into other cell types , which undergo p53-dependent apoptosis [28] , [40] , [41] . However , a previous report shows that , unlike mESCs , exposure of hESCs to DNA damage induces p53-dependent cell cycle arrest rather than differentiation [42] . In order to assess functions of p53 during differentiation of hESCs we performed flow cytometry analysis of the cell cycle in hESCs at time points of exposure to RA , and compared control and p53-depleted hESCs ( Figure 3 ) . We efficiently depleted p53 , and other targets , with pools of small interfering RNA ( siRNA ) and a modified siRNA transfection protocol ( see Materials Methods for details ) that had an average 60% transfection efficiency and that produced an up to 80% reduction in RNA expression ( Figure S3 ) . Flow cytometry showed that 60% of pluripotent hESCs are in S phase , and approximately 10% of hESCs are in G1 ( time = 0 ) , consistent with a rapid cell cycle ( 15–16 h ) due to truncation of G1 [13] ( Figures 3A and S4A ) . During differentiation , hESCs spend increased time in G1 , slowing down cell cycle over time with RA treatment: at day 4 there is a 3-fold increase in cells in G1 ( Figure 3A ) . The accumulation of hESCs in G1 continued during differentiation; after 10 d of RA treatment , hESCs attained a cell cycle profile more similar to that of differentiated cells ( human foreskin fibroblasts ) , with more than 60% of cells in G1 ( Figure S4B ) . When hESCs were depleted of p53 by siRNA and exposed to RA , the accumulation of cells in G1 was attenuated , indicating that p53 plays an integral role in the process ( Figures 3A and S4A ) . The accumulation of hESCs in G1 during differentiation stands in contrast to DNA damage , which led to a p53-dependent arrest at the G2–M transition and apoptosis with exposure to damage-inducing levels of Adr ( Figure S4C–S4E ) . Cell cycle arrest in G1 phase may be mediated by cyclin-dependent kinase inhibitor p21/WAF1 , a downstream gene target of p53 [43] , [44] . We observed increased expression of CDKN1A ( p21 ) ( Figure 3B ) , which was p53-dependent ( Figure 4C ) , in parallel with accumulation of hESCs in G1 phase during differentiation of both WA09 and WA01 hESCs ( ) . p53 directly regulates p21 expression , as chromatin immunoprecipitation ( ChIP ) analysis revealed RA-induced enrichment of p53 at the distal p53 response element ( p53RE ) of CDKN1A ( Figure 3C ) . The importance of p21 in RA-mediated alteration of the hESC cell cycle was shown by siRNA depletion of CDKN1A and loss of hESC accumulation in G1 ( Figure 3A ) . An RA-mediated elongation of the G1 phase during differentiation of hESCs was marked by a specific increase in unmodified retinoblastoma tumor suppressor protein ( non-phosphorylated RB ) , alongside up-regulated p21 protein ( Figure S4F ) . Previous studies show that RB is hyper-phosphorylated and inactive in self-renewing , cycling hESCs [11] , [45] . Interestingly , differentiation-induced p53 did not activate expression of genes GADD45A and BAX , which are associated with apoptosis ( Figure S4G ) . In contrast , hESCs underwent cell death after exposure to appropriate levels of DNA-damaging agents ( Figure 3D and 3E ) , and showed p53 enrichment on p53REs and increased expression of CDKN1A , MDM2 , BAX , and GADD45A under these conditions ( Figure S4G and S4H ) . RA-induced p53 and differentiation had little effect on the number of apoptotic hESCs , as shown by Annexin V staining and γ-H2AX levels ( Figure 3D and 3E ) . One approach we used to assess the progression of RA-mediated differentiation was to stain hESC cultures with alkaline phosphatase ( AP ) at each time point . As previously reported [46] , differentiation is marked by loss of AP staining and appearance of cells with a flattened cellular morphology ( Figure 4A ) . Depletion of p53 by siRNA delayed RA-mediated differentiation ( Figure 4A and 4B; see also Figure 5E ) , as more than 60% of hESCs remained undifferentiated after 3 d of RA treatment ( AP-stained colonies quantified in Figure 4F ) . Additionally , with siRNA of p53 , pluripotency markers NANOG and OCT4 maintained expression and there was no induction of p21 , as compared to cells transfected with non-target siRNA ( siControl ) ( Figure 4B and 4C ) . These results were confirmed by flow cytometry analysis of hESCs stained with OCT4 and SSEA4 , which revealed no reduction in OCT4 staining in cells depleted of p53 compared to only 64% cells positive for OCT4 in siControl hESCs , 3 d after RA treatment ( Figure 4D ) . Up-regulation of p53 is transient during differentiation of hESCs , as auto-regulation of negative regulators , HDM2 and TRIM24 , is triggered by p53 ( Figure S5 ) . We transiently transfected pools of siRNA specific for either HDM2 or TRIM24 , and achieved ∼70% reduction in levels of gene expression in each case ( Figures S3 and S5 ) . Depletion of HDM2 and TRIM24 by siRNA in untreated hESCs increased p53 protein levels ( Figure S5A ) , increased p21 RNA and protein , and decreased OCT4 and NANOG expression ( Figure S5B ) . Depletion of either HDM2 or TRIM24 led to spontaneous differentiation ( approximately 50% differentiated colonies ) ( Figure 4E and 4F ) , as well as a 4-fold increase in the number of hESCs in G1 phase ( Figure 4H ) . Flow cytometry revealed a significant reduction in OCT4-positive cells ( Figure 4G ) , which correlates with siRNA-mediated depletion of HDM2 and TRIM24 ( Figure S3 ) . Spontaneous differentiation of hESCs , with increased expression of p53 and p21 , occurred even under cell culture conditions where pluripotency is normally maintained and without RA treatment ( Figures 4E–4H and S5 ) . This is in sharp contrast to hESCs depleted of p21 , which exhibited significantly delayed differentiation ( Figure 4A , bottom panel , and Figure 4F ) , no change in percent of cells positively stained for OCT4 ( Figure 4D ) , and no increase in cells residing in G1 ( Figure 3A ) . Expression of pluripotency markers under these conditions further supported the links between p53 , p21 , and differentiation ( Figure S5B ) . We established a system in hESCs where expression of p53 is controlled in a dose-dependent manner without addition of RA . Lentiviral constructs , which express wild-type or mutant p53 with an IRES-GFP reporter under control of a tetracycline ( doxycycline [Dox] ) –responsive promoter , were introduced into hESCs and selected for stable integration . Cell lines with regulated expression of wild-type p53 ( p53WT ) or p53 mutated within its DNA-binding domain ( p53R175H and p53R175P ) were positive for both OCT4 and SSEA4 stem cell markers , as assessed by flow cytometry analysis , in the absence of Dox ( Figure S6A ) and exhibited Dox-regulated expression of p53 ( Figure 5 ) . In the absence of RA , Dox-induced expression of p53WT led to loss of OCT4 expression . In contrast , expression of a mutated form of p53 , incapable of binding to DNA and regulating p53 target gene expression ( p53175H ) , did not correlate with decreased OCT4 even though p53R175H protein levels are higher than those of p53WT ( Figure 5A ) . Interestingly , Dox induction of a mutated form of p53 , p53R175P , known to induce cell cycle arrest but not apoptosis in mouse models [47] , [48] led to loss of OCT4 expression , although the decrease was smaller than the significant reduction induced by p53WT ( Figure 5A and 5C ) . The dichotomy between responses of hESCs exposed to p53R175H versus p53R175P supports functions of p53 in the activation of cell cycle arrest , without apoptosis , during differentiation of human cells . Further analysis of differentiation driven by conditionally regulated p53 , in the absence of RA , showed that p53 target genes , CDKN1A and HDM2 , were activated over a time course of Dox exposure ( Figures 5C and S6B ) . In parallel , pluripotency marker gene expression of KLF4 , NANOG , and OCT4 was significantly reduced with length of exposure to Dox ( Figures 5C and S6C ) . Dox treatment led to a ∼4- to 5-fold induction in exogenous wild-type and mutant p53 RNA , with an insignificant change in endogenous TP53 levels ( Figures 5C and S6B ) . Differentiation occurred as marked by a gain in AFP expression and flattened cell morphology , with loss of AP staining in hESCs expressing p53WT or p53R175P but not p53R175H ( Figure 5B and 5C ) . Ectopic expression of p53WT and p53R175P led to an increase of cells in G1 , similar to induction by RA treatment; however , p53R175H did not affect the cell cycle profiles of hESCs ( Figure 5D ) . Induction of p53WT in hESCs led to expression of endoderm markers GATA4 and AFP , as well as ectoderm marker PAX6 ( Figure 5F and 5G ) . However , these cells did not express mesodermal marker Brachyury ( Figure 5F and 5G ) . Differentiation is specific to p53 , as depletion of Dox-induced p53 by siRNA , added at t = 0 , rescues OCT4 protein expression ( Figure 5E ) . Similarly , after siRNA-mediated depletion of p21 in hESCs over-expressing p53 , OCT4 protein levels were rescued , further confirming p21 as a mediator of p53-induced differentiation of hESCs , whether induced by exogenous p53WT or by RA treatment ( Figure 5E ) . Interestingly , Dox-induced p53 is not acetylated at K373 , as is readily detectable when RA is used to induce equivalent levels of endogenous p53 and differentiation of hESCs ( Figures 2 and 5H ) . This finding suggests that ectopic induction of p53 circumvents K373 acetylation , which promotes release of endogenous p53 from negative regulatory proteins during RA-induced differentiation ( Figure 2 ) . Taken together , these results support a view of p53 as a critical regulator of hESC differentiation , capable of acting in the absence of RA and other stimuli that may be induced by RA treatment . The role of cell cycle regulators in this process is underscored by expression of mutated forms of p53 that specifically regulate genes that lead to G1 arrest but are unable to regulate apoptosis in mouse models [47] . To understand the mechanism underlying p53-mediated differentiation of hESCs , we performed high-throughput ChIP sequencing analysis of hESCs incubated with RA ( unpublished data ) . Putative p53 targets included miRNAs; among these , we focused on miR-34a and miR-145 as likely significant in the p53-mediated regulation of hESCs . In somatic cells , miR-34a acts in a feed-forward loop of p53 control . In response to stress stimuli , p53 is activated and induces expression of miR-34a , which in turn represses negative p53 regulator SIRT1 to augment p53 activation [49] , [50] , and CyclinD1 and CDK6 to support cell cycle arrest [51] . SIRT1 deacetylates p53 , which decreases the ability of p53 to bind DNA and regulate gene expression [52] , as we also show in pluripotent hESCs ( Figure 2 ) . Regulation of miR-34a and its downstream targets in ESCs has not been previously reported , to our knowledge . In contrast , a role for miR-145 in differentiation of hESCs is known , where miR-145 acts by negatively regulating levels of pluripotency genes , OCT4 , SOX2 , and KLF4 [53] . miR-145 is known to be a p53 target in somatic cells [54]; however , the mechanisms that lead to miR-145 up-regulation during differentiation of hESCs have not been defined . In response to RA treatment and differentiation of hESCs , miR-34a and miR-145 were significantly up-regulated in a p53-dependent manner ( Figure 6A and 6B ) , an induction which also occurs with a DNA-damaging agent , Adr ( Figure S7A ) . RA treatment led to a time-dependent enrichment of p53 at predicted p53REs of both miR-34a and miR-145 ( Figure 6C ) , in parallel with the transient activation of p53 . Interestingly , p53 enrichment on two identified p53REs of miR-145 exhibited distinct patterns during differentiation compared to DNA damage: p53 accumulation occurred at both p53REs but was stronger on the proximal p53RE ( p53RE2 ) during differentiation and on the distal p53RE ( p53RE1 ) after DNA damage ( Figures 6C and S7B ) . Introduction of small inhibitory oligonucleotides to counter expression of targeted miRNAs ( anti-miRNAs ) , anti-miR-34a and anti-miR-145 , resulted in ∼80% miRNA depletion ( Figure S6C ) , and had specific effects on expression of stem cell factors: inhibition of miR-34a led to increased expression of OCT4 , KLF4 , LIN28A , and SOX2 proteins , and to a lesser extent SIRT1 ( Figure 6E ) , as well as SOX2 and SIRT1 RNA ( Figure 6D ) . Inhibition of miR-145 induced protein levels of OCT4 , SOX2 , and KLF4 , as well as increased RNA expression of SOX2 and KLF4 ( Figure 6D and 6E ) . Quantitative determination of OCT4/SSEA4-positive cells by flow cytometry analysis revealed that hESCs could differentiate with RA after inhibition of miR-34a but not in the presence of anti-miR-145 ( Figure 6F ) . Depletion of both miRNAs significantly delayed differentiation of hESCs , as ∼97% of hESCs remained OCT4-positive 3 d after RA treatment ( Figure 6F ) , indicating the significance of these miRNAs during differentiation . Depletion of miR-145 also significantly affected accumulation of hESCs in G1 after RA treatment ( Figure S7D ) . miR-145 targets c-Myc [54] , which is known to repress p21 [55]; thus , miR-145 represses pluripotency factors and likely contributes to regulation of the hESC cell cycle by decreasing c-Myc and indirectly activating p21 during differentiation . In silico analysis by TargetScan [56] , PicTar [57] , miRanda [58] , and miRBase [59] of genes potentially regulated by miR-34a and miR-145 identified several genes significant to ESC biology ( Figure S7E ) . Pluripotency genes targeted by mir-145 are known [53]; additionally , we found that mir-34a has predicted target sites within the 3′ UTRs of KLF4 and LIN28A , which are conserved across species ( Figure 6G ) . To investigate whether KLF4 and LIN28A are directly targeted by miR-34a , we engineered luciferase reporters that have either the wild-type 3′ UTRs of these genes , or mutated 3′ UTRs with a 4-bp mutation in the predicted target sites . The luciferase reporters were cotransfected with miRNA precursor ( pre-miRNAs ) mimics , which are processed into mature miRNAs in HEK293 cells . A scrambled precursor with no homology to the human genome was used as a control . The pre-miRNA mimic of miR-34a ( pre-miR-34a ) significantly reduced the luciferase activity of the wild-type LIN28A reporter ( ∼30% ) , compared to the scrambled precursor control ( two-tailed Student's t test; Figure 6H ) , and did not alter activity of mutated reporters ( Figure 6H ) . Repression was specific to LIN28A , as there was no significant effect of pre-miR-34a transfection on the KLF4 reporter . These results suggest that miR-34a directly targets sites within the LIN28A 3′ UTR . Taken together , p53-activated miRNAs decrease expression of major stem cell factors to oppose self-renewal of hESCs , as well as inhibiting SIRT1 , a negative regulator of p53 . Thus , p53-mediated regulation of miRNAs reinforces and expands the direct effects of p53 in regulation of the cell cycle during differentiation of hESCs .
TP53 is mutated in more than half of all human cancers , and maintains genomic stability in somatic cells , primarily as a stress-responsive transcription regulator of genes that control cell cycle arrest and apoptosis [60] , [61] . Functions of p53 in cellular metabolism , homeostasis , and development are less understood , but are increasingly appreciated [62] , [63] . In adult stem cells , p53 negatively regulates proliferation and self-renewal of neural stem cells and hematopoietic stem cells to maintain their quiescent state [64] , [65] . A role for p53 in ESC modulation was first suggested by a report that p53 directly represses Nanog in mESCs [40] . Likewise , p53 functions in apoptosis and differentiation of hESCs were previously reported , but no clear mechanisms were revealed [66] , [67] . Here , we show that p53 actively promotes differentiation of hESCs and does so by mechanisms distinct from direct regulation of NANOG transcription ( Figure 7 ) . In contrast to in mESCs , human p53 is localized in the nucleus of hESCs at a low concentration and in a deacetylated state . In response to differentiation signals , SIRT1 is down-regulated [33] , allowing p53 to be acetylated at Lys373 , a target of CBP/p300 . Acetylation of p53 activates its functions as a transcription factor [68] , and relieves p53 from HDM2- and TRIM24 ( shown here ) – mediated ubiquitination and degradation [34] . The importance of p53 concentration and its regulation was shown by the significant levels of differentiation that occur either in response to siRNA-mediated depletion of MDM2 and TRIM24 or by ectopic expression of p53 . In these cases , differentiation occurs in the absence of RA and in medium that normally maintains stem cells as such . Comparison of cell cycle profiles during RA-mediated differentiation and DNA damage conditions highlights the diverse roles played by p53 to restrict cell division and initiate either differentiation or DNA repair , respectively . Differentiation-activated p53 binds to the p53REs of downstream gene target CDKN1A to promote a G1 block that effectively elongates G1 and lengthens the cell cycle of hESCs , with minimal induction of apoptosis . The pattern of post-translational modifications of p53 and the accumulation of hESCs in G1 induced by RA are in contrast to arrest of Adr-treated hESCs at G2–M of cell cycle in a classical , p53-mediated DNA damage response [60] . We find that p53 , transiently activated during differentiation , regulates cell cycle but does not induce significant apoptosis . Although selectivity of p53 in activating arrest of cell cycle versus apoptosis remains incompletely understood [30] , our findings for hESC differentiation recapitulate the specificity previously shown in mouse models that express specific point mutants of p53 [48] , [69] and suggest that these mechanisms are highly conserved . Differentiation of hESCs is significantly delayed when TP53 and/or CDKN1A levels are reduced , as shown by AP staining , cell cycle analysis , and expression of markers of pluripotency in hESCs transfected with siRNAs . Recently , Dox-inducible exogenous expression of p21 in hESCs was shown to induce cell cycle arrest and massive hESC differentiation [70] , further supporting that induced levels of p21 and control of cell cycle are required for hESCs to differentiate . A number of p53 downstream target genes have been extensively studied , especially in transformed cells , and non-coding RNAs regulated by p53 are now being identified [71] . Roles of miRNAs during hESC differentiation or reprogramming of somatic cells have recently been reported [24] , [53] , [72]–[76] , with specific signatures of miRNAs shown in distinct stem cell states of pluripotency regulated by p53 [77] . A known p53 target , miR-34a , was shown to increase p53 activation by repressing SIRT1 in cultured cells [49] but was not previously linked to hESCs . miR-145 was discovered to be a direct repressor of pluripotency factors , but was not shown to be regulated by p53 in stem cells [53] . Here we show that p53 activates miR-34a and miR-145 expression during RA-mediated differentiation of hESCs . Expression of these miRNAs , directly induced by chromatin interaction of p53 at p53REs , impacts a network of target transcripts that control pluripotency , either directly or indirectly . Further , during preparation of this manuscript , Choi et al . [78] showed that miR-34a provides a barrier to somatic cell reprogramming . This study offers further support for our finding that miR-34a antagonizes pluripotency of hESCs and has pro-differentiation functions in stem cell biology . We found that miR-145 has a more significant role in differentiation of hESCs , with miR-34a acting to augment its functions . However , since DNA damage induced miRNAs miR-34a and miR-145 but did not promote accumulation of hESCs in G1 and differentiation of hESCs , as seen with RA treatment or ectopic p53 expression , it is clear the miRNAs alone are insufficient to induce differentiation of hESCs . We showed that p53 activation is transient during differentiation of hESCs; thus , activation of miRNAs that repress stem cell factor expression broadens the impact of p53 activation and may prevent “backsliding” to pluripotency , once p53 returns to its normally low concentration during differentiation to a committed state . Recently , the creation of TP53−/− hESCs by homologous recombination showed that loss of p53 promotes pluripotency , a role for p53 conserved in both murine and human ESCs [79] . However , TP53−/− hESCs contribute to all three germ layers during teratoma formation in SCID mice , perhaps because of compensation by structurally similar members of the p53 family , p63 and p73 . Deletion of all p63 and p73 isoforms in mice reveals critical roles in development and differentiation [80] , [81] . p63 and p73 can bind to canonical p53 DNA-binding sites and regulate transcription from p53-responsive promoters , in the presence or absence of p53 itself [82]–[84] . Compensation may be incomplete , as p53-null mice exhibit some developmental anomalies , such as a high percentage of exencephaly in females , and specific genes exhibit altered p53-regulated gene expression during development [85]–[87] . The establishment of elongated Gap-phase timing in stem cells , more similar to that in somatic cells , was previously proposed as a requirement for reception of differentiation signaling [11] . Our studies show that p53 is integral in this process and actively promotes differentiation of hESCs , in the absence of cellular stress or DNA damage . The collective effects of p53 activation elongate the G1 phase and antagonize pluripotency by induction of miR-34a and miR-145 ( Figure 7 ) . Importantly , activation of p53 during differentiation of hESCs is transient , allowing later stages of growth and differentiation , while p53-induced miRNAs regulate a network of genes that bolster forward progression to differentiate . How these findings in ESCs may be relevant in adult and tumor stem cells—where they may be channeled toward therapeutic applications to restructure cell cycle and regulate a network of miRNAs—is an important area for further study .
hESCs ( WA09 and WA01 ) were obtained from National Stem Cell Bank and cultured according to the protocol from WiCell Research Institute . Briefly , WA09 cells were maintained in hESC culture medium on γ-irradiated mouse embryonic fibroblasts ( MEFs ) prepared using WiCell instructions . hESCs ranging from passage number 32–38 were used for all of our experiments . hESC complete culture medium is composed of DMEM/F12 supplemented with 20% knockout serum replacement , 1 mM L-glutamine , 1% nonessential amino acids , 4 ng/ml human FGF2 ( all from Invitrogen ) , and 0 . 1 mM 2-mercaptoethanol ( Sigma ) . The medium was changed daily , and cells were passaged every 4–6 d with 1 mg/ml Collagen IV ( Invitrogen ) . For differentiation studies hESCs were cultured in differentiation medium ( hESC medium without FGF ) containing 1 µM RA for 5 d , with fresh medium change daily . hESCs were also maintained as feeder-free cultures on hESC qualified Matrigel ( BD Biosciences ) in mTeSR1 medium ( Stemcell Technologies ) and MEF conditioned medium ( CM ) . Passage 32 hESCs were grown on mTeSR1 medium for five passages . hESCs were cultured on Matrigel following manufacturer's instructions and received fresh mTeSR1 medium daily , and cells were passaged every 4–6 d with 1 mg/ml Dispase ( Stemcell Technologies ) . For differentiating hESCs cultured on mTeSR1 , 1 µM RA was added to homemade MEF CM ( without additional FGF ) . CM was prepared in our facility by culturing γ-irradiated MEFs in complete hESC culture medium for 24 h , collected daily , filtered , and freezed at −20°C . FGF was added to CM before use to a final concentration of 10 ng/ml to culture the cells grown on Matrigel under pluripotent conditions . hESC colonies were grown on Matrigel in six-well plates . siRNA targeting human TP53 , CDKN1A ( p21 ) , HDM2 , TRIM24 ( Table S1 ) and non-target ( control ) were purchased from Dharmacon and anti-miRNA oligonucleotides targeting human miR-34a , miR-145 , and miR-nonspecific were purchased from Applied Biosystems . 75 nM siRNA or 75 nM anti-miRNA oligonucleotides were transfected twice into cells using Lipofectamine2000 ( Invitrogen ) transfection reagent according to manufacturer's protocol within a period of 5 d . The first siRNA transfection was performed 24 h after splitting the cells , followed by medium change 6 h post-transfection . 36 h after the first siRNA transfection , cells were cultured in differentiation medium ( −FGF , +RA ) for 3 d and harvested to analyze protein , RNA , AP , or cell cycle status . Cells were transfected one more time with siRNA on the beginning of day 2 of RA treatment to maintain the knockdown efficiency . To determine the transfection efficiency these siRNAs were cotransfected with siGLO Green ( FAM ) , also purchased from Dharmacon . Twenty-four hours post-transfection cells were either visualized by microscopy or subjected to flow cytometry analysis to determine the percent of cells transfected . See Figure S3 for percent transfection and knockdown efficiencies of siRNAs . In case of anti-miRNA transfections , cells were harvested 24 h post-transfection for RNA and miRNA analysis and 48 h post-transfection for analyzing protein levels . WA09 cells were cultured in mTeSR1 medium as described above . At each passage ( Accumax , Millipore ) cells were treated for 24 h with 1 µM Y-27632 ( Rock inhibitor; Alexis ) . Lentiviral supernatants were produced by transfecting 293T cells with the pSAM2 . gw vector , pCMV-VSV-G and pCMV-ΔR8 . 91 , using Fugene HD transfection reagent ( Roche ) and collected 48 h post-transfection . WA09 rtTA cells were generated by spin infection of WA09 cells with Lenti-rtTA [88] at 2 , 500 rpm at 37°C for 90 min with polybrene ( 4 µg/ml ) , and incubated at 37°C for an additional 2 h prior to medium replacement with mTeSR1 . The WA09 rtTA cells were subsequently transduced with listed pSAM2 . gw plasmid as described above . Enrichment of infected cells was performed by FACS sorting ( FACSAria II , BD Biosciences ) for GFP-positive cells ( to a final enrichment of about 80% ) , following low dose transient induction ( addition of 0 . 05 µg/ml Dox [Sigma] ) for 8 h . About 16 h before the sort , the Dox was removed , and the cells were incubated with regular mTeSR1 medium . Positive cells were sorted , plated in fresh mTeSR1 , and expanded for secondary/tertiary enrichment or testing . pSam2 . gw was generated by replacing the ubiquitin C promoter from FUGW [89] with a second generation tetracycline response element [90] , inserting a Gateway positive/negative selection cassette flanked by attR1 and attR2 sequences ( Invitrogen ) , and replacing GFP with the IRES-GFP sequence from pMSCV-IRES-GFP [91] . All constructs to be expressed were then cloned into pENTR1a ( Invitrogen ) and transferred into pSAM2 . gw by Gateway recombination . RNA was isolated using TRIzol Reagent ( Invitrogen ) and miRVana Isolation Kit ( Ambion ) as per manufacturer's specifications . For RNA analysis 500 ng of total RNA was treated with DNase I , and cDNA was synthesized as previously described [38] and amplified with human-gene-specific primers ( Table S2 ) with Power SYBR Green PCR Master Mix ( Applied Biosystems ) . Primers targeting the 3′ UTR were used to detect endogenous TP53 , and primers targeted to coding region were used to detect Dox-induced exogenous TP53 . The average threshold ( Ct ) was determined for each gene and normalized to Actin mRNA level as an internal normalization control . The miRNA levels were assayed with the TaqMan gene expression assays and TaqMan Universal PCR Master Mix in an Applied Biosystems PRISM 7900HT Fast Real-Time PCR System according to the manufacturer's instructions . Briefly , reverse transcription of 10 ng of total RNA by the High-Capacity cDNA Archive Kit ( Applied Biosystems ) was followed by 18 cycles of pre-PCR and 40 cycles of real-time PCR using TaqMan Fast Universal PCR Master Mix . miRNA levels were normalized against the control RNU6 probe . hESCs were lysed in RIPA buffer ( 50 mM Tris [pH 8 . 0] , 150 mM NaCl , 1% NP-40 , 0 . 5% deoxycholic acid , 0 . 1% SDS , 1 mM PMSF , and 1 mM Na-Vanadate ) supplemented with protease inhibitor cocktail ( Calbiochem ) and phosphatase inhibitor cocktails I and II ( Sigma ) . The protein concentration was estimated by the Bradford Protein Assay Kit ( Bio-Rad ) . 50 µg of cell lysate was then separated via sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) and transferred to nitrocellulose membrane . The membrane was blocked with 5% milk , and protein levels were analyzed by immunoblotting with anti-p53 ( DO1 ) , anti-OCT4 , anti-SIRT1 , anti-SIN3A ( Santa Cruz Biotechnology ) , anti-NANOG , anti-KLF4 ( R&D Systems ) , anti-SOX2 , anti-p53K373ac , anti-p53S15ph , anti-p53S46ph , anti-p53K320ac ( Cell Signaling Technologies ) , anti-p21 ( BD Pharmingen ) , anti-pRB ( Oncogene Sciences ) , anti-HDM2 ( Calbiochem ) , anti-TRIM24 ( Novus Biologicals ) , anti-γ-H2AX ( Trevigen ) , and anti-Actin ( GeneTex Biotechnology ) , followed by corresponding HRP-tagged secondary antibody ( GE Healthcare ) . hESCs grown on inactivated MEF feeders were cultured in complete hESC medium or treated with RA for 5 d in differentiation medium , and the cells were harvested and lysed in RIPA buffer . 0 . 5 mg of cell lysates was used for immunoprecipitation with primary antibody . The extract was incubated with 2 . 5 µg of antibody overnight at 4°C with shaking . Forty microliters of washed Protein A bead suspension ( GE Healthcare ) was added , and the extract was incubated for additional 1 h at 4°C with shaking . Immune complexes were washed twice with RIPA buffer , boiled with 1× SDS sample dye , and resolved on 10% SDS-PAGE gel followed by immunoblotting with corresponding antibodies . hESCs were grown on 100-mm tissue culture plates in either complete hESC medium or in differentiation medium ( −FGF , +RA ) for 4 d . At the end of treatment , cells were washed twice with ice-cold PBS , scraped in PBS , and centrifuged at 500 rpm for 5 min . Biochemical fractionation of cells was done using the Nuclear Extract Kit ( Active Motif ) following the manufacturer's protocol . Briefly , the cell pellet was resuspended in 1× hypotonic buffer ( cytoplasmic buffer ) supplemented with complete protease inhibitor mixture ( Calbiochem ) , incubated for 15 min at 4°C , vortexed in the presence of detergents , and centrifuged briefly . The supernatant ( cytoplasmic fraction ) was collected into a prechilled microcentrifuge tube . The nuclear pellet was washed twice with the cytoplasmic buffer , followed by resuspending in the lysis buffer supplemented with 1 mm dithiothreitol and protease inhibitors . The suspension was incubated on a rocking platform at 4°C for 30 min , vortexed briefly and centrifuged for 10 min at 14 , 000 g at 4°C , and the supernatant ( nuclear fraction ) was collected . The protein concentration was determined using the protein assay reagent ( Bio-Rad ) . 50 µg of the cell fractions was resolved on a 10% SDS-PAGE gel , Western blotted , and probed with anti-p53 , anti-OCT4 , and anti-NANOG antibodies . To confirm the purity of subcellular fractionation , the extracts were immunoblotted with cytoplasm-specific anti-LDH antibody ( Chemicon ) and nucleus-specific anti-TBP antibody ( Santa Cruz Biotechnology ) . hESC colonies were grown on Matrigel-coated coverslips in mTeSR1 or MEF complete CM ( CM + FGF , 10 ng/ml ) or CM differentiation medium ( +RA , 1 µM ) for 3 d . Cells were fixed with 4% paraformaldehyde in water for 15 min at room temperature ( RT ) . Cells were washed three times with PBS and blocked with blocking buffer ( 10% normal goat serum in PBS ) for 2 h at RT , incubated with anti-p53 and anti-OCT4 antibodies ( Santa Cruz Biotechnology ) overnight at RT , and washed twice in PBST ( PBS + Tween20 ) . The cells were then incubated with the secondary antibody ( Alexa-Fluor goat anti-rabbit 488 for OCT4 and Alexa-Fluor goat anti-mouse 546 for p53; Molecular Probes ) for 45 min in the dark at RT , followed by two washes with PBST . Coverslips with cells were then mounted on glass slides using Antifade mounting reagent from the Slowfade Antifade Kit ( Molecular Probes ) . The cells were examined and photomicrographed using an Olympus confocal microscope . hESCs grown on inactivated MEF feeders were cultured in complete hESC medium or treated with RA upto 4 d in differentiation medium and were collected , PBS washed , and crosslinked in 1% formaldehyde for 10 min at RT . After glycine followed by PBS wash , cells were lysed using lysis buffer ( 5 mM HEPES [pH 8 . 0] , 85 mM KCl , 0 . 5% NP40 ) supplemented with protease inhibitor ( Calbiochem ) . After removal of cytoplasmic extract , remaining cell pellet was lysed in nuclear lysis buffer ( 50 mM Tris-HCl [pH 7 . 5] , 10 mM EDTA , 1% SDS ) and protease inhibitor . Lysates were sonicated with glass beads ( Sigma ) ten times for 10-s pulse on ice to obtain DNA fragments of average length under 500 bp . After centrifugation , the supernatant was preabsorbed with 40 µl of Protein A beads ( GE Healthcare ) and IgG for 2 h , then incubated with 5 µg of p53 antibody ( Santa Cruz Biotechnology ) or control IgG ( Millipore ) overnight at 4°C . The immunocomplex was collected on Protein A beads and washed , and bound DNA was eluted and reverse crosslinked overnight at 65°C . The DNA region of interest was detected by SYBR quantitative real-time PCR ( qRT-PCR ) using primers encompassing p53REs on the respective gene ( see Table S3 for sequence of primers used for ChIP-qRT-PCR ) . hESCs grown on inactivated MEF feeders were cultured in complete hESC medium or treated with RA for 5 d in differentiation medium , and the cells were harvested and lysed in RIPA buffer . Equal amounts of p53 were pulled down by varying the amounts of total lysates from each treatment and probed with p53K373ac antibody . In order to analyze the effects of CBP/p300 on RA-mediated acetylation of p53 , hESCs were treated with RA for 2 d , and 25 µM circumin ( Sigma ) was added 24 h before harvesting on day 2 [32] . Similarly , to inhibit the deacetylase activity of SIRT1 on p53 , 5 mM nicotinamide ( Sigma ) was added 24 h before harvesting the cells treated with RA for 4 d [34] . hESCs were treated with proteasome inhibitor , MG132 ( Calbiochem ) , or lactacystin ( Calbiochem ) for 6 h prior to lysis in RIPA buffer supplemented with 10 mM iodoacetamide ( GE Healthcare ) and protease inhibitors . Endogenous p53 was immunoprecipitated from 1 mg of protein lysate using p53 ( DO1 ) antibody and immunoblotted with anti-ubiquitin antibody ( Sigma ) . The blot was reprobed with p53 to confirm the equal p53 pull down . hESC colonies were fixed in 2% formaldehyde ( Sigma ) and stained with Vector Blue Alkaline Phosphatase Substrate Kit I ( Vector Labs ) following manufacturer's instructions . The self-renewing colony stains positive for AP , but differentiated colonies stain less or negative for AP . For cell cycle analysis , 1−5×105 cells from each sample were trypsinized to make single cell suspension , washed with PBS , and fixed in 95% ethanol ( Sigma ) . The cells were then treated with RNaseA and stained with propidium iodide ( PI ) ( Calbiochem ) as per manufacturer's instructions . 20 , 000 events were analyzed on an Epics XL flow cytometer ( Coulter ) , and cell cycle distribution was analyzed by the ModFit LT program . For apoptosis assay , cells were double stained with Annexin V–FITC and PI as per manufacturer's instructions ( BD Pharmingen ) . 20 , 000 events were analyzed on an Epics XL flow cytometer , and percent apoptosis was determined using System II software ( Coulter ) . hESCs were grown in six-well plates and submitted to the Human Embryonic Stem Cell Core at Baylor College of Medicine , Houston , Texas , for FACS staining and quality control . Briefly , the cells were removed from the dish with trypsin/EDTA ( Invitrogen ) . Trypsin was neutralized with MEF medium ( DMEM containing 10% FBS [Hyclone] ) and pelleted by centrifugation for 5 min at 250 g . Cells were resuspended in FACS buffer ( PBS containing 2% FBS and 0 . 1% sodium azide ) and probed for the surface antigens with SSEA4 ( R&D Systems ) conjugated with Alexa-488 ( Invitrogen ) . The cells were then fixed with 2% paraformaldehyde for 30 min at RT and permeabilized with 0 . 1% saponine ( Sigma ) in PBS with 0 . 1% BSA ( Sigma ) for 30 min . Cells were washed with FACS buffer and probed for 30 min at RT with the OCT4 antibody conjugated with R-phycoerythrin ( both from BD Biosciences ) as an intracellular protein . The samples were analyzed using LSRII equipment ( BD Biosciences ) . The cell population of interest was determined and dead cells excluded using forward and side scatter parameters . Acquisition was set for 30 , 000 events per sample . The data were analyzed with FACSDiva software ( version 4 . 1 . 2; BD Biosciences ) . Triplicate samples were analyzed in each experiment . Primer pairs were designed to amplify a region of about 200–300 bp around every predicted miR-34a target site within the 3′ UTRs of KLF4 and LIN28A ( Table S4 ) . The amplicons were cloned in pMir-Report vector ( Ambion ) at HindIII and SpeI sites . Several residues in the miR-34a target site in the 3′ UTRs were mutated using site-directed mutagenesis ( Stratagene ) with the mutagenesis primers . In the 3′ UTR assay , 10×104 293 cells were transfected with 100 ng of the UTR reporter ( pMir-Report ) , 10 ng of the transfection control Renilla vector ( phRLTK , Promega ) , and 20 nM pre-miR-34a precursor molecules or scrambled control miRNA ( Ambion ) with 3 µl of Lipofectamine 2000 . Twenty-four hours after transfection , cells were lysed in 1× Passive Lysis Buffer , and reporter activity was measured using the Dual Luciferase Assay ( Promega ) . Each assay was tested in triplicate in three independent experiments . | Most cell types in an organism are generated from embryonic stem cells ( ESCs ) , which are able to proliferate an unlimited number of times and have the potential to produce any kind of cell of that organism ( this ability is called pluripotency ) . In order to maintain these properties , ESCs have to remain in a proliferate state , which is achieved by the collaboration of several factors . Expressing combinations of these factors in differentiated cells can result in ESC-like qualities; these induced pluripotent stem cells ( iPSCs ) can then function like ESCs . Previous studies suggested that p53 , generally known for its roles in maintaining genomic integrity by regulating cell cycle and cell death pathways , also acts as a barrier to reprogramming adult cells during the creation of iPSCs; whether this is strictly due to repression of proliferation remains a subject of debate . Here , we show that p53 plays a significant role in actively promoting differentiation of human ESCs ( hESCs ) . We find that , prior to differentiation , p53 is expressed at very low levels in hESCs , held in check by two negative regulators , HDM2 and TRIM24 , that trigger p53 degradation . Upon induction of differentiation , lysine 373 of p53 is acetylated , and this disrupts the existing interactions with negative regulators , thus allowing stabilization and activation of p53 . Active p53 in turn promotes expression of the cell cycle regulator p21 to slow down the hESC cell cycle; cells in the gap ( G1 ) phase of the cell cycle accumulate , preventing division . In parallel , p53 activates specific microRNAs , miR-34a and miR-145 , that inhibit the expression of several stem cell factors and prevent differentiated cells from backsliding to pluripotency . Our results highlight a novel function of p53 in determining the human stem cell state . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"developmental",
"biology",
"biology"
] | 2012 | p53 Regulates Cell Cycle and MicroRNAs to Promote Differentiation of Human Embryonic Stem Cells |
Inter-individual variation in facial shape is one of the most noticeable phenotypes in humans , and it is clearly under genetic regulation; however , almost nothing is known about the genetic basis of normal human facial morphology . We therefore conducted a genome-wide association study for facial shape phenotypes in multiple discovery and replication cohorts , considering almost ten thousand individuals of European descent from several countries . Phenotyping of facial shape features was based on landmark data obtained from three-dimensional head magnetic resonance images ( MRIs ) and two-dimensional portrait images . We identified five independent genetic loci associated with different facial phenotypes , suggesting the involvement of five candidate genes—PRDM16 , PAX3 , TP63 , C5orf50 , and COL17A1—in the determination of the human face . Three of them have been implicated previously in vertebrate craniofacial development and disease , and the remaining two genes potentially represent novel players in the molecular networks governing facial development . Our finding at PAX3 influencing the position of the nasion replicates a recent GWAS of facial features . In addition to the reported GWA findings , we established links between common DNA variants previously associated with NSCL/P at 2p21 , 8q24 , 13q31 , and 17q22 and normal facial-shape variations based on a candidate gene approach . Overall our study implies that DNA variants in genes essential for craniofacial development contribute with relatively small effect size to the spectrum of normal variation in human facial morphology . This observation has important consequences for future studies aiming to identify more genes involved in the human facial morphology , as well as for potential applications of DNA prediction of facial shape such as in future forensic applications .
The morphogenesis and patterning of the face is one of the most complex events in mammalian embryogenesis . Signaling cascades initiated from both facial and neighboring tissues mediate transcriptional networks that act to direct fundamental cellular processes such as migration , proliferation , differentiation and controlled cell death . The complexity of human facial development is reflected in the high incidence of congenital craniofacial anomalies , and almost certainly underlies the vast spectrum of subtle variation that characterizes facial appearance in the human population . Facial appearance has a strong genetic component; monozygotic ( MZ ) twins look more similar than dizygotic ( DZ ) twins or unrelated individuals . The heritability of craniofacial morphology is as high as 0 . 8 in twins and families [1] , [2] , [3] . Some craniofacial traits , such as facial height and position of the lower jaw , appear to be more heritable than others [1] , [2] , [3] . The general morphology of craniofacial bones is largely genetically determined and partly attributable to environmental factors [4]–[11] . Although genes have been mapped for various rare craniofacial syndromes largely inherited in Mendelian form [12] , the genetic basis of normal variation in human facial shape is still poorly understood . An appreciation of the genetic basis of facial shape variation has far reaching implications for understanding the etiology of facial pathologies , the origin of major sensory organ systems , and even the evolution of vertebrates [13] , [14] . In addition , it is feasible to speculate that once the majority of genetic determinants of facial morphology are understood , predicting facial appearance from DNA found at a crime scene will become useful as investigative tool in forensic case work [15] . Some externally visible human characteristics , such as eye color [16]–[18] and hair color [19] , can already be inferred from a DNA sample with practically useful accuracies . In a recent candidate gene study carried out in two independent European population samples , we investigated a potential association between risk alleles for non-syndromic cleft lip with or without cleft palate ( NSCL/P ) and nose width and facial width in the normal population [20] . Two NSCL/P associated single nucleotide polymorphisms ( SNPs ) showed association with different facial phenotypes in different populations . However , facial landmarks derived from 3-Dimensional ( 3D ) magnetic resonance images ( MRI ) in one population and 2-Dimensional ( 2D ) portrait images in the other population were not completely comparable , posing a challenge for combining phenotype data . In the present study , we focus on the MRI-based approach for capturing facial morphology since previous facial imaging studies by some of us have demonstrated that MRI-derived soft tissue landmarks represent a reliable data source [21] , [22] . In geometric morphometrics , there are different ways to deal with the confounders of position and orientation of the landmark configurations , such as ( 1 ) superimposition [23] , [24] that places the landmarks into a consensus reference frame; ( 2 ) deformation [25]–[27] , where shape differences are described in terms of deformation fields of one object onto another; and ( 3 ) linear distances [28] , [29] , where Euclidean distances between landmarks instead of their coordinates are measured . Rationality and efficacy of these approaches have been reviewed and compared elsewhere [30]–[32] . We briefly compared these methods in the context of our genome-wide association study ( GWAS ) ( see Methods section ) and applied them when appropriate . We extracted facial landmarks from 3D head MRI in 5 , 388 individuals of European origin from Netherlands , Australia , and Germany , and used partial Procrustes superimposition ( PS ) [24] , [30] , [33] to superimpose different sets of facial landmarks onto a consensus 3D Euclidean space . We derived 48 facial shape features from the superimposed landmarks and estimated their heritability in 79 MZ and 90 DZ Australian twin pairs . Subsequently , we conducted a series of GWAS separately for these facial shape dimensions , and attempted to replicate the identified associations in 568 Canadians of European ( French ) ancestry with similar 3D head MRI phenotypes and additionally sought supporting evidence in further 1 , 530 individuals from the UK and 2 , 337 from Australia for whom facial phenotypes were derived from 2D portrait images .
Characteristics of the study cohorts from The Netherlands ( RS1 , RS2 ) , Australia ( QTIMS , BLTS ) , Germany ( SHIP , SHIP-TREND ) , Canada ( SYS ) and the United Kingdom ( TwinsUK ) are provided in Table 1 . All participants included in this study were of European ancestry . Facial landmarks in the discovery cohorts RS1 , RS2 , QTIMS , SHIP , and SHIP-TREND were derived from directly comparable 3D head MRIs analyzed using the very same method ( Figure 1 ) . Similar 3D MRIs were available in SYS but the phenotyping method used here was slightly different ( see Method ) . For the BLTS and TwinsUK cohorts , facial landmarks were derived from 2D portrait photos . The SYS ( mean age 15 years ) , QTIMS ( mean age 23 years ) , and BLTS ( mean age 23 years ) cohorts were on average much younger than other cohorts considered ( mean age over 50 years , Table 1 ) . The majority of the TwinsUK cohort was female ( 95 . 5% ) . For 3D MRI based phenotyping , we focused on the nine most well-defined landmarks from the upper part of the face , including Zygion Right ( ZygR ) , Zygion Left ( ZygL ) , Eyeball Right ( EyeR ) , Eyeball Left ( EyeL ) , Alare Right ( AlrR ) , Alare Left ( AlrL ) , Nasion ( Nsn ) , Pronasale ( Prn ) , and Subnasale ( Sbn ) ( Figure 1 ) . The lower part of the face i . e . , from underneath the nose further down was not available due to brain-focussed MRI scanning . Raw landmark coordinates from 5 , 388 individuals in the five discovery cohorts ( RS1 , RS2 , QTIMS , SHIP , and SHIP-TREND ) showed systematic differences in position and orientation ( Figure 2A ) . They were superimposed onto a consensus 3D Euclidean space based on partial PS ( Figure 2B ) . A total of 27 principal components ( PCs ) , and the centroid size parameter , were derived from the superimposed landmarks . Eleven PCs , each explaining >1% and all together explaining 96 . 0% of the total shape variance , were selected for further genetic association analysis ( Table S1 ) . Furthermore , we derived 36 Euclidean distances between each pair of landmarks . The partial PS had no effect on inter-landmark distances i . e . , the distances remain the same after the superimposition . We derived the phenotypic correlations in discovery cohorts containing only adults or young adults . The SYS cohort was excluded from this correlation analysis because the changes through adolescence may confound the effect of age . Centroid size was highly correlated with the first PC ( r = 0 . 96 , Table S1 ) as well as with all 36 inter-landmark distances ( mean r = 0 . 76; minimal r = 0 . 56 for Prn-Sbn; maximal r = 0 . 94 for ZygR-AlrL; Table S2 ) . Inter-landmark distances were also correlated with each other ( mean r = 0 . 56; minimal r = 0 . 10 between EyeL-AlrL and ZygL-EyeL; maximal r = 0 . 96 between AlrR-Nsn and AlrL-Nsn; Table S2 ) . The distances between symmetric landmarks all showed the highest correlations ( Table S2 ) , consistent with general knowledge about facial symmetry . Compared with females , males on average had greater centroid size ( P<1 . 0×10−300 ) and on average 5 mm larger inter-landmark distances ( Table S3 ) . These values are similar to the sex-specific ranges previously reported from cranial data [4] . After adjusting for the effect of centroid size , the most characteristic sex effect was that males on average had larger noses than females ( AlrL-Prn and AlrR-Prn; 4 mm difference; P<1 . 0×10−141; Table S3 ) . This sex difference is also illustrated using a thin plate splines deformation ( Figure S1 ) , showing a larger nose size in males ( Figure S1C ) . Increased age was most significantly associated with increased bizygomatic distance ( ZygR-ZygL , P = 1 . 9×10−111 , Table S3 ) . This is unlikely to be explained by the amount of subcutaneous fat in elderly people since the zygion landmarks were placed on the cortex of the bone . Heritability of 36 inter-landmark distances was estimated in 79 MZ and 90 DZ Australian twins ( range 0 . 46–0 . 79 , mean 0 . 67; Table S4 ) . The phenotypic correlations in MZ pairs ( r = 0 . 71 ) were on average much higher than those in DZ pairs ( r = 0 . 28; Table S4 ) . These estimates are consistent with previous facial morphology studies [1] , [2] and suggest reasonably high reliability of the derived phenotypes . We conducted a discovery phase GWAS in the combined sample ( N = 5 , 388 ) from RS1 , RS2 , QTIMS , SHIP , and SHIP-TREND where facial shape phenotypes were all derived from comparable 3D head MRIs and using the same approach . We tested 2 , 558 , 979 autosomal SNPs for association with 48 facial phenotypes , including the centroid size , 36 inter-landmark Euclidean distances , and 11 shape PCs . Q-Q plots ( Figure 3 ) and genomic inflation factors ( λ<1 . 03 ) did not show any sign of inflation of the test statistics . Since many phenotypes tested were highly correlated ( Table S1 , Table S2 ) , and Bonferroni correction of 48 traits would obviously be too stringent , we considered the traditional threshold P<5×10−8 as the significance threshold in the discovery phase . The GWAS revealed five independent loci at P<5×10−8 ( Table 2 ) . All these signals were observed for inter-landmark distances and most involved the nasion landmark . No genome-wide significant associations were found for individual PCs or for the centroid size . The genome-wide significantly associated SNPs were located either within ( missense or intronic ) or very close to ( <10 kb ) the following five genes: PRDM16 , PAX3 , TP63 , C5orf50 , and COL17A1 . Notably , our finding at PAX3 reflects an independent discovery from a parallel GWAS of facial features recently reported by Paternoster et al . [34] , which identified an intronic SNP of PAX3 , rs7559271 , in association with the nasion position . In our study , three different SNPs , rs16863422 , rs12694574 , and rs974448 at PAX3 on chromosome 2q35 , in the same linkage-disequilibrium ( LD ) block containing rs7559271 , were associated with the distance between the eyeballs and nasion ( 7 . 1×10−7<P<1 . 6×10−8 for EyeR-Nsn and EyeL-Nsn; Table 2 , Figure 4B ) . The SNP rs7559271 from Paternoster et al . was nominally significantly associated with EyeR-Nsn ( P = 0 . 008 ) and EyeL-Nsn ( P = 0 . 004 ) in our data . We therefore independently confirm a role for PAX3 in contributing to facial shape variation at the genome-wide scale , which provides confidence in the remainder of our GWAS findings . Multiple intronic SNPs of PRDM16 on chromosome 1p36 . 23-p33 were associated with nose width and nose height ( such as rs4648379 , 2 . 5×10−7<P<1 . 1×10−8 for AlrL-Prn and AlrR-Prn; Table 2 , Figure 4A ) . An intronic SNP of TP63 on chromosome 3q28 , rs17447439 , showed association with the distance between eyeballs ( P = 4 . 4×10−8 for EyeR-EyeL , Table 2 , Figure 4C ) . A SNP rs6555969 very close to C5orf50 on chromosome 5q35 . 1 was associated with nasion position ( 5 . 8×10−7<P<1 . 2×10−9 for ZygR-Nsn , ZygL-Nsn , EyeR-Nsn , and EyeL-Nsn; Table 2 , Figure 4D ) . A missense SNP rs805722 in COL17A1 on chromosome 10q24 . 3 was also associated with the distance between eyeballs and nasion ( 6 . 5×10−7<P<4 . 0×10−8 for EyeR-Nsn and EyeL-Nsn; Table 2 , Figure 4E ) . We attempted to replicate our GWAS findings in the SYS cohort ( N = 568 ) . Unlike the other ( adult ) cohorts , the SYS cohort is an adolescent one , with a mean age of 15 and a minimum age of 12 years . This may potentially lead to false negative replications since the face continues to develop and ossify throughout adolescence in a different manner than in the adult , especially in male adolescents [22] . On the other hand , the recent identification of PAX3 [34] was based on an adolescent cohort . Our independent identification of PAX3 in adults here demonstrates that at least some genetic effects on facial features that manifest in adolescence remain detectable in adulthood . The association signal at PAX3 , however , did not replicate in SYS , possibly due to the small sample size available in this replication cohort . Likewise , the signals at PRDM16 and TP63 were not replicated in SYS . Two other loci , C5orf50 and COL17A1 , were successfully replicated for the same phenotypes ( 0 . 032<P<7 . 5×10−5 for C5orf50 and 9 . 7×10−4<P<5 . 9×10−4 for COL17A1; Table 2 ) . Besides the exact replication , association signals at C5orf50 and COL17A1 were observed for multiple phenotypes , i . e . 16 . 0% and 18 . 1% of the 1 , 540 pair-wise distances between all 56 landmarks available in SYS [22] ( Table 2 ) . In addition to the direct replication of MRI-based phenotypes in the SYS cohort , we sought further supporting evidence of association in a combined data set of two additional samples from the UK ( TwinsUK , N = 1 , 366 ) and Australia ( BLTS , N = 2 , 137 ) where we localized eight facial landmarks on 2D portrait photos ( illustrated in Figure 5A ) . Raw landmark coordinates showed significant differences not only in position and orientation but also in size ( Figure 5B ) ; we thus used full PS including rescaling to remove these differences ( Figure 5C ) . Note that the inter-landmark distances from 2D photos , with or without rescaling , do not represent the absolute distance in terms of millimeters , as those from 3D MRIs do . Furthermore , the fact that 2D data in general miss a complete dimension may potentially lead to false negative replications . Note also that the twin correlations for the inter-landmark distances , estimated based on 2D photos , were in general much lower than those from 3D MRIs ( r = 0 . 42 in 218 MZ pairs , r = 0 . 16 in 533 DZ pairs; TwinsUK and BLTS combined sample ) , indicating that these phenotypes were more noisy than those derived from the 3D images . In spite of these limitations , we observed nominally significant associations for approximately the same phenotypes for 2 of the 5 loci identified from our GWAS , TP63 and C5orf50 ( P<0 . 05; Table 2 ) . The associations at these 2 loci were also observed for multiple phenotypes , i . e . P<0 . 05 for 17 . 9–21 . 4% of all 28 inter-landmark distances ( Table 2 ) . Thus , except for PRDM16 and PAX3 , all loci identified with genome-wide significance in the discovery cohorts were replicated in 3D MRI ( SYS ) or 2D photo ( TwinsUK , BLTS ) analyses . For PAX3 there was a significant association between rs974448 and the distance between the eyeballs in the 2D data ( beta = 0 . 30 , se = 0 . 15 , P = 0 . 045 , data not shown , note in Table 2 the results for PAX3 are shown for Eye-Nsn phenotypes ) . In our discovery phase GWAS a total of 102 SNPs at 29 distinct loci showed significant or suggestive evidence of association ( P<5×10−7 ) with various facial phenotypes ( Table S5 ) . We provide raw genotype and respective phenotype data for all SNPs that revealed genome-wide significant and suggestive evidence ( Table S6 ) to make our most important findings publically available to other researchers who may wish to explore them further . Finally , we re-investigated the potential association between SNPs previously involved in NSCL/P and normal facial shape variation in our discovery cohorts ( Table 3 ) . For this purpose , we tested associations between facial phenotypes and 11 SNPs ascertained in our previous candidate gene study [20] , originally discovered in previous GWAS on NSCL/P [35] , [36] , [37] , [38] . Five SNPs at 4 candidate NSCL/P loci were significantly associated with normal facial phenotypes even after a strict Bonferroni correction of multiple testing of all 48 correlated phenotypes ( Table 3 ) . These included rs7590268 at 2p21 , rs16903544 and rs987525 at 8q24 , rs9574565 at 13q31 , and rs227731 at 17q22 . All these SNPs were also associated with over 10% of 48 facial phenotypes at P<0 . 05 , where rs987525 at 8q24 was associated with over half of the studied phenotypes ( 52 . 08% , Table 3 ) . In addition , the SNP rs642961 at chromosome 1q32 was associated with 27 . 1% of the studied phenotypes at P<0 . 05 , although the minimal P value was not significant after the over-conservative Bonferroni correction . The SNP rs1258763 at 15q13 was nominally significantly associated with nose-width ( P = 0 . 03 for AlrR-AlrL , Table 3 ) , although not significant after the Bonferroni correction . These findings strongly suggest that genetic variants involved in NSCL/P also influence normal facial shape variation .
We identified five independent loci at 1p36 . 23-p33 , 2q35 , 3q28 , 5q35 . 1 , and 10q24 . 3 consisting of common DNA variants associated with normal facial shape phenotypes in individuals of European ancestry . Candidate genes at these loci include PRDM16 ( PR domain containing 16 ) , PAX3 ( paired box 3 ) , TP63 ( tumor protein p63 ) , C5orf50 ( chromosome 5 open reading frame 50 ) , and COL17A1 ( collagen , type XVII , alpha 1 ) . In addition to our GWA findings , we confirmed links between NSCL/P cleft associated SNPs at 2p21 , 8q24 , 13q31 , and 17q22 and normal human facial shape variation based on a candidate gene approach . From a statistical perspective , the most robust result was the one at the PAX3 locus . We identified this gene in our discovery GWAS , a finding independent of , but consistent with , a recent GWAS [34] . Although the association with the same set of phenotypes failed replication in our replication cohorts , the identification of the same locus at the genome-wide significant level from completely independent GWASs cannot be coincidence . This provides strong statistical evidence that the PAX3 gene is indeed involved in facial morphology . The signal observed at C5orf50 at 5q35 . 1 in association with the nasion position was successfully replicated in both SYS using 3D MRI derived phenotypes under similar ( buit not identical ) methodology as well as in a combined sample of TwinsUK and BLTS using 2D photograph derived phenotypes . The association signal observed at COL17A1 was replicated only in SYS ( 3D MRI ) and that at TP63 was replicated only in TwinsUK and BLTS ( 2D photos ) . The signal observed at PRDM16 was not replicated in our replication cohorts . The failure in replication for some of our GWAS findings may be explained by physical limitations in our replication cohorts as discussed in detail below . Three of the five loci identified in this study have previously been shown to play an essential role in craniofacial development; in particular , they have been implicated in orofacial clefting phenotypes in mice or humans . PAX3 encodes a developmentally important transcription factor expressed in neural crest cells , a multipotent cell population contributing to most differentiated cell types in the vertebrate face . In humans , PAX3 is one of six genes mutated in Waardenburg syndrome , which is characterized by a range of neural crest related phenotypes including minor facial dysmorphism manifest as a broad nasal root and an increased distance between the medial canthi or corners of the eye ( telecanthus ) [39] . Studies in mice have demonstrated that failure to down regulate PAX3 during neural crest differentiation leads to cleft palate , due to inhibitory effects on osteogenesis [40] . Of particular relevance to this study , a recent GWAS detected an association between PAX3 and position of the nasion [34] . PRDM16 was previously identified as a SMAD binding partner; it is thought to act in downstream mediation of TGFβ signaling in developing orofacial tissues [41] . Consistent with this role , PRDM16 is expressed in both the primary and secondary palate and the nasal septum in mouse embryos [41] . Functional studies in mice confirm a role in craniofacial development , with an N-ethyl-N-nitrosourea-induced mutation in PRDM16 resulting in cleft palate and other craniofacial defects including mandibular hypoplasia [42] . Moreover , variants at the human PRDM16 locus have been implicated in NSCL/P [42] . TP63 encodes a transcription factor belonging to the p53 family , and plays important roles in orchestrating developmental signaling and epithelial morphogenesis . Heterozygous mutations in human TP63 are associated with a number of allelic syndromes characterized by orofacial defects , including Ectrodactyly-Ectodermal dysplasia-Cleft lip/palate and Ankyloblepharon-Ectodermal dysplasia-Clefting [43] . Furthermore , TP63 has been implicated in human NSCL/P [44] , and null mice recapitulate the human orofacial clefting phenotypes [45] . The remaining two loci identified in the discovery sample and replicated in both the SYS and TwinsUK & BLTS samples have no previously known direct involvement in craniofacial development to date . C5orf50 at 5q35 . 1 is predicted to encode an uncharacterized transmembrane protein , which lies within a 1 . 24 Mb duplicated region in a patient with preaxial polydactyly and holoprosencephaly ( HPE ) , a defect in development of the forebrain and midface [46] . The most likely candidate in the duplicated region is FBXW11 , a gene with links to sonic hedgehog signaling , the main pathway affected in HPE [47] . It is therefore possible that variants at the C5orf50 locus influences craniofacial patterning through effects on FBXW11 expression , although it is also feasible that the protein encoded by C5orf50 has a novel , and more direct , effect on the face . Mutations in COL17A1 cause Junctional epidermolysis bullosa ( JEB ) , a genetic blistering condition [48] , however , there is no evidence to date for a direct involvement of this gene in craniofacial morphogenesis . Our data suggest that COL17A1 may play an as yet undefined role in patterning facial tissues . However , the association signal observed for COL17A1 at 10q24 . 3 spans a 300 kb region ( 105 . 7 Mb–106 Mb ) and also harbors other genes including SLK , C10orf78 and C10orf79 . Thus , we cannot exclude the possibility that these genes are responsible for the observed association . Many medical-genetic syndromes show a clear connection between genetic alteration and typical facial gestalt [49] , hence genes involved in affected individuals may also contribute to normal variations in facial shape . Our previous study of 11 NSCL/P associated SNPs [20] showed some borderline significance for association with nose-width and bizygomatic distance but inconsistent effect was observed in two populations studied ( Rotterdam and Essen ) . This discrepancy may be partially explained by sample size and different sources of facial phenotype , namely 3D MRI in the Rotterdam Study and 2D portrait photos in Essen . In the 2D facial pictures in the Essen sample , for instance , the bizygomatic distance was defined indirectly by neighboring landmarks of the face [50] . In the current study using 3D MRI data in a larger sample ( N = 5 , 388 ) , multiple SNPs showed significant association with multiple facial phenotypes , even after an over-conservative Bonferroni correction . Thus , in the present study we established clear links between NSCL/P associated SNPs at 2p21 , 8q24 , 13q31 , and 17q22 and normal facial shape phenotypes , including nose width and facial width , in general populations of European descent . This is in line with previous evidence showing that unaffected relatives of NSCL/P cleft patients have wider upper faces and noses than unrelated individuals [51] , [52] . Together with our GWAS findings at three loci previously known to play a role in orofacial clefting , our data strongly suggest that genetic variants associated with NSCL/P also influence normal facial shape variation . This study is not without limitations . The limited number of landmarks used in this study cannot capture the full range of the complex 3D shape variation in the face . This is partly due to the physical limitation of our MRI data that miss the lower part of the face and partly due to other factors such as partial incompatibility of 3D and 2D image sources for phenotype extraction . Consequently , some of the significant associations based on 3D distances could not be tested in the 2D photo analysis . For instance , the zygion landmarks available in 3D MRI could not be successfully derived in 2D photos . Further , the precision of phenotypes derived from 2D photos is expected to be lower than that from 3D MRIs . This is indicated by the lower twin correlations in 2D photos than in 3D MRI . Phenotypic noise in 2D photos may arise from slight differences in face orientation , an effect that cannot be removed by PS without measuring the 3rd coordinate . Furthermore , different image sizes and pixel resolutions in 2D photos may also influence the phenotyping results . Thus , we used the 2D photo analysis to provide supporting information but cannot be considered as an exact replication . Another concern is that the facial landmarks from the SYS cohort were derived in a previous study [22] based on slightly different definitions compared with the ones from the five discovery cohorts . This may potentially lead to some heterogeneity in replication results . Furthermore , the SYS cohort consists of adolescents . Some of us have shown previously that several facial features continue to change during the male ( but not female ) adolescents [23] . This study included only samples of European ancestry , which reduces the potential risk of false positive findings due to systematic genetic differences between different populations . On the other hand , further investigation in world-wide samples is required to generalize our findings to populations other than Europeans , and to investigate the genetic basis of particular facial phenotypes that are absent from Europeans . In addition , we have only focused on common variants ( MAF>3% ) ; further investigations of less common and/or quite rare variants as for instance available from next generation sequencing data may provide a more complete figure on the genetic basis of facial morphology . In spite of these limitations we have been able to demonstrate that a phenotype as complex as human facial morphology can be successfully investigated via the GWAS approach with a moderately large sample size . Three of the five loci highlighted here map to known developmental genes with a previously demonstrated role in craniofacial patterning , one of which has been unequivocally associated with nasion position in a recent independent GWAS [34] . The remaining two loci map to or close to C5orf50 and COL17A1 , neither of which have previously been implicated in facial development . The associated DNA variants may either affect neighboring genes , or alternatively identify C5orf50 , and COL17A1 as potential new players in the molecular regulation of facial patterning . Overall , we have uncovered five genetic loci that contribute to normal differences in facial shape , representing a significant advance in our knowledge of the genetic determination of facial morphology . Our findings may serve as a starting point for future studies , which may test for allele specific expression of these candidate genes and re-sequence their coding regions to identify possible functional variants . Moreover , our data also highlight that the high heritability of facial shape phenotypes ( as estimated here and elsewhere ) , similar to that of adult body height [53] , involves many common DNA variants with relatively small phenotypic effects . Future GWAS on the facial phenotype should therefore employ increased sample sizes as this has helped to identify more genes for many other complex human phenotypes such as height [53] and various human diseases . Combined with the emerging advances in 3D imaging techniques , this offers the poteintial to advance our understanding of the complex molecular interactions governing both normal and pathological variations in facial shape .
The RS is an ongoing population-based prospective study including a main cohort RS-I [54] and two extensions RS-II and RS-III [55] , [56] , including 15 , 000 participants altogether , of whom 12 , 000 have GWAS data . Collection and purification of DNA have been described in detail previously [57] . A subset of participants were scanned on a 1 . 5 T General Electric MRI unit ( GE Healthcare , Milwaukee , WI , USA ) , using an imaging protocol including a 3D T1-weighted fast RF gradient recalled acquisition in steady state with an inversion recovery prepulse . The following parameters were used: 192 slices , a resolution of 0 . 49×0 . 49×0 . 8 mm3 ( up sampled from 0 . 6×0 . 7×0 . 8 mm3 using zero padding in the frequency domain ) , a repetition time ( TR ) of 13 . 8 ms , an echo time ( TE ) of 2 . 8 ms , an inversion time ( TI ) of 400 ms , and a flip angle of 20° . More details on image acquisition can be found elsewhere [58] . The Medical Ethics Committee of the Erasmus MC , University Medical Center Rotterdam , approved the study protocol , and all participants provided written informed consent . Principal components analysis of SNP microarray data was used to identify ancestry outliers . These were removed before further analyses , and the present sample is of exclusively northern/western European origin . The current study included 3 , 215 RS participants who had both SNP microarray data and 3D MRI . These participants were considered here as two cohorts ( RS1 N = 2 , 470 and RS2 N = 745 ) as they were scanned and genotyped at different times . Adolescent twins and their siblings were recruited over a period of sixteen years into the BLTS at 12 , 14 and 16 years , as detailed elsewhere [59] and as young adults into the QTIMS [60] , [61] . The present study includes a sub-sample of 545 young adults ( aged 20–30 years , M = 23 . 7±2 . 3 years; 79 MZ and 90 DZ pairs , 110 unpaired twins , and 97 singletons , from a total of 332 families ) from QTIMS with 3D MRIs , and 2 , 137 adolescents ( aged 10–22 years , M = 15 . 6±1 . 5 years; 311 MZ and 530 DZ pairs , 44 unpaired twins and 411 singletons , from a total of 1 , 038 families ) from BLTS with 2D portrait photos . 3D T1-weighted MR images were collected at the Centre for Advanced Imaging , University of Queensland , using a 4T Bruker Medspec whole body scanner ( Bruker Medical , Ettingen , Germany ) [61] . 2D portrait photos were taken from a distance of 1–2 meters for identification , with no specific instruction given to smile . Those who had both 3D MRI scans and 2D photos were included in discovery GWAS and excluded from the replication analysis in 2D photos . Over 70% were digital with the remainder being scanned from high quality film . The study was approved by the Human Research Ethics Committee , Queensland Institute of Medical Research . Informed consent was obtained from all participants ( or parent/guardian for those aged less than 18 years ) . The SHIP is a longitudinal population based cohort study assessing the prevalence and incidence of common , population relevant diseases and their risk factors with examinations at baseline ( SHIP-0 , 1997–2001 ) , 5-year-follow-Up ( SHIP-1 , 2002–2006 ) and an ongoing 10-year-follow-Up ( SHIP-2 , 2008–2012 ) [62] , [63] . Data collection from the baseline sample included 4 , 308 subjects . A new cohort targeted 5 , 000 participants ( SHIP-TREND ) has been started parallel to the SHIP-2-Follow-Up . In addition to the baseline examinations , participants of SHIP-2 and SHIP-TREND also had a whole-body MRI scan [64] . MRI examinations were performed on a 1 . 5T MR imager ( Magnetom Avanto; Siemens Medical Systems , Erlangen , Germany ) . Head scans were taken with an axial ultra-fast gradient echo sequence ( T1 MPRage , TE 1900 . 0 , TR 3 . 4 , Flip angle 15° , 1 . 0×1 . 0×1 . 0 mm voxel size ) . The present study includes 797 SHIP as well as 831 SHIP-TREND participants which had both SNP genotyping data and 3D MRI . The medical ethics committee of University of Greifswald approved the study protocol , and oral and written informed consents were obtained from each of the study participants . Adolescent sibpairs ( 12 to 18 years of age ) were recruited from a French-Canadian population with a known founder effect living in the Saguenay-Lac-Saint-Jean region of Quebec , Canada in the context of the ongoing Saguenay Youth Study [65] . Local ethics committee approved the study; the parents and adolescent participants gave informed consent and assent , respectively . MRI was acquired on a Phillips 1 . 0-T magnet using the following parameters: 3D radio frequency spoiled gradient-echo scan with 140–160 slices , an isotropic resolution of 1 mm , TR 25 ms , TE 5 ms , and flip angle of 30° . Outlying individuals including those with putative Indigenous American admixture were excluded based on genetic outlier analysis [66] . The current study contains 568 adolescents with MRI and SNP data . The TwinsUK cohort is unselected for any disease and is representative of the general UK population [67] . All were volunteers , recruited through national media campaigns . Written informed consent was obtained from every participant . Population substructure and admixture was excluded using eigenvector analysis on SNP microarray data . The current study included 1 , 366 individuals with 2D portrait photos and SNP microarray data . In our discovery cohorts , since the lower part of the face was not available from the MRIs , we focused on nine landmarks of the upper face ( Figure 1 ) . These included Right ( ZygR ) and left ( ZygL ) zygion: the most lateral point located on the cortex of the zygomatic arches; right ( EyeR ) and left ( EyeL ) eyeball: the middle point of the eyeball; right ( AlrR ) and left ( AlrL ) alare: the most lateral point on the surface of the ala nasi; nasion ( Nsn ) : the skin point where the bridge of the nose meets the forehead; pronasale ( Prn ) : the most anterior tip of the nose; subnasale ( Sbn ) : the point where the base of the nasal septum meets the philtrum . Although these landmarks provide only a very sparse representation of the facial shape , they cover most prominent facial features and are easy to interpret and compare to other studies [22] , [34] , [51] . Furthermore , these landmarks could be measured with higher accuracy from images than most semilandmarks [22] . The coordinates of these nine landmarks were derived with an automated technique as described previously [20] , which uses image registration to transfer predefined landmarks from a limited set of annotated images to an unmarked image . The manual annotation was based on landmark definitions from the anthropology literature [68] , which were adapted for application to T1-weighted MR images of the head . None of the MRI showed distortions in a visual inspection . Furthermore , the automatically localized landmark positions were robust against the number of samples included . The test-retest correlations based on a subset of 40 subjects from QTIMS who were scanned twice were high ( r>0 . 99 ) . In the SYS cohort , in total 56 facial landmarks were available from a previous quantitative analysis of craniofacial morphology using 3D MRI [22] . In brief , an average MRI was constructed using non-rigid image registration . The surface of this average image represents the mean facial features and was then annotated with 56 landmarks and semi-landmarks . These landmarks were then warped using the nonlinear transformation that maps each subject to the average . This allows for automatic identification of the different craniofacial landmarks . We defined eight landmarks in 2D portrait photos that approximately correspond to the respective landmarks ascertained from our 3D MRIs . These include EyeL , EyeR , Prn , AlrL , AlrR , Nsn , earlobe left ( EarL ) , and earlobe right ( EarR ) ( Figure 5A ) . Note the Sbn , ZygL and ZygR landmarks available in 3D MRIs could not derived in 2D photos . We developed an algorithm to locate these landmarks in 2D portrait images and implemented it in an in-house C++ program . Briefly , the algorithm first recognizes the face layout within an image by matching a face template . It then recognizes eyes , nose , and ears by matching corresponding templates . The template matching routines were based on external open source computer vision library , OpenCV 2 . 3 . 1 ( http://sourceforge . net/projects/opencvlibrary/ ) . The automatically identified landmarks were then manually adjusted by 5 research assistants on a standard computer screen . We used un-scaled PS , or partial PS [24] , [69] , to superimpose the landmarks from the 3D MRIs in the discovery cohorts onto a consensus 3D Euclidean space . Unlike full PS , partial PS only re-positions and re-orientates but does not rescale the landmark configurations; thus , it has no effect on the Euclidean distances between landmarks as measured in terms of millimeters from MRIs . Keeping the absolute inter-landmark distances allows us to interpret the association results more directly . Furthermore , the full PS has been criticized for introducing artificial correlations between landmarks [70] . We considered the centroid size as a measurement of face size , and it was significantly correlated with absolute head volume ( r = 0 . 95 ) . We derived 11 principal components ( PCs ) from the superimposed landmarks , each explaining at least 1% of the total phenotypic variance . We also derived 36 Euclidean distances between all pairs of landmarks . Thus 48 phenotypes were included in our GWAS , including centroid size , 11 PCs , and 36 inter-landmark distances . All phenotypes were approximately normally distributed and outliers ( >3sd ) were removed . Deformation approaches including the use of transformational grids provide an alternate way to study shape difference . Thin plate splines ( TPS ) [27] depicts the deformation geometrically , where the total deformation is decomposed into several orthogonal components to localize and illustrate the shape differences . We used TPS to illustrate the facial shape differences between males and females using the tpsgrid function in R library shapes . For 3D MRI data in SYS , we used the 56 landmarks derived in a previous study and calculated 1 , 540 Euclidean distances between all pairs of landmarks . These distances were considered as phenotypes in our replication analysis of GWAS findings . We also chose a subset of nine landmarks most closely resembled those used for the current study for exact replication . Since the size of the face vary substantially between 2D portrait images , we used the full PS [24] to also remove the scaling differences between landmark configurations . Note that the inter-landmark distances from 2D photos do not represent the absolute distances in terms of millimeters regardless of whether full or partial PS was used . After superimposition , we calculated 28 Euclidean distances between all pairs of the 8 landmarks , which were considered as phenotypes in the replication analysis . The PS analyses were performed with CRAN package shapes developed by Ian Dryden [30] . By clarifying which facial features are under strong genetic control , we should be better able to identify specific genes that influence facial variation . Heritability estimates are also important indicators of the phenotype quality . Using QTIMS ( 79 MZ pairs , 90 DZ pairs ) heritability analysis was carried out in Mx [71] using full information maximum likelihood estimation of additive genetic variance ( i . e . heritability ) , common environmental variance , and unique environmental variance . Sex and age were included as covariates . Phenotypic correlations were estimated in BLTS ( 311 MZ pairs , 90 DZ pairs ) and in TwinsUK ( 93 MZ pairs , 352 DZ pairs ) where the facial shape phenotypes were derived from 2D photos . Details of SNP microarray genotyping , quality control and genotype imputation are described in prior GWAS conducted in RS [16] , QTIMS and BLTS [72] , SHIP [62] , SYS [73] , and TwinsUK [74] . In brief , DNA samples from the RS , BTNS , SYS and TwinsUK cohorts were genotyped using the Human 500–610 Quad Arrays of Illumina and samples from SHIP were genotyped using the Genome-Wide Human SNP Array 6 . 0 of Affymetrix and HumanOmni2 . 5 of Illumina , respectively . Genotyping of the SHIP-TREND probands ( n = 986 ) was performed using the Illumina HumanOmni2 . 5-Quad , which has not been reported previously and described here as follows . DNA from whole blood was prepared using the Gentra Puregene Blood Kit ( Qiagen , Hilden , Germany ) according to the manufacturer's protocol . Purity and concentration of DNA was determined using a NanoDrop ND-1000 UV-Vis Spectrophotometer ( Thermo Scientific ) . The integrity of all DNA preparations was validated by electrophoresis using 0 . 8% agarose-1x TBE gels stained with ethidium bromide . Subsequent sample processing and array hybridization was performed as described by the manufacturer ( Illumina ) at the Helmholtz Zentrum München . Genotypes were called with the GenCall algorithm of GenomeStudio Genotyping Module v1 . 0 . Arrays with a call rate below 94% , duplicate samples as identified by estimated IBD as well as individuals with reported and genotyped gender mismatch were excluded . The final sample call rate was 99 . 51% . Imputation of genotypes in the SHIP-TREND cohort was performed with the software IMPUTE v2 . 1 . 2 . 3 against the HapMap II ( CEU v22 , Build 36 ) reference panel . 667 , 024 SNPs were excluded before imputation ( HWE p-value≤0 . 0001 , call rate ≤0 . 95 , monomorphic SNPs ) and 366 SNPs were removed after imputation due to duplicate RSID but different positions . The total number of SNPs after imputation and quality control was 3 , 437 , 411 . The genetic data analysis workflow was created using the Software InforSense . Genetic data were stored using the database Caché ( InterSystems ) . After SNP imputation to the HapMap Phase II CEU reference panel ( Build 36 ) and quality control , 2 , 558 , 979 autosomal SNPs were common in all discovery cohorts and used for analyses . We conducted discovery phase GWAS in a combined set of all discovery cohorts ( RS1 , RS2 , QTIM , SHIP , SHIP-Trend ) for 48 facial shape phenotypes . Imputed GWAS data in all discovery cohorts were merged according to the positive strand . We tested 2 , 558 , 979 autosomal SNPs with linear regression ( adjusted for sex , age , EIGENSTRAT-derived ancestry informative covariates [75] , plus any additional ancestry informative covariates as appropriate ) in GenABEL [76] . The centroid size was adjusted in the analysis of inter-landmark distances . SNPs with MAF<3% , overall call rate <95% , and HWE P<1×10−3 were not considered for report . Genomic inflation factors were estimated in range 1 . 0–1 . 03 for all studied phenotypes . The observed P-values were Q-Q plotted against the expected P-values at −log10 scale . We considered the traditional threshold of 5×10−8 as being genome-wide significant since many phenotypes were highly correlated . All SNPs in this study were annotated based on NCBI build 36 . 3 . The linear modeling used here separately analyzes each facial phenotype . It is also possible to derive a global P-value for testing the shape difference between different genotype groups using other approaches , such as the Euclidean Distance Matrix Analysis ( EDMA ) [28] , [29] and the multivariate analysis of variance ( MANOVA ) [77] . The EDMA computes a score of the maximal ratio [28] or difference [29] between the mean shapes estimated in two groups . Since this score does not follow a known distribution , the statistical significance is derived by bootstrapping all landmark configurations . In the context of GWAS , this bootstrap procedure should be conducted for every SNP , which turned out to be computationally heavy when we attempted to implement it at the genome-wide scale . In addition , EDMA is less flexible than linear modeling when the effects of covariates are to be adjusted and when more than two genotype groups are to be compared . The MANOVA is a classic statistical method for analysis of multiple correlated response variables , which has been shown to be useful in GWAS [78] . We implemented MANOVA for GWAS in R and conducted a GWAS for the residuals of the 11 facial shape PCs after regressing out the effect of sex , age , and population stratification . However , no significant signal ( P<5×10−8 ) was observed for SNPs with MAF>3% ( results not shown ) . All SNPs with P values<5×10−8 in our discovery phase GWAS were sought for replication in SYS , TwinsUK , and BLTS . Promising SNPs were tested for association with 1 , 054 inter-landmark distances in SYS and 28 inter-landmark distances in a combined sample of TwinsUK and BLTS assuming additive allelic effect adjusted for sex and age using MERLIN [79] , which also takes into account family relationships . We report the association results for the same phenotypes as discovered in GWAS as exact replication . In addition , for each SNP we report the percentage of significantly ( P<0 . 05 ) associated phenotypes , which is expected to be lower than 5% under the null hypothesis of no association . For the analysis of 11 NSCL/P associated SNPs in our discovery cohorts , we additionally Bonferroni corrected the P values for 48 correlated phenotypes since no specific facial phenotypes were selected for replication . | Monozygotic twins look more alike than dizygotic twins or other siblings , and siblings in turn look more alike than unrelated individuals , indicating that human facial morphology has a strong genetic component . We quantitatively assessed human facial shape phenotypes based on statistical shape analyses of facial landmarks obtained from three-dimensional magnetic resonance images of the head . These phenotypes turned out to be highly promising for studying the genetic basis of human facial variation in that they showed high heritability in our twin data . A subsequent genome-wide association study ( GWAS ) identified five candidate genes affecting facial shape in Europeans: PRDM16 , PAX3 , TP63 , C5orf50 , and COL17A1 . In addition , our data suggest that genetic variants associated with NSCL/P also influence normal facial shape variation . Overall , this study provides novel and confirmatory links between common DNA variants and normal variation in human facial morphology . Our results also suggest that the high heritability of facial phenotypes seems to be explained by a large number of DNA variants with relatively small individual effect size , a phenomenon well known for other complex human traits , such as adult body height . | [
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] | 2012 | A Genome-Wide Association Study Identifies Five Loci Influencing Facial Morphology in Europeans |
The anesthetic propofol elicits many different spectral properties on the EEG , including alpha oscillations ( 8–12 Hz ) , Slow Wave Oscillations ( SWO , 0 . 1–1 . 5 Hz ) , and dose-dependent phase-amplitude coupling ( PAC ) between alpha and SWO . Propofol is known to increase GABAA inhibition and decrease H-current strength , but how it generates these rhythms and their interactions is still unknown . To investigate both generation of the alpha rhythm and its PAC to SWO , we simulate a Hodgkin-Huxley network model of a hyperpolarized thalamus and corticothalamic inputs . We find , for the first time , that the model thalamic network is capable of independently generating the sustained alpha seen in propofol , which may then be relayed to cortex and expressed on the EEG . This dose-dependent sustained alpha critically relies on propofol GABAA potentiation to alter the intrinsic spindling mechanisms of the thalamus . Furthermore , the H-current conductance and background excitation of these thalamic cells must be within specific ranges to exhibit any intrinsic oscillations , including sustained alpha . We also find that , under corticothalamic SWO UP and DOWN states , thalamocortical output can exhibit maximum alpha power at either the peak or trough of this SWO; this implies the thalamus may be the source of propofol-induced PAC . Hyperpolarization level is the main determinant of whether the thalamus exhibits trough-max PAC , which is associated with lower propofol dose , or peak-max PAC , associated with higher dose . These findings suggest: the thalamus generates a novel rhythm under GABAA potentiation such as under propofol , its hyperpolarization may determine whether a patient experiences trough-max or peak-max PAC , and the thalamus is a critical component of propofol-induced cortical spectral phenomena . Changes to the thalamus may be a critical part of how propofol accomplishes its effects , including unconsciousness .
Propofol is one of the most popular intravenous anesthetics [1] . Despite its ubiquity , the neural mechanisms by which it disables consciousness remain poorly understood [2 , 3] . Characterization of the critical mechanisms of propofol could enable creation of better targeted anesthetics , identification of consciousness-disabling pathways , and more advanced tools to evaluate depth of anesthesia . This paper presents a novel thalamic sustained alpha rhythm , explores the limits of these thalamic intrinsic oscillations , and describes a thalamocortical origin of propofol phase-amplitude coupling regimes . The many electroencephalogram ( EEG ) spectral properties of propofol , illustrated in Fig 1 , may provide insight into how it accomplishes its effects . Beginning at patient loss of consciousness ( LOC ) , its EEG profile consists of a rise in Slow Wave Oscillation power ( SWO , 0 . 1–1 . 5 Hz ) [2 , 4–6] and beta power ( 14–20 Hz ) [7 , 8] that decays to alpha power ( 8–12 Hz ) [3 , 9 , 10] for the duration of the anesthesia . Recently , the SWO phase and alpha amplitude were found to exhibit phase-amplitude coupling ( PAC ) [6 , 11 , 12]: around time of LOC , alpha amplitude is maximum at the SWO trough ( trough-max ) , but as the dose increases and the patient experiences deeper anesthesia , the alpha amplitude switches to maximize at the SWO peak ( peak-max ) . How propofol controls alpha-SWO PAC is still a mystery , but investigating the neural mechanisms of propofol-induced spectral properties may allow discovery of the key components needed for LOC provided by propofol . Propofol rhythms are similar in frequency to sleep rhythms associated with the thalamocortical loop , including SWO [2 , 13 , 14] and sleep spindles ( 10–16 Hz ) [9 , 15] . Mathematical models have played a prominent role in understanding thalamocortical rhythms in both sleep and anesthesia . There is a strong history of modeling SWO [16–21] , especially in cortex [22–24] , and likewise for thalamic spindle generation [25 , 26] . More recently , many facets of propofol rhythms have been modeled , including transient propofol beta [8] , burst-suppression [27] , corticothalamic entrainment of alpha [28] , alpha anteriorization [29] , and neuromodulatory impacts on propofol SWO [21] . However , the fundamental source of propofol alpha , its relationship to anesthetic hyperpolarization , and its relationship to the GABAA potentiation of propofol are all unknown . Specifically , while we have previously modeled propofol alpha arising from propofol GABAA potentiation and thalamocortical entrainment of this propofol alpha [28] , we did not investigate the mechanisms of its creation . Additionally , there have been no modeling studies examining either the mechanistic relationship between SWO and alpha under propofol ( including their PAC ) , why this PAC is affected by dose , or the effects of direct hyperpolarization on these dynamics in this system . Since propofol alpha appears to be sustained over the entire course of anesthesia [11 , 12 , 30] and propofol alpha has been detected in thalamic recordings [31–35] , the relationship of that rhythm to waxing-and-waning spindle oscillations is also unclear . To better understand how propofol sustained alpha and alpha-SWO PAC are generated , we simulated a thalamic , Hodgkin-Huxley network model under corticothalamic SWO input . We found that the thalamus is able to generate a sustained alpha rhythm independently of cortical input . While thalamic intrinsic oscillations are sensitive to both thalamocortical ( TC ) cell H-current maximal conductance ( gH ) and background excitation ( excitation from brainstem and cortex ) [36 , 37] , only under strong propofol GABAA potentiation did we find a persistent , sustained alpha rhythm . This thalamic sustained alpha may be relayed up to cortex , where it is subsequently seen on the EEG similar to that seen under propofol unconsciousness in the operating room . Our simulations also showed that the thalamus may control the phase of the alpha coupling to SWO . Surprisingly , changes to the background excitation alone could control the PAC regime of the system between trough-max and peak-max . Our analysis of the mechanisms of propofol PAC could help differentiate the roles of thalamus and cortex in propofol phenomena , leading to better understanding of anesthetic action and the components of consciousness .
We began by investigating sustained thalamic alpha; this is the first step in understanding alpha-SWO PAC , in which the alpha rhythm may occur in only parts of the SWO phase . For all simulations , unless otherwise noted , we considered a 50 thalamocortical cell ( TC ) and 50 reticular cell ( RE ) computational thalamic model for our thalamus ( see Methods ) , inherited from [28] , itself derived from a well-established model [37] . Under "baseline" conditions ( no propofol ) , the model thalamus did not produce any intrinsic oscillatory activity , but instead settled into a stable , silent depolarized state after a transient from initial conditions ( Fig 2A ) . To model low-dose propofol , we doubled both the maximal GABAA conductance , gGABAA , and the decay time constant of GABAA inhibition , τGABAA , throughout the system [8 , 28]; to model high-dose propofol , we tripled both gGABAA and τGABAA . Simulating the same baseline system as above , but under either low-dose or high-dose propofol conditions , we found sustained , persistent alpha firing ( Fig 2B , 2C and 2D ) . Synaptic effects from propofol can enable sustained alpha firing in the thalamus , and we explore how this comes from intrinsic properties of the thalamus . The thalamus is known to display a range of behaviors , largely dependent on the excitation level of the system , TC cell gH , and TC cell T-current window interaction: tonic spiking , silent depolarization , hyperpolarized oscillation including spindling and SWO , and silent hyperpolarization [36 , 37] . This raises the question of whether sustained alpha oscillation can emerge in "normal" thalamus , especially in oscillations near the alpha frequency range , such as spindles . Because both the level of background excitation , also known as applied current , and the level of gH are known to be variable and alter the dynamical state of the thalamus [16 , 36 , 37] , we simulated across both of these dimensions to search for regions of sustained alpha oscillations . Note that our use of background excitation is not meant to model direct inhibition into the system , but rather the sum of hyperpolarizing effects from changes to neuromodulators , second-order neuromodulatory effects , and loss of brainstem excitation . Since we did not know how weakly or strongly hyperpolarizing the anesthetic was to the system , we modeled the sum of these effects as voltage-invariant tonic charge changes to investigate the entire dimension . Thus , negative background excitation represents tonic hyperpolarization of the system . Over the entire physiological range of the gH-background excitation plane , sustained alpha oscillations did not emerge under baseline conditions ( Fig 3 ) . However , alpha transients in the form of spindles occurred . These were distinguished from propofol sustained alpha by their waxing and waning nature . Although propofol decreases gH [38] , these results indicate that decreasing the H-current alone is not sufficient to enable sustained alpha . Similarly , by varying background excitation to model ascending brainstem neuromodulation , we also found this was not sufficient to enable sustained alpha . Therefore , sustained alpha is likely not a normal thalamic rhythm , and propofol GABAA potentiation is likely to be responsible for generating sustained alpha . Our simulations so far suggest that potentiation of GABAA by propofol enables sustained alpha ( Fig 2B , 2C and 2D ) , but we have yet to demonstrate the robustness of these propofol-induced oscillations to changes in gH and background excitation . To do this , we analyzed the behavior regimes of each simulation under low-dose and high-dose propofol across the physiological gH-background excitation plane in ( Fig 4B and 4C ) . In baseline simulations in Fig 4A , as background excitation increases , the system shifts from sub-alpha oscillations and transients/spindling into silent depolarization . When propofol is applied in low-dose and high-dose propofol planes ( Fig 4B and 4C ) , increasing the background excitation enables the system to fire in sustained alpha oscillations . The sustained alpha oscillations emerge from the same region of the gH-background excitation plane , and even at low-dose propofol it comprised roughly the same area as all other intrinsic oscillations combined . The size of this sustained alpha "firing area" roughly doubled when the GABAA potentiation of propofol was tripled ( high-dose , Fig 4C ) compared to doubled ( low-dose , Fig 4B ) . The region of sustained alpha expanded into states where the background excitation was even higher . This effect was due to increasing the dynamic range over which excitation and inhibition were balanced ( see next subsection ) . However , other than this increased propensity for sustained alpha firing , there were no major differences between the sustained alpha oscillations of low-dose versus high-dose propofol . For this reason , when we discuss mechanisms of the sustained alpha , we do not differentiate between sustained alpha coming from low-dose versus high-dose propofol simulations and may only show high-dose simulations in the interest of brevity . Note that , as in the baseline simulations , the gH of the system must be below some threshold , ~0 . 024 mS/cm2 , in order for any intrinsic thalamic activity to occur , including sustained alpha . Therefore , it is necessary but not sufficient to decrease gH below this threshold to produce propofol-induced sustained alpha . By applying low-dose or high-dose propofol via , doubling or tripling both gGABAA and τGABAA , respectively , we find that the synaptic GABAA effects of propofol enable sustained alpha in a dose-dependent manner . Having shown that GABAA potentiation by propofol can lead to robust sustained alpha oscillations in the model , we next explored the dynamical mechanisms underlying its production in thalamic networks . As we compare high-dose propofol simulations to their baseline counterparts in Fig 5 , there are two main facets to how propofol enables sustained alpha in this thalamic system: engaging thalamic spindling dynamics and balancing enhanced inhibition with excitation . Propofol takes advantage of intrinsic spindling dynamics in producing sustained alpha . During high-dose propofol , sustained alpha was present where there were either transients/spindles or silent depolarization in the baseline case ( Fig 5 ) . In the case of transients/spindling , Fig 5A shows both the voltage traces of a terminating spindle at baseline and sustained alpha at high-dose propofol; these simulations correspond to the same point in the gH-background excitation plane and are differentiated only by whether propofol has potentiated GABAA synapses or not . As in prior modeling work [26 , 37] , the spindle at baseline wanes due to Calcium-based up-regulation of the H-current , as can be seen by the increasing magnitude of current in ( Fig 5D ) . At baseline , this slow , depolarizing current raises the baseline TC cell membrane potentials above the T-current activation window such that bursting cannot occur ( Fig 5B ) . In contrast , when GABAA is potentiated by propofol , sustained alpha does not wane , since the RE inhibition consistently hyperpolarizes TC cells into the T-current window , enabling T-current bursts to occur ( Fig 5B ) . Note that the H-current is more active in the high-dose propofol case than at baseline even though the maximal conductance is the same ( Fig 5D ) , yet the H-current is not strong enough to overcome the enhanced RE inhibition of propofol ( up to a point of gH > 0 . 024 mS/cm2 , see previous subsection ) . Similarly , in Fig 5H through 5K , propofol enabled sustained alpha oscillations in parameter regimes where , due to the strength of the background excitation under baseline conditions , there would otherwise be silent depolarization . This new sustained alpha occurred for the same reason: the enhanced inhibition forces TC membrane potentials into the T-current window . The second major facet of propofol-sustained alpha concerns how propofol changes the balance of excitation and inhibition . The fundamental cycle of the excitatory inputs to this system ( TC cell H-current and positive background excitation ) balanced against the inhibitory inputs ( propofol GABAA potentiation and negative background excitation ) is illustrated in Fig 5G . TC cells fire and activate RE cells , which burst and have their inhibition onto TC cells magnified by propofol . This greater inhibition paradoxically increases the probability of a TC cell burst by hyperpolarizing the TC cell membrane potentials into the T-current window . Upon entering the T-window for long enough , the T-current de-inactivation state variable ( hT ) builds up , and time until the next burst is decreased by depolarizing effects such as positive background excitation and TC H-current . The amount of augmenting depolarization affects the frequency of the oscillation , determining if the propofol-infused system oscillates at sub-alpha frequencies like theta or delta , or sustained alpha . The TC bursts again , and the cycle is reset . The enhanced inhibition is also the reason for the dose-dependent increase in sustained alpha area on the gH-background excitation plane in Fig 4B and 4C: greater RE inhibition by high-dose vs . low-dose propofol allows high-dose propofol to enable sustained alpha oscillations under a broader range of stronger background excitation . Increases in propofol from baseline lead to an increase in network frequency up to a maximum ( Fig 6 ) ; the sustained alpha emerges at the peak , and its frequency decreases with further propofol potentiation . Increases in background excitation increase the thalamic oscillation frequency up to its network frequency maximum , after which the system is too depolarized to oscillate . These results are consistent with alpha being the maximum thalamic frequency possible via hyperpolarized intrinsic oscillations [28] . We have shown how propofol enables thalamic sustained alpha , but in order to analyze propofol PAC between alpha and SWO , we constructed a model of corticothalamic SWO UP and DOWN states in the thalamus . UP states have two effects on the thalamus: cortical firing ( CF ) and a tonic depolarization step , while DOWN states have no cortical firing ( NCF ) and do not include a tonic depolarization step [14 , 20] . All of our simulations so far have no spiking inputs and can be used to model NCF states . To model CF , we simulated gH-background excitation behavior planes for all three dose levels under the influence of an AMPA-ergic 12 Hz Poisson spiking process [28 , 39] . The behavior under low-dose and high-dose conditions are respectively shown in Fig 7A and 7E . As a result of introducing CF to the thalamic network , the system oscillates throughout more of the parameter space , and slightly less depolarization is needed to elicit thalamic oscillation , but the CF does not significantly alter the sustained alpha oscillation or overall behavior . Separately , we can model any tonic depolarization steps as increases in background excitation , so the depolarizing step component of the shift from DOWN to UP is accounted for if the system moves to the right on the background excitation axis as in Fig 7A and 7C . Similarly , a hyperpolarizing step from UP to DOWN is identical to moving the state of the system from the right to the left on the background excitation axis as in Fig 7A and 7C . Additionally , as dose increases from trough-max to peak-max PAC , we can model the progressive loss of brainstem excitation simultaneously as an overall decrease in background excitation of both UP and DOWN states . By analyzing the thalamic networks across the gH-background excitation plane , propofol dose , and CF/NCF , we can compare UP states to DOWN states , therefore enabling steady-state SWO modeling of the thalamic network . During trough-max , we found our thalamic model could couple its alpha oscillation to the trough of the cortical SWO input , driving cortical alpha power trough-max PAC ( Fig 7A through 7D and illustrated in Fig 8 ) . Under low-dose , trough-max conditions , the thalamic DOWN state is hyperpolarized such that it expresses sustained alpha even though there is no cortical input . Note that this is a change in hyperpolarization through decreasing background excitation , not a change in gH . This is illustrated in Fig 7B and 7C , and a simulation is shown in the DOWN states of Fig 7D . Under a thalamic UP state , however , the incoming CF and increase in background excitation depolarize the TC cells out of their sustained alpha regime , forcing them into their silent depolarized state as shown in Fig 7A . Thus , the cortex will receive strong alpha input from the thalamus only during its DOWN states , when the thalamus is independent , and this thalamic alpha input will cease during the UP states , allowing the cortex to experience trough-max PAC . Similarly , during peak-max PAC , we found the thalamic network could be the alpha driver on the cortical SWO peak ( Fig 7E through 7H ) . As the high-dose thalamus can be more hyperpolarized than the low-dose state , the DOWN state of the thalamus experiences silent hyperpolarization as in Fig 7G . However , upon receiving CF and depolarization from the cortical UP state , the thalamic UP state exhibits sustained alpha output ( Fig 7E ) . The cortex receives no thalamic input during DOWN states but receives sustained alpha thalamic input during UP states , producing peak-max PAC observable in the cortex . Surprisingly , we also found that a change in only thalamic background excitation could enable switching between trough-max and peak-max PAC . Keeping the state of the system steady on the gH-background excitation plane , increasing GABAA potentiation was insufficient to switch between trough-max and peak-max . While UP/DOWN alternation in the low-dose propofol thalamus can explain cortical trough-max PAC ( Fig 7A through 7D ) , a decrease in the overall level of background excitation would cause the thalamic UP states to exhibit sustained alpha while the DOWN states exhibit silent hyperpolarization , enabling peak-max PAC even at this lower dose regime . Thus , peak-max PAC is possible even under a very hyperpolarized thalamus with only moderate GABAA potentiation . Tripling the GABAA potentiation ( high-dose ) instead of doubling increased the probability of sustained alpha but could not account for a PAC shift under alternating SWO states by itself .
Human EEG studies have shown that , near the onset of and during LOC from propofol , the brain produces an alpha oscillation and a SWO [30] . These are coupled differently depending on the depth of anesthesia: at the onset or offset of LOC , the alpha rhythm appears in the trough of the SWO ( “trough-max” ) , while at a deeper level of anesthesia , the alpha appears at the peak of the SWO ( “peak-max” ) [12] . In this study , we have used thalamic Hodgkin-Huxley-type simulations of the effects of propofol on gH , GABAA conductance , and decay time , as well as changes in background excitation , to investigate how the thalamus can control such alpha-SWO PAC . We found that the hyperpolarized thalamus exhibited a novel thalamic sustained alpha rhythm that only occurs under GABAA potentiation , as in propofol . Furthermore , depending on thalamic hyperpolarization level , the thalamus expressed alpha during either the corticothalamic SWO trough , producing trough-max output , or during the corticothalamic SWO peak , producing peak-max output . The first central result is that the thalamus alone is able to produce a sustained alpha rhythm under potentiated GABAA inhibition ( Fig 2 ) . Thus , we propose a novel thalamic rhythm not observed under normal/awake or sleep conditions , one that requires potentiation of GABAA receptors . This sustained alpha is fundamentally different than native thalamic spindling due to its lack of waxing-and-waning; it lasted indefinitely , as long as our longest simulations ( 10 seconds ) . Secondly , this sustained alpha can be produced in the absence of oncoming SWO; this second finding extends the work of [28] by showing the how thalamus alone , independent of patterned cortical input , can robustly produce sustained alpha in the presence of propofol . The baseline model was able to produce spindling and lower-frequency rhythms , including delta and theta . However , at no level of depolarization , hyperpolarization , or gH could it produce a sustained alpha rhythm ( Fig 3 ) . Only propofol potentiation of inhibition , by increasing GABAA conductance and decay time , led to an ongoing alpha-frequency rhythm . The propofol-induced sustained alpha used the physiological building blocks of the spindling rhythm [25 , 37] . The mechanism was found to involve the T-current: the added RE cell inhibition led to greater de-inactivation of the T-current in the TC cells , leading to faster and more reliable TC bursts up to a sustained alpha frequency . With the potentiated inhibition , the sustained alpha appears in regions of parameter space where , in the baseline model without propofol , the network is spindling or silent and depolarized ( Fig 4 ) . Sustained alpha thus appears between regions of intrinsic oscillations and silent depolarization , created by TC cell membrane potential interactions with the T-current de-inactivation window . An interesting implication of these results is that direct application of propofol onto hyperpolarized thalamic circuits may be sufficient to produce sustained alpha . Since the thalamically-generated sustained alpha is expressed in the TC cells , which project to cortex , our models predict that the thalamic alpha could be the source of the coherent frontal alpha observed during anesthetic doses of propofol [11 , 30] . Our models further suggest that the thalamic alpha is generated independently of propofol-mediated changes to cortical or brainstem circuits , except for the necessary thalamic hyperpolarization . If this thalamic sustained alpha is consistently relayed to cortex , then we expect the oscillation to be detectable on the EEG . This prediction could be tested by infusion of propofol into the rodent thalamus alone , along with a hyperpolarizing agent , while recording cortical LFP or EEG and behavior . Importantly , such experiments may help further characterize the relative role of thalamic alpha in the production of unconsciousness . Our results also suggest that alpha oscillations are the maximum intrinsic oscillatory frequency achievable by an independent , hyperpolarized thalamus , and this occurs only under GABAA potentiation e . g . by propofol . The frequency of the ongoing oscillation increased with the conductance and/or decay time of inhibition but not beyond its sustained alpha peak at 300% of the baseline values , the values theoretically associated with high-dose propofol . This supports earlier computations in [28] , which showed that , as the decay time increased to 300% , the frequency decayed to an asymptotic value . Increasing the GABAA potentiation to 300% also led to a larger region of parameter space displaying sustained alpha , effectively increasing the probability of the rhythm occurring . Our study suggests that the H-current effectively acts as a switch between the existence or absence of sustained alpha , and thus potentially as a switch between the conscious and unconscious states . This is because sustained alpha is possible only if the H-current falls below a threshold level . Note that the H-current is an intrinsic property of the TC cells and is separable from the level of hyperpolarization of the system . The thalamic H-current plays a permissive role in our simulations: intrinsic oscillations , including sustained ones , are possible only when gH is adequately small , as in [37] . Such persistent activity is possible even when this conductance is almost zero , provided there is some depolarizing background excitation . The effect of propofol on thalamic H-currents is , however , controversial [38 , 40 , 41] . An H-current switch could be an important component of other anesthesias; sevoflurane has a very similar EEG profile to propofol [42] , and may inhibit the thalamic H-current [43] . Conversely , this finding has important implications for potentially reversing the anesthetic state , suggesting that agents that increase the H-current in thalamic circuits will shift thalamic dynamics out of the region of sustained alpha oscillation and into the more depolarized awake/relay states . There are many neuromodulators of the H-current , including dopamine , norepinephrine , and serotonin [44] , and these are widely known to be involved in the sleep-wake cycle; thus their H-current effects may affect consciousness . Our models suggest that the level of thalamic hyperpolarization is the critical factor determining trough-max versus peak-max coupling as anesthetic dose is increased . Note that , while propofol GABAA potentiation is necessary for sustained alpha to appear , changing the inhibition level is not sufficient for switching between PAC states . To achieve peak-max and trough-max coupling , we added another component to our model: cortical spiking , looking separately at thalamic dynamics under the influence of either UP or DOWN states coming from cortex [14 , 20] . Whereas DOWN state thalamus simply received no cortical input , the UP state thalamus received both a depolarization step , represented by an increase in background excitation , and cortical spiking . Of note , the UP and DOWN states were modeled the same for all levels of propofol , suggesting that trough-max and peak-max coupling could occur independently of changes to SWO properties . The trough-max coupling can be produced by introducing propofol GABAA potentiation to a hyperpolarized thalamus during a cortical DOWN state , enabling a sustained alpha . The corresponding thalamic UP state is too depolarized to express alpha oscillations , so the alpha becomes phase-locked to the SWO trough in the cortical DOWN state . By contrast , peak-max coupling is possible from hyperpolarizing the thalamus more strongly ( discussed below ) : the input from the corticothalamic UP state helps to compensate for the lower voltage level of the thalamus , enabling alpha oscillations only at the thalamic UP states . Thus , in peak-max , thalamocortical alpha oscillation occurs only during the peak of the cortical UP state , and there is no major thalamocortical signal during the cortical DOWN state . Since sustained alpha requires TC cell membrane potential interaction with the voltage-defined T-current window of de-inactivation , the thalamus can switch from trough-max to peak-max merely by becoming more hyperpolarized ( and vice versa ) , even if the cortical activity remains the same . Increasing thalamic hyperpolarization with propofol dose , an assumption of our model and a critical component of propofol-induced PAC , is supported by several lines of evidence . In our models , peak-max occurs during decreased , direct background excitation to thalamus that may result from the increased action of propofol on brainstem circuits at high doses [3 , 45 , 46] . Decreasing the background excitation term may represent potentiation of the potassium leak conductance ( gKL ) from those brainstem neuromodulatory effects , such as decreasing acetylcholine ( ACh ) output , increasing gKL , thus leading to hyperpolarization [47 , 48] . Recent modeling work has looked at the effects of varying gKL as a proxy for endogenous ACh and histamine ( HA ) changes in thalamic circuits , finding that spindle and SWO oscillations can be generated at certain levels of gKL [21] . Additionally , physostigmine , a cholinesterase inhibitor , has been found to reverse unconsciousness caused by propofol , ostensibly by enhancing ACh activity [49] . In the future , we plan to model propofol SWO directly , enabling better understanding of the PAC phenomenon and allowing us to understand the difference between general hyperpolarization and gKL on the thalamic and cortical systems . This evidence points to the importance of propofol effects in both the thalamus and the brainstem for determining the PAC . Regardless of hyperpolarization level , there must be thalamic GABAA potentiation from the propofol to enable the creation of sustained alpha oscillations in the thalamus . Similarly , even if there are sustained alpha oscillations , the thalamus will only exert different PAC regimes if brainstem neuromodulation dynamically hyperpolarizes the system . We initially thought that both of these changes would have similar effects , allowing us to model both GABAA potentiation and hyperpolarization simultaneously , but this was not the case: the phasic component of GABAA potentiation is critical to creating sustained alpha ( see Fig 5 ) . The model suggests that trough-max alpha may be more coherent more than peak-max alpha , since trough-max alpha is intrinsically generated by the thalamus , whereas cortical input during peak-max may interfere with the thalamically generated alpha ( Fig 8 ) . During trough-max coupling , individual thalamic cells synchronize their sustained alpha bursts due to RE cell synchronization . This greater synchronization in trough-max coupling could increase alpha coherence in cortex and could partially explain the high frontal alpha coherence seen in trough-max [11 , 12 , 42] . The increased probability of sustained alpha firing in parameter space under peak-max alpha may account for the fact that peak-max alpha is found in more regions across the cortex than trough-max [11 , 12] . Further experimentation is needed to distinguish between the mechanisms behind such different alpha coherence in cortical circuits during trough-max and peak-max states . This work has implications for other anesthetics and sedatives . Dexmedetomidine works in ways similar to sleep pathways by removing excitation to the thalamus and cortex [3 , 10 , 33]; experimentally , it produces spindling and SWO at higher doses [50] but not a strong , persistent alpha band that occurs in every SWO cycle . Our model explains the lack of sustained alpha with dexmedetomidine by the fact that it is not just thalamic hyperpolarization that is necessary but also the change in the time scale of the inhibition produced by GABAA potentiation . Benzodiazepines , which do change the GABAA time constant of inhibition , can produce beta or alpha oscillations depending on the dose [51 , 52] . Another GABAA potentiator , sevoflurane also produces coherent frontal alpha and SWO simultaneously [42] . Recent sevoflurane experiments in rodents have shown that alpha oscillations in the thalamus lead the phase of those in the cortex [53] , suggesting a thalamic source of alpha . Our model suggests that because sevoflurane induces GABAA potentiation [54] , probable thalamic hyperpolarization , and possible thalamic H-current inhibition [43] , sevoflurane alpha could also be capable of trough-max and peak-max coupling to SWO . Propofol-induced unconsciousness has been associated with SWO [4] , alpha oscillations [9] , and their PAC [11] . Our model predicts experimental manipulations that could dissociate alpha and PAC from the SWO component , delineating the role of each of these oscillations in the production of the unconscious state . Intracranial recordings have shown that humans experience LOC under propofol within seconds of SWO power manifesting [4] , but trough-max PAC has also been found to occur immediately around LOC [11 , 12] . It is still unknown which , if any , of these spectral phenomena are causes rather than correlates of unconsciousness . It is possible that strong rhythmic synchronization of the thalamus alone , anywhere in the intrinsic oscillation range of SWO to sustained alpha , could be sufficient to disable consciousness . That is , unconsciousness could simply be the effect of disabling the relay function of the thalamus . In the case of propofol , sevoflurane , and similar anesthesias , the thalamocortical delivery of very coherent frontal alpha oscillations could actively disable higher-order thinking and possibly even arousal . However , the relationship of PAC regimes to different levels of consciousness is still controversial [12 , 55] . Peak-max and trough-max do not just represent different doses of propofol [11] , but also exhibit different levels of spatial coherence across oscillations [12] . Previous propofol PAC analysis [12]concluded: “…trough-max coupling of alpha amplitude with [low-frequency activity] phase was likewise concentrated at frontal electrodes…However , peak-max coupling dominated activity throughout frontal , temporal , and posterocentral regions” [12] . During trough-max , relay of propofol alpha to the frontal cortex specifically may interfere with self-awareness and other properties of consciousness , while still allowing for limited thalamocortical communication involving other parts of cortex . In this low level of unconsciousness immediately following LOC , a strong enough stimulus may be enough to retain or re-enable responsiveness . In fact , in a recent experiment [55] , propofol-anesthetized patients were found to respond when experiencing a strongly noxious stimulus , but only when their EEG showed trough-max PAC or no PAC at all . In contrast , peak-max PAC may maintain a broadly disconnected state across the entire cortex , given both the higher power of its SWO oscillations and the broad distribution of peak-max PAC [12] . Since peak-max PAC disables responsiveness even under noxious stimuli [55] , but SWO exists globally in both trough-max or peak-max , it may be that global alpha or PAC are the critical factors for disabling responsiveness and consciousness under peak-max PAC . Our model predicts that localized injection of propofol into a rodent along with hyperpolarizing the thalamus , possibly via localized injection of a gKL agonist , should cause strong thalamic and frontally coherent alpha to emerge , with or without SWO . If there is no accompanying SWO but the animal still loses consciousness , this would point to a clear , non-SWO , rhythmic way to modulate arousal that is used by propofol and sevoflurane . Our models additionally suggest that propofol SWO may be generated from cortex . How propofol induces SWO is unknown , although both cortical and thalamic generation of SWO has been documented in normal sleep states [14 , 20 , 56] . In our model , intrinsic thalamic sustained alpha does not co-occur with lower frequency rhythms . This suggests that , if the anesthetized thalamus is dominated by sustained alpha , propofol SWO could be initiated exclusively by cortex . This is supported by the local , but not global , synchronization of propofol SWO in human intracranial data [4] , as globally synchronous sleep SWO is thought to depend on thalamic relay of SWO [14 , 56] . This propofol SWO generation and its importance for modulating arousal could be tested by injecting propofol into only the cortex or the cortex and brainstem of a rodent . We predict that one would see propofol SWO , but no strong , sustained alpha; it is possible there would still be activity in the alpha range , however , representing endogenous spindles , owing to the brainstem-induced hyperpolarization of the thalamus . Moving forward , modeling SWO directly is critical to understanding the PAC phenomena better . Many different mechanisms that have been found to successfully model slow waves in cortex [16 , 19–21 , 24 , 57] et al . , and propofol slow waves may use an unknown , novel mechanism . The complexity of understanding propofol SWO generation was beyond the scope of this work , but in the future we plan to explore the many mechanisms possible for this , and how properties of these mechanisms could work in tandem with alpha mechanisms to produce propofol PAC characteristics . Despite not modeling cortical SWO generation under propofol directly , we feel it is an interesting finding that our model thalamus can respond to cortical SWO with the appropriate phase amplitude coupling seen in increasing doses of propofol . This finding not only makes immediate experimental predictions , including that alpha may only be observed in subthreshold voltage traces of cortical neurons during trough-max , but it also provides constraints for how cortical UP and DOWN states of trough- and peak-max may be robust to thalamic alpha inputs . We conclude that the thalamus may be the generator of this unique sustained alpha rhythm seen in propofol and the subcortical source of the cortically-detected alpha oscillation seen on EEG , and that propofol-induced PAC may be influenced by the specific effects of propofol on thalamic circuits . Further investigation based on this theory may enhance our understanding of the role of the thalamus and cortex for both anesthesia and consciousness in general .
Our model is almost identical to the thalamic model used in [28] , which is based on [37] . Our model network is illustrated in Fig 9C . The only nontrivial changes were differences in the size of the network and our inclusion of a weak RE to TC GABAB current , but these do not dramatically change the behavior of the system . All of our simulations used 50 TC and 50 RE Hodgkin-Huxley single-compartment cells connected all-to-all . See the S1 Appendix for all the equations and their baseline parameterizations . As in [28] , we used a relatively high gKL to simulate a baseline hyperpolarized thalamus . While many neuromodulators act on gKL [47 , 48] , we explored the impact of directly varying background excitation instead . Modeling the effect of background excitation instead of gKL lets us more broadly model uncharacterized brainstem input such as neuromodulators , second-order effects from neuromodulators , and loss of brainstem excitation . Using background excitation also lets us model these hyperpolarizing changes in a voltage-invariant way , unlike gKL with its equilibrium potential . Our resulting thalamic states across all gH-background excitation planes and simulations therefore represent a thalamus hyperpolarized from any awake/relay state . If our thalamus was not this hyperpolarized , it would have responded more strongly to cortical spiking input in our simulations under CF in Fig 7A and 7E . Baseline ( no propofol ) simulations used all default parameters as listed in the S1 Appendix . Low-dose propofol was modeled by doubling gGABAA and τGABAA for all GABAA synapses in the model , while high-dose propofol was modeled by tripling gGABAA and τGABAA , as in [8 , 28] . This parametrization was derived in two steps , as mentioned in the subsection Methods > Computational Model > Propofol in [8] . First , we independently calculated the EC50 propofol concentration in human blood to be ~0 . 38 uM using the known propofol blood plasma concentration [1] , the molecular weight of propofol ( 178 . 271 g/mol ) , and the percentage of free propofol in plasma [58] . This value was roughly the same as what others calculated , 0 . 4 uM [59] . We also calculated the high-dose concentration to be ~1 . 0 uM using [60] . Having derived the concentrations , we then used experimental data from propofol IPSC effects [60] to predict the percent increase of gGABAA and τGABAA at these doses . According to these calculations [61] , low-dose propofol at a concentration of 0 . 4 uM should cause an increase in gGABAA and τGABAA to 200% of baseline , and high-dose propofol at 1 . 0 uM should cause an increase to 300% of baseline . We are aware that there are other in vitro experiments that come to different conclusions about the magnitude of these propofol effects [62] . However , we believe the experiments used in our calculations [61] best represent the state of our hyperpolarized , anesthetized thalamic circuitry [61] . Only by investigating the thalamus in isolation from cortex could we show it produces oscillations independent of cortical input . In our prior work modeling the entire thalamocortical loop [28 , 29] , the mechanistic source of the propofol-induced alpha oscillation is not examined in detail , and part of the purpose of the present study was to investigate whether thalamus could be a generator of the thalamocortical alpha observed in these previous models . We are confident that this thalamic alpha generation will not be completely eliminated by the introduction of responsive cortical cells , since cortical inputs were shown to reliably output alpha oscillations under anesthetic conditions previously [28] . In fact , the main point of [28] was that thalamocortical interaction enhanced propofol alpha under feedback , instead of eliminating it . Finally , while 50 cells of each thalamic cell type may seem too small to be effective , even a thalamic population of this size can directly affect the activity of many cortical cells . By one measure [63] , 50 thalamic cells may be enough to connect to roughly 8000 cortical cells at a 1–160 thalamocortical-cortical cell ratio . Furthermore , the ratio between higher-order thalamic cells and their downstream cortical cell targets could be substantially higher , but is unclear [64] . Furthermore , since we do not model cortical cells directly , we do not claim to model the EEG signal directly . In our prior work modeling the entire thalamocortical loop [28 , 29] , we do simulate a cortically-generated EEG signal , finding that our prior model produces the expected propofol phenomena even when the thalamic network is substantially smaller than in the current work . We believe these reasons are sufficient evidence that we do not need to simulate very many thalamic cells in order to accurately model both the general thalamic output to the cortex and the resulting EEG signal from the cortex . We did not model cortex directly; instead , we chose to model its inputs to the thalamus across the SWO cycle artificially . Corticothalamic UP states were modeled as a combination of AMPAergic cortical firing generated from a 12 Hz Poisson process [28] , shown in Fig 9A , and stepwise increases in background excitation , shown in Fig 9B . Corticothalamic DOWN states were modeled as the absence of these inputs . These individual components of corticothalamic impact during SWO phases are well supported [14 , 20] and allowed us to study their influences on the system independently of each other . We chose to model only UP and DOWN thalamic steady states instead of transitions due to the high number of degrees of freedom in the waveform of a dynamic transition . In our investigations , we decided it was too complex to compare all the different possible UP and DOWN states in the thalamus robustly at the same time we were varying transition attributes . We decided to characterize the steady states of the system before moving on to dynamic state changes in the future . Furthermore , as we move forward to model the slow waves in cortical cells directly , this will allow us to examine their thalamocortical interaction more accurately than by modeling artificial signals , since artificial signals would not allow for responsive cortical cell changes to the oscillation . Additionally , cortically-generated SWO will offer greater modeling constraints than the freedom of artificial signals , requiring less simulation . In Figs 3 , 4 , 5 and 7 , we hand-classified the behavior regime of each simulation ( represented by an individual pixel ) across the gH-background excitation plane . By examining the voltage traces of the TC cells , we classified simulations into the following list of behavior regimes: nonphysiological , silent/hyperpolarized , transients/spindle oscillations , sub-alpha oscillations , alpha oscillations , or silent/depolarized . Nonphysiological ( black ) represented simulations where the average membrane potential was either hyperpolarized to be less than -100 mV , depolarized to be greater than -50 mV , or depolarized and firing pathologically rapidly; “pathologically rapidly” was defined as persistent firing of the same frequency purely in response to extreme depolarization of the system , and it only occurred under extreme depolarization at the limits of the system . Silent/hyperpolarized ( grey ) , shown in Fig 3C , represented states where the TC cells did not intrinsically fire but their average membrane potential was above -100 mV and below the “T -current window” from roughly -72 mV to -80 mV . Transients/spindle oscillations , shown in Fig 3D , were defined as thalamic states where the TC cells either expressed waxing-and-waning spindles oscillations ( the spindles themselves were around 10 Hz ) , or transients that lasted long enough that the system could possibly wax into a spindle past the 8 seconds of simulation performed . Sub-alpha oscillations , shown in Fig 3E , were defined as states where the TC cells expressed persistent oscillations that lasted either most or all of the 8 second long simulations , and the oscillations were less than the alpha range of 8 to 13 Hz; different simulations in this category expressed firing in slow ( 0 . 1–1 . 5 Hz ) , delta ( 1 . 5–4 Hz ) , or theta ( 4–8 Hz ) frequency ranges . Alpha oscillations were defined as those that had TC cells bursting at 8–13 Hz for the entire duration of the 8 second long simulations; they were differentiated from transient/spindling oscillations by the lack of wax-and-waning as described in the Results . Silent/depolarized states , shown in Fig 3F , were defined as those that had TC cells not firing and had an average membrane potential depolarized above the T-current window but below -50 mV . Simulations were run using the open source DynaSim MATLAB toolbox [65] originally created by Jason Sherfey , on the Massachusetts Green High Performance Computing Center . Mechanism files used to populate the model in DynaSim are freely available on GitHub [66] , and code to reproduce all simulations and preliminary figures is also available on GitHub [67] ( except for MATLAB itself ) . | Anesthetics make patients lose consciousness , but how they affect brain dynamics is still unknown . Changes in EEG brainwaves are some of the few noninvasive signals we can use to learn about this . By analyzing such data , we can develop more targeted anesthetics , expand our knowledge of sleep circuits , and better understand how diseases impact these systems . The anesthetic propofol is known , among other effects , to increase synaptic inhibition , but it is unclear how these changes induce EEG alpha ( 8–12 Hz ) oscillations and their interaction with slow wave ( 0 . 1–1 . 5 Hz ) oscillations; these signals have been correlated with the state of propofol-infused consciousness . We simulated a network of thalamic cells to understand the mechanisms generating these signals . Propofol-potentiated inhibition produced a novel , sustained alpha rhythm in our network . Changes to the tonic level of depolarization enabled the alpha oscillations to occur at different phases in the slow wave oscillation , as seen clinically with increasing propofol dose . The thalamus may be critical to propofol-induced alpha oscillations and their coupling to slow wave oscillations . By understanding the mechanisms generating alpha , we may be able to design experiments to dissociate alpha from slow waves and determine their independent effects on levels of consciousness . | [
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] | 2017 | Thalamocortical control of propofol phase-amplitude coupling |
Bartonella bacilliformis is the etiological agent of Carrion’s disease , a neglected tropical poverty-linked illness . This infection is endemic of Andean regions and it is estimated that approximately 1 . 7 million of South Americans are at risk . This bacterium is a fastidious slow growing microorganism , which is difficult and cumbersome to isolate from clinical sources , thereby hindering the availability of phylogenetic relationship of clinical samples . The aim of this study was to perform Multi Locus Sequence Typing of B . bacilliformis directly in blood from patients diagnosed with Oroya fever during an outbreak in Northern Peru . DNA extracted among blood samples from patients diagnosed with Oroya’s fever were analyzed with MLST , with the amplification of 7 genetic loci ( ftsZ , flaA , ribC , rnpB , rpoB , bvrR and groEL ) and a phylogenetic analysis of the different Sequence Types ( ST ) was performed . A total of 4 different ST were identified . The most frequently found was ST1 present in 66% of samples . Additionally , two samples presented a new allelic profile , belonging to new STs ( ST 9 and ST 10 ) , which were closely related to ST1 . The present data demonstrate that B . bacilliformis MLST studies may be possible directly from blood samples , being a promising approach for epidemiological studies . During the outbreak the STs of B . bacilliformis were found to be heterogeneous , albeit closely related , probably reflecting the evolution from a common ancestor colonizing the area . Additional studies including new samples and areas are needed , in order to obtain better knowledge of phylogenetic scenario B . bacilliformis .
Carrion’s disease is neglected tropical neglected poverty-linked illness caused by Bartonella bacilliformis . This infection is endemic in low-income areas of Peru , specifically related to Andean regions from Peru , Ecuador and Colombia , covering roughly 145 , 000Km2 only in Peru , and it is estimated that approximately 1 . 7 million of South Americans are at risk [1–3] . This illness has two phases: the first , named Oroya’s Fever , mainly affects young children ( >60% of cases ) and is characterized by fever , acute bacteremia at about 60 days and severe hemolytic anemia [2 , 4] . Complications are common in this phase , and secondary infections are also frequent due to transient immunosuppression [5] . In the absence of adequate treatment , high levels of mortality ( 44% to 88% ) have been reported [2 , 4] . The second phase is called “Verruga Peruana” ( Peruvian Wart ) , in which the bacterium induces the proliferation of endothelial cells , resulting in a series of cutaneous lesions [6] . A variety of verrugal lesions are presented in the chronic phase: miliary , nodular and mular [1] . Asymptomatic carriers have also been described in the population from endemic areas ( 0 . 5–45% ) [7] . B . bacilliformis is a fastidious slow growing microorganism , which is difficult and cumbersome to culture and isolate from clinical sources [2] . Thus , the data available about the phylogenetic relationship of clinical samples of B . bacilliformis are scarce and non-uniform . Indeed , to the best of our knowledge no studies on clonal relations based on Pulsed Field Gel Electrophoresis ( PFGE ) have been performed , and molecular approaches have been based on PCR methodologies , including Repetitive Extragenic Palindromic PCR ( REP-PCR ) , Enterobacterial Repetitive Intergenic Consensus ( ERIC-PCR ) , Amplified Fragment Length Polymorphism ( AFLP ) , Infrequent Restriction Endonuclease Site PCR ( IRS-PCR ) , analysis of the 16S-23S ribosomal DNA intergenic spacer regions or analysis of the sequence of specific genetic loci such as gltA , ialB and flaA [8–10] . This latter methodology resembles a Multi-locus sequence typing ( MLST ) technology . MLST approaches are based on housekeeping gene sequencing , being robust , standardized methodology useful to develop epidemiological and evolutionary studies [11] . In fact , MLST schedules have been developed to analyze the phylogenetic relationships of Bartonella henselae [12] , and adapted to other Bartonella species , including Bartonella quintana [13] and Bartonella bovis [14] . Furthermore , the use of MLST has been useful in the identification of Bartonella ancashi , new specie of Bartonella genus , closely related to B . bacilliformis [15] . Regarding B . bacilliformis , a MLST schedule has recently been developed based on the sequence of 7 housekeeping genes ( bvrR , ribC , ftsZ , groEL , flaA , rnP and rpoB ) [16] , with 8 different ST being detected in 43 isolates . However , it should be considered that due to the relative isolation of the Andean valleys , the population structure of B . bacilliformis might differ between different endemic areas . The aim of the study was to perform direct blood MLST of B . bacilliformis from patients diagnosed with Oroya Fever during an outbreak in Northern Peru .
Seven blood samples from Cachachi ( Department of Cajamarca in Northern Peru ) were collected during March and April 2009 from patients clinically diagnosed with Oroya Fever . Additionally , another two blood samples were collected from Oroya’s Fever patients living in the Condebamba ( Cajamarca Department , 50 Km from Cachachi ) and Ancash Department in November and October 2011 , respectively . Finally , two collection strains isolated in 1941 ( CIP 57 . 19; NCTC12135 ) and 1949 ( CIP 57 . 18; NCTC12134 ) from the Pasteur Institute Collection and previously described as belonging to Sequence Type 3 [16] were used as controls ( Fig 1 ) . The clinical data and disease presentation of some patients were obtained . All adult participants provided written informed consent . The study were submitted , revised and approved by the Ethics and Research Committees of the Universidad Peruana de Ciencias Aplicadas in Peru and Hospital Clinic of Barcelona in Spain . The presence of B . bacilliformis in all the blood samples was confirmed by PCR amplification of 438 bp of the 16S rRNA gene of B . bacillifomis ( 5’CCTTCA GTTMGGCTGGATC-3’ and 5’-GCCYCCTTGCGGTTAGCACA-3’ ) as previously described [17] . In all cases the identity of the amplified fragments was confirmed after being visualized in 1 . 5% agarose gel stained with Sybr Safe and gel recovered using Wizard SV gel and PCR clean up system , ( Promega , Madison , WI , USA ) following manufacturer's instructions and were sequenced by Macrogen ( Seoul , Korea ) . The DNA was extracted from 200 μl of blood sample and directly from the control bacterial strains using a commercial extraction kit ( High Pure Kit Preparation template , Roche Applied Science , Mannheim , Germany ) . Blood and bacterial DNA obtained after extraction were eluted in 100 μl of nuclease free water and then processed or stored at -20°C until use . Internal fragments of the 7 genetic loci ( ftsZ , flaA , ribC , rnpB , rpoB , bvrR and groEL ) included in the B . bacilliformis MLST schedule were amplified as previously described [16] . Reaction mixtures were exposed to denaturation at 96°C for 5 min followed by 50 cycles of 96°C for 40 sec , 55°C for 40 sec and 72°C for 50 sec , with a final extension step of 72°C for 10 minutes . Amplified fragments were visualized in 1 . 5% agarose gel stained with Sybr Safe and subsequently gel recovered using Wizard SV gel and PCR clean up system , ( Promega , Madison , WI , USA ) following manufacturer’s instructions and sequenced by Macrogen ( Seoul , Korea ) . Phylogenetic relationship analyses were conducted using MEGA version 5 [18] . The phylogenetic tree was constructed by UPGMA ( Unweighted Pair Group Method with Arithmetic Mean Analysis ) . The phylogenetic tree was inferred from 500 bootstrap replicates . The sequences of all the alleles described previously were obtained from Genbank ( accession numbers JF326267 to JF326294 ) and were ordered according to the corresponding Sequence Type ( ST ) in order to develop the phylogenetic tree .
The mean age of patients studied was 25 . 9 years ( SD = 13 . 77 , IC95% = 19 . 5–32 . 3 ) , 44 . 4% being female . Among the 5 patients from whom clinical data were recovered , all ( 100% ) presented fever ( >38°C ) and malaise , 4 ( 80% ) reported chills , myalgia and pallor , 3 ( 60% ) headache , 2 ( 40% ) reported jaundice and arthralgia and only one patient ( 20% ) presented vomiting ( Table 1 ) . In 3 cases the treatment was recorded , in all cases being ciprofloxacin alone ( 2 cases ) or with ceftriaxone ( 1 case ) during 14 days . Among the 9 blood samples analyzed , a total of 4 different B . bacilliformis STs were identified . The most frequently found was ST1 , present in 6 out of 9 ( 66% ) samples , all from the Cajamarca Department ( 5 out of 7 belonging to the Cachachi outbreak , and that of Condebamba ) , while the sample from the Ancash Department belonged to ST4 ( Fig 1 ) . Additionally , two samples from the Cachachi outbreak presents a new allelic profile , belonging to new STs , which were classified as ST9 ( 1 , 2 , 1 , 1 , 1 , 1 , 1 ) and ST10 ( 1 , 1 , 1 , 1 , 1 , 3 , 1 ) respectively . The 2 collection strains were classified as ST3 ( Table 2 ) . On determination of phylogenetic relationships between the ST9 and ST10 and the previously described ST , they were found to be closely related to ST1 , differing in only 1 of the 7 alleles ( Fig 2 ) .
Studies on the clonality and phylogeny of B . bacilliformis are scarce . This may be due to the slow growth of this bacterium and a series of specific requirements which directly affect the culture . The present study demonstrates MLST studies of B . bacilliformis may be performed directly from blood samples thereby avoiding the difficult step of culturing this microorganism . However , a series of limitations that may limit the usefulness of this technique should be taken into account . Among these , definitive results may not be obtained in the hypothetical case of polyclonal infections when the infecting isolates belong to different STs . Along this line , although to the best of our knowledge coinfection by two different B . bacilliformis clones has not been described to date , coinfections by different Bartonella variants has been reported in cotton rats [19] . In the present report , new MLST were not related to artefactual overlapping of sequences belonging to different B . bacilliformis causing a concomitant infection , because no double peaks were observed in any DNA sequence . Another limitation is that direct blood PCR approaches in the study of asymptomatic B . bacilliformis carriers do not have enough power due to the low bacterial burden [20] . The present study demonstrates the heterogeneity of the B . bacilliformis population . Highly clonality has also been found in other species of Bartonella , such as B . quintana , a re-emerging pathogen causing trench fever [13] . Thus , 3 different ST ( ST1 , ST9 and ST10 ) were recovered amongst the samples analyzed from the Cachachi outbreak . However , it is of note that these 3 STs were closely related to each another , and thus may reflect the evolution from a common ancestor colonizing the area . To date only one study has determined the MLST of B . bacilliformis isolates [16] . In this study ST1 was found to be widely distributed in central and northern areas of Peru , accounting for 46% of the samples analyzed , including samples from the 1960's . In addition , ST1 has been detected in the neighboring San Martin Department , and thus , its presence in the Cajamarca Department is not surprising . ST2 , ST3 , ST4 and ST8 have been described in the center of the country , similar the present sample belonging to ST4 . Meanwhile , ST5 has been observed in southern isolates and ST6 and ST7 in the north of the country . Some techniques such as PFGE or REP-PCR are useful for the description of clones and specific outbreak characterization , as for example virulence , being of special interest to study recent genetic events . On the other hand , techniques such as MLST classification describe ancient genetic differentiations that may underlie more in depth differences [11–13] . For example , specific STs could possess increased virulence or may have a greater facility to develop either acute or chronic infection , or to remain undetected in asymptomatic carriers . Unfortunately , the scarce data on STs of B . bacilliformis make it difficult to delineate these aspects . The clinical data of only a few patients were recorded; however the symptoms reported are in accordance with those more extensively described , such as the presence of fever , pallor , malaise and headache in acute cases , [17] . Some of these symptoms are a consequence of hematological complications , such as pallor , while others like vomiting or jaundice are related to gastrointestinal problems [21] . Although this disease mainly affects children under 14 years of age ( more than 60% of cases ) [22] , in our study the youngest patient of the Cachachi outbreak was 15 years old . This may be related to the outbreak nature of the samples . Fortunately , all patients who receipt treatment respond well to the treatment . The currently recommended treatment for the acute phase of Oroya’s Fever includes the use of ciprofloxacin as first line therapy in adults and children >14 years , while chloramphenicol , cotrimoxazole , amoxicillin plus clavulanic acid and ceftriaxone are used as a second line or children and pregnant women [23] . Fortunately , up to now , the levels of antibiotic resistance reported among B . bacilliformis have shown that this microorganism is highly susceptible to the antibiotics tested [24] . In summary , this is the first report of MLST of B . bacilliformis performed in direct blood samples , with two new ST variants being described . Present data highlight the need to extend the studies to new samples and geographical areas , in order to provide a better picture of the situation , which will allow specific STs of B . bacilliformis to be associated with clinical symptoms , and the severity or phase of the disease . | The bacteria Bartonella bacilliformis is the etiological agent of Carrion’s disease , which is a neglected poverty-related disease , related to Mountain Andean valleys of Peru , Colombia and Ecuador . This disease , in absence of treatment presents a high mortality during the acute phase , called Oroya’s Fever . The second phase is characterized by the development of dermal eruptions , known as “Verruga peruana” ( Peruvian wart ) . This bacterium is a fastidious slow growing microorganism , being difficult and cumbersome to isolate from clinical sources . Then , the available data about phylogenetic relationship in clinical samples are really scarce , but suggesting high variability . The aim of the study was to perform direct blood analysis of B . bacilliformis Multi Locus Sequence Typing ( MLST ) , a genotyping tool , in patients with Oroya fever during an outbreak . The present study demonstrates that the direct blood PCR , followed by nucleotide sequencing and MLST is a technique useful in the phylogenic characterization of this fastidious microorganism endemic from Andean regions . In this study , we demonstrate that the outbreak of Oroya’s fever was caused by closely related Sequence Typing ( ST ) microorganisms and , additionally , new STs have been described . | [
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] | 2016 | Multi-Locus Sequence Typing of Bartonella bacilliformis DNA Performed Directly from Blood of Patients with Oroya’s Fever During a Peruvian Outbreak |
Intrauterine growth restriction ( IUGR ) due to placental insufficiency is associated with blood flow redistribution in order to maintain delivery of oxygenated blood to the brain . Given that , in the fetus the aortic isthmus ( AoI ) is a key arterial connection between the cerebral and placental circulations , quantifying AoI blood flow has been proposed to assess this brain sparing effect in clinical practice . While numerous clinical studies have studied this parameter , fundamental understanding of its determinant factors and its quantitative relation with other aspects of haemodynamic remodeling has been limited . Computational models of the cardiovascular circulation have been proposed for exactly this purpose since they allow both for studying the contributions from isolated parameters as well as estimating properties that cannot be directly assessed from clinical measurements . Therefore , a computational model of the fetal circulation was developed , including the key elements related to fetal blood redistribution and using measured cardiac outflow profiles to allow personalization . The model was first calibrated using patient-specific Doppler data from a healthy fetus . Next , in order to understand the contributions of the main parameters determining blood redistribution , AoI and middle cerebral artery ( MCA ) flow changes were studied by variation of cerebral and peripheral-placental resistances . Finally , to study how this affects an individual fetus , the model was fitted to three IUGR cases with different degrees of severity . In conclusion , the proposed computational model provides a good approximation to assess blood flow changes in the fetal circulation . The results support that while MCA flow is mainly determined by a fall in brain resistance , the AoI is influenced by a balance between increased peripheral-placental and decreased cerebral resistances . Personalizing the model allows for quantifying the balance between cerebral and peripheral-placental remodeling , thus providing potentially novel information to aid clinical follow up .
Intrauterine growth restriction ( IUGR ) , predominately due to placental insufficiency , is one of the main causes of perinatal mortality and morbidity [1] , [2] , and defined as a birth weight below the 10th percentile for gestational age . IUGR fetuses , suffering from hypoxia and undernutrition , show Doppler changes in several arteries of the feto-placental circulation such as umbilical artery ( UA ) , middle cerebral artery ( MCA ) , and also in the aortic isthmus ( AoI ) . These changes are assessed in clinical practice to stage the severity of IUGR and are thought to reflect blood flow redistribution due to increased peripheral resistance , with decreased brain resistance in order to maximize brain blood supply under an adverse environment . In IUGR fetuses , AoI diastolic forward flow usually decreases and can become reversed in the more severe cases reflecting blood redistribution from the ductus arteriosus towards the brain instead of the periphery . Reverse flow in the AoI is associated with worse perinatal and neurodevelopmental outcome [3]–[7] . However , which remodeling or redistribution processes in the cardiovascular system induce the observed changes in AoI flow in IUGR fetuses is not fully understood . It has been proposed that cerebral vasodilation plays a major role in decreasing diastolic flow in the AoI [8] . However , other clinical studies suggested that AoI flow pattern is influenced by simultaneous changes in both cerebral and peripheral-placental resistances [6] , [9]–[11] . Moreover , some experimental studies in an ovine animal model [12]–[14] have quantified the influence of placental resistance increase in the AoI flow , showing a strong correlation between the increase in the placental resistance and the amount of diastolic reversal flow in the AoI . However , the cerebral vasodilation occurring as response to fetal hypoxemia also influences the flow patterns in the AoI , and this influence could not be isolated from those changes caused by the increase in placental resistance . Therefore , it would be of interest to be able to estimate the separate influence of the individual contributors , such as the cerebral vasodilation and placental resistance increase , on the AoI flow . This can further improve the understanding of the flow changes in IUGR and provide more targeted assessment of flow redistribution . For this , lumped computational models have been proposed to recreate and better understand hemodynamic changes in the fetal circulation [15]–[22] . These models are based on the idea that the flow in a tube is analogous to the current in an electrical circuit and flow properties such as viscosity , inertia and compliance can be modeled with resistors , inductors and capacitors respectively . Hence , the different parts of the fetal circulation , such as arteries or vascular beds , can be modeled with a set of electrical components . The parameters of the electrical components are calculated based on the cardiovascular system's physical properties and dimensions together with physiological and imaging measurements , where possible . Thus , an equivalent electric circuit of the fetal circulation can be obtained . Computational models have the advantage that they enable to evaluate the effects of changes in individual parameters on the total system performance . For example , the influence of changes in placental and/or brain resistance on the AoI flow can be evaluated separately , which cannot be performed in a clinical or experimental setting . Previously published models of the fetal circulation focused either on the materno-fetal circulation , studying oxygen exchange [15] , [17] , [18]; only focused on the fetal circulation under normal conditions [16] , [20]; lack the full complexity of the fetal circulation ( such as the ductus arteriosus ) to study the flow redistribution in the places connecting the specific segments present [18] , [21]; or have used a simplified and non-measured and personalized flow waveform at the entrance of the aorta and pulmonary artery [19] , [21] , [22] . Therefore , we developed an extended lumped model of the fetal circulation , including all relevant components to study flow redistribution in IUGR that additionally can be tuned towards an individual fetus when blood flow measurements are available . This model was further used to help in better understanding the hemodynamic changes induced by altered conditions in the brain circulation , including the AoI and the MCA . The model was calibrated and validated using clinical measurements from a healthy control fetus . Next a parametric study was performed to specifically evaluate contributors to flow changes in the AoI and cerebral arteries ( CA ) . Finally , the model was personalized with clinical data from three IUGR cases with different degrees of severity in order to show the individual contributors to flow redistribution .
In order to calibrate and validate the accuracy of the fetal circulation model , and to show its potential for personalization , clinical and Doppler data from one control and three IUGR fetuses with different degrees of severity were included . Eligible cases were singleton pregnancies that were selected from women who attended the Maternal-Fetal Medicine Department at Hospital Clínic de Barcelona . The study protocol was approved by the local Ethics Committee and patients provided written informed consent . IUGR was defined as an estimated fetal weight [23] and confirmed birth weight below the 10th percentile according to local reference curves [24] together with a pulsatility index ( PI ) in the UA above 2 standard deviations [25] . IUGR fetuses were classified in stages of severity based upon the end-diastolic flow ( EDF ) in the UA as: present ( PEDF ) , absent ( AEDF ) or reversed ( REDF ) [26] . We selected one IUGR representative of each severity stage . All IUGR cases and the control fetus underwent an ultrasonographic examination between 31–34 weeks of gestation using a Siemens Sonoline Antares machine ( Siemens Medical Systems , Malvern , PA , USA ) which included estimation of fetal weight , standard obstetric Doppler evaluation and fetal echocardiography . Fetal echocardiography included the evaluation of flow velocities in the UA , MCA , AoI , ductus arteriosus and ascending aorta ( only in the control fetus ) . The UA was evaluated in a free loop of the umbilical cord . The MCA was measured in a transverse view of the fetal skull at the level of its origin from the circle of Willis [27] . The cerebroplacental ratio was calculated by dividing MCA and UA PI [25] . PI was calculated as: systolic velocity minus diastolic velocity divided by time-averaged maximum velocity . Ductus arteriosus and AoI flow velocities were obtained either in a sagittal view of the fetal thorax with a clear visualization of the aortic arch or in a cross section of the fetal thorax at the level of the 3-vessel and trachea view [28] . The AoI flow velocity was quantified by measuring the AoI PI and flow index ( IFI ) . The IFI was calculated as: ( systolic + diastolic ) /systolic velocity integrals . Aortic inflow velocity was imaged in an apical or basal 5-chamber view of the heart , and pulmonary artery inflow velocity was obtained in a right ventricular outflow tract view . Peak systolic velocities of both aortic and pulmonary artery inflows , ejection time and heart rate were measured . The diameters of the aortic and pulmonary valves were measured in frozen real-time images during systole by the leading-edge-to-edge method [29] . Additionally , Doppler recording from the ascending aorta was obtained in the control fetus . The angle of insonation was kept as close as possible to 0° and always below 30° . Doppler data are shown as crude values and z-scores by gestational age according to previously published normal values [25] , [27] , [28] , [30] . Upon delivery , gestational age , birth weight , birth weight centile , mode of delivery , Apgar scores , presence of preeclampsia and length of stay at the neonatal intensive care unit were recorded .
Ultrasonographic and perinatal data are shown in Table 2 . As expected , IUGR cases showed a higher UA PI and AoI PI , together with lower MCA PI , cerebroplacental ratio and IFI values as compared to the control fetus . Also , as expected , IUGR cases delivered earlier and had lower birthweight and birthweight centile as compared with the control fetus . There were no perinatal deaths with the exception of the most severe IUGR case ( REDF ) who died in utero . Fig . 2 shows the real measured flow velocity waveforms in UA , MCA and AoI for the control and the 3 IUGR fetuses . Reversed diastolic flow in the AoI can be appreciated in IUGR cases with AEDF and REDF . Model-based and measured flow waveforms from the aortic and pulmonary inflows , ascending aorta , AoI , MCA and ductus arteriosus of the control fetus after calibration are displayed in Fig . 3 highlighting their similarity . The model-based pressure waveform is displayed in the same figure ( Fig . 3G ) . Furthermore , we confirmed that the amount of blood flow distributed towards each vascular bed was within the range of normal values , as shown in Table 3 . The parameters values' of the vascular bed resistances and compliances are listed in Table 4 . Fig . 4 displays model-based AoI and CA traces for different combinations of peripheral and brain resistance values , modeling different severity degrees of IUGR . It shows that as peripheral resistance increases and/or brain resistance decreases , late-systolic and diastolic flow in the AoI is reversed while blood flow in the CA increases . The amount of reversal flow in the AoI ( Fig . 5A–B ) , PI in the AoI ( Fig . 5C–D ) and IFI ( Fig . 5E–F ) were plotted as a function of brain and peripheral resistances relative to their normal value . Both the individual increase in peripheral resistance and decrease in brain resistance seem to have similar effects on the AoI flow . PI in the CA ( PICA ) and the ratio between the PI in the dAo and CA ( PIdAo/PICA ) were plotted in Fig . 6 as a function of brain and peripheral resistances relative to their normal value . In the case of the CA , the decrease in brain resistance seems to have a bigger influence on decreasing PICA than the increase in peripheral resistance ( Fig . 6B ) . Regarding PIdAo/PICA , up to a two-fold increase/decrease in peripheral/brain resistance , its relation with the corresponding resistance variation is similar ( Fig . 6D ) . The increase in percentage of CCO towards the brain depends mainly on the reduction of brain resistance rather than peripheral changes , as shown in the graphs plotted in Fig . 6E . However the reduction of the amount of blood flow towards the lower body and the placenta was mainly produced by the increase in the peripheral resistance . The comparison between the measured and the patient specific fitting of the AoI and CA blood waveforms are displayed in Fig . 7A–F showing similar measured and model-based flow waveforms . Table 4 shows the values of vascular bed resistances and compliances and also the estimated peripheral and brain resistances after fitting for all individuals in the study . Table 5 shows the model-based parameters' values obtained for the four fetuses , which are similar to the clinical measurements shown in Table 2 . The relative increase and decrease in peripheral and brain resistance respectively , estimated for each modeled case is plotted in Fig . 7G . In the IUGR fetus with UA-PEDF , the peripheral resistance was increased by 20% ( ×1 . 2 ) while brain resistance remained almost equal ( ÷1 . 08 ) . In the most severe IUGR case , the peripheral resistance increased 274% ( ×3 . 7 ) with −41% ( ÷1 . 7 ) decrease of brain resistance . Therefore , in all three IUGR fetuses , the estimated variation in peripheral resistance is much higher than the variation in the brain resistance . The estimated amount of blood flow towards the brain and the lower body and placenta for the four fetuses is plotted in Fig . 7H showing that as the severity condition increases , the percentage of blood flow to the brain increases and the flow to the lower body and placenta is reduced .
We developed a realistic computational model of the fetal circulation to study blood redistribution in IUGR enabling both parametric studies to determine individual contributors to remodeling as well as personalization to quantify changes in an individual fetus . Using this approach , we show that AoI flow changes depend both on brain vasodilation and peripheral resistance increase , while MCA flow is mainly affected by changes in cerebral resistance . Furthermore we showed that individual IUGR fetuses show marked differences in their vascular components with an exaggerated change in peripheral-placental resistance as major determinant for observed changes in measured Doppler flows . For this , we implemented and calibrated a lumped model of the fetal circulation taking into account the main arteries of the fetal circulation , including the ductus arteriosus and the aortic isthmus . Previous approaches to model the fetal circulation focused mainly on the ( oxygen ) exchange within the placenta [15] , [17] , [18] or have only modeled fetal circulation under normal conditions [16] , [20] . Some previous studies that investigated the influence of an increase in placental resistance on flow related indexes did not include the ductus arteriosus , thus limiting its comparison with in-vivo imaging and the translation to clinical practice [19] , [21] . Therefore , to our knowledge , ours is the first attempt to evaluate the effect of peripheral and cerebral resistances on AoI flow by using a computational model of the fetal circulation . Another important difference from previously published models is that we use data from individual clinical imaging as boundary conditions for each modeled subject , instead of considering a single flow input with fixed values calculated from the literature . The reliability of our model was ensured by comparing the model-based flow waveforms with Doppler velocity profiles recorded in the control fetus . Moreover , the fraction of cardiac output distributed to the different peripheral regions of the fetus obtained with our model agrees with the cardiac output distribution measured in human fetuses [10] , [46] , [47] , and also estimated in other models [16] , [20] . We have comprehensively evaluated how the flow in AoI and CA are affected by changes in the vascular resistances . The increase in placenta vasculature resistance together with the vasoconstriction of the lower body arteries were modeled by increasing the peripheral resistance . The cerebral vasodilation that occurs as a compensatory mechanism to fetal hypoxia and undernutrition was modeled by decreasing brain resistance . We observed that , as increasing the modeled IUGR severity , diastolic flow in AoI decreases and , in very severe cases becomes markedly retrograde . This pattern of changes in AoI flow is consistent with the changes clinically described in IUGR cases [6] , [9]–[11] , [30] . We also found that the AoI flow changes observed in IUGR are influenced independently by both cerebral vasodilation and peripheral resistance increase . Previous reports suggested that the decrease in cerebral resistance plays a major role in determining the net AoI diastolic flow since MCA vasodilation precedes AoI flow abnormalization [8] . Other studies have described a correlation between the IFI and postnatal neurodevelopmental outcome [6] , [7] , supporting the impact of cerebral vasodilation in AoI flow changes . However , other studies [6] , [9] indicated that since the AoI is connecting the two fetal circulations in parallel , AoI flow pattern reflects the existence of differences in vascular resistances , suggesting that both the increase in peripheral resistance and the cerebral vasodilation are responsible for AoI flow changes . Our results support this last hypothesis since we showed that not only cerebral vasodilation but also the increase in peripheral resistance altered the AoI flow pattern . These results are consistent with the consideration of AoI as a good predictor of not only the poor neurodevelopmental outcome [5] , [6] but also of the high risk of adverse perinatal outcome and mortality [3] , [7] , [50] . Regarding the CA , we showed that the decrease in PICA is mostly related to cerebral vasodilation and much less influenced by changes in the periphery/placenta . This is also reflected in the amount of blood flow that is distributed towards the brain and towards lower body and placenta . For example , when brain resistance was decreased by 75% , the flow that went to the lower body and placenta was decreased by 57% but cerebral flow increased 150% , showing how cerebral vasodilation is the most responsible for the blood flow increase in CA . These results are in line with those previously reported by van den Wijngaard et al . [21] and consistent with MCA being a risk stratifying factor for suboptimal neurodevelopment in IUGR rather than perinatal complications [4] , [50] . Finally , PIdAo/PICA showed a linear increase with the severity of IUGR , starting from a value about 1 . 0 for the control fetus . This result was consistent with the data published by Makikallio et al . [9] that showed also a value of 1 . 0 in control fetuses and an increase in the IUGR group . Next we constructed patient-specific models of three IUGR fetuses . We were able to reproduce the AoI and CA flow waveforms and also the model-based values for AoI PI , CA PI and IFI parameters were consistent with the measured ones , demonstrating that the developed lumped model of the fetal circulation not only is able to reproduce the hemodynamic changes that occur in fetus under normal conditions , but also with increased peripheral-placental resistance and vasodilation . This is helpful to estimate parameters that cannot be measured clinically such as the relative variation of the upper and lower body resistances who might be more directly related to the staging of the disease than only the measurements of PI , currently used in clinical practice . However , the presented lumped model has some limitations . Firstly , 0D lumped models only consider the temporal variation of pressure , flow and volume variables , assuming no variation of these parameters in the spatial dimension . However , since the aim of the study was to evaluate the hemodynamic interactions among the different cardiovascular parts , without considering flow phenomena or wave reflections , we think that 0D lumped models were accurate enough for our purpose . Also , the model is a simplified version of the fetal arterial tree because it only considers one artery and one peripheral bed for the lower body . However since the goal was to study the flow changes in the AoI and brain we considered that including or not all the lower arteries and organs would have the same effect on the AoI and cerebral flows . Secondly , the increase in right ventricular predominance observed in IUGR fetuses [10] , [51] , [52] was not taken into account for the parametric study as we decided to focus on the resistance variation and used the measurement of a normal fetus as input rather than changing the input for each combination of resistances . Thirdly , the fetal heart was not modeled . Nevertheless , since we used patient-specific Doppler waveforms at the ventricular outputs as the input of the model , we were indirectly considering the cardiac changes that may affect the cardiac output when studying the individual cases . Fourthly , our model does not take into account the major biochemical disorders created by placental circulatory insufficiency , such as low pH , hypoxia and respiratory acidosis , that can interfere with the normal cardio circulatory function and play a significant role in the hemodynamic changes during IUGR [53] . Fifthly , considering a venous pressure of 0 mmHg could have an effect , but mainly on the pressure values . However , we were interested only in flow waveforms and we were able to reproduce the measured Doppler traces in all cases . Finally , although we are not estimating all the parameters that may change between subjects and/or under pathological conditions , we still can consider our approach as a patient-specific modeling . We are using patient specific data to build the model and its boundary conditions ( gestational age , fetal weight , heart rate , Doppler velocities and valve radius ) to finally estimate the specific resistances variation for each individual . In conclusion , the proposed equivalent lumped model seems to be a good approximation to assess hemodynamic changes in the fetal circulation under abnormal growth conditions . Further developments of the model can be useful for assessing further vessels and their interactions under various clinical conditions , and the impact of interventions . Our results suggested that AoI flow is affected by peripheral-placental as well as cerebral resistances while CA flow mainly depends on cerebral resistance . Furthermore , when personalizing the model to IUGR fetuses we were able to estimate the specific vascular resistances variation , suggesting that the peripheral-placental resistance is the major determinant for observed changes in measured Doppler flows . This study supports the potential role of AoI as marker of adverse perinatal and neurological outcome since it is a central vessel connecting the two ventricular outputs and therefore its flow reflects the balance between ventricular output and upper/lower body vascular resistances . Personalizing the model shows promise to directly assess properties of the vascular bed rather than using indirect Doppler measurements . | Intrauterine growth restriction ( IUGR ) is one of the leading causes of perinatal mortality and can be defined as a low birth weight together with signs of chronic hypoxia or malnutrition . It is mostly due to placental insufficiency resulting in a chronic restriction of oxygen and nutrients to the fetus . IUGR leads to cardiac dysfunction in utero which can persist postnatally . Under these altered conditions , IUGR fetuses redistribute their blood in order to maintain delivery of oxygenated blood to the brain , known as brain sparing . Given that , in the fetus the aortic isthmus ( AoI ) is a key arterial connection between the cerebral and placental circulations , quantifying AoI blood flow has been proposed to assess this brain sparing effect in clinical practice . However , which remodeling or redistribution processes in the cardiovascular systems induce the observed changes in AoI flow in IUGR fetuses is not fully understood . We developed a computational model of the fetal circulation , including the key elements related to fetal blood redistribution . Using measured cardiac outflow profiles to allow personalization , we can recreate and better understand the blood flow changes in individual IUGR fetuses . | [
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] | 2014 | A Computational Model of the Fetal Circulation to Quantify Blood Redistribution in Intrauterine Growth Restriction |
Osteocytes , cells forming an elaborate network within the bones of most vertebrate taxa , are thought to be the master regulators of bone modeling , a process of coordinated , local bone-tissue deposition and removal that keeps bone strains at safe levels throughout life . Neoteleost fish , however , lack osteocytes and yet are known to be capable of bone modeling , although no osteocyte-independent modeling regulatory mechanism has so far been described . Here , we characterize a novel , to our knowledge , bone-modeling regulatory mechanism in a fish species ( medaka ) , showing that although lacking osteocytes ( i . e . , internal mechanosensors ) , when loaded , medaka bones model in mechanically directed ways , successfully reducing high tissue strains . We establish that as in mammals , modeling in medaka is regulated by the SOST gene , demonstrating a mechanistic link between skeletal loading , SOST down-regulation , and intense bone deposition . However , whereas mammalian SOST is expressed almost exclusively by osteocytes , in both medaka and zebrafish ( a species with osteocytic bones ) , SOST is expressed by a variety of nonosteocytic cells , none of which reside within the bone bulk . These findings argue that in fishes ( and perhaps other vertebrates ) , nonosteocytic skeletal cells are both sensors and responders , shouldering duties believed exclusive to osteocytes . This previously unrecognized , SOST-dependent , osteocyte-independent mechanism challenges current paradigms of osteocyte exclusivity in bone-modeling regulation , suggesting the existence of multivariate feedback networks in bone modeling—perhaps also in mammalian bones—and thus arguing for the possibility of untapped potential for cell targets in bone therapeutics .
Fish bone is comprised of the same material building blocks as mammalian bone ( mineral , water , collagen , and other proteins ) [1] . As in mammalian bone , fish bone also possesses both bone-depositing ( osteoblast ) and bone-resorbing ( osteoclast ) cells , the building and wrecking crews of the bone-modeling response [2 , 3] . It is the bone-modeling process—the addition or removal of bone tissue to or from bone surfaces—that grants bone the ability to respond adaptively to changing loads [4–6] . However , the bones of most advanced fishes ( neoteleosts ) completely lack osteocytes , which are present in huge numbers and constitute over 90% of all cells in the bones of all other vertebrate taxa , including basal fishes [1 , 3 , 7–9] . Osteocytes are considered the architects of modeling , directing osteoblast and osteoclast action [4 , 10] . Although the mechanosensing and regulatory functions of osteocytes in bone , particularly with regard to modeling , have not been confirmed incontrovertibly in vivo [11–13] , the density and connectivity of the osteocyte network makes these putative roles very likely [14] . Mammalian paradigms argue therefore that neoteleost fish should be at a functional disadvantage because of the absence of osteocytes , unable to adapt their bones to changing loads . However , several studies demonstrated that anosteocytic fish bones do respond to their mechanical environment by modeling ( e . g . , [3 , 15–17] , particularly in response to swimming [18–20] ) , though the mechanisms and cellular effectors of this response remain unknown . Anosteocytic fish bone , a natural osteocyte knock-out , therefore offers a unique model for studying the regulatory mechanism of bone modeling and for investigating the widely accepted primacy of osteocytes in bone biology .
In order to critically examine the role osteocytes play in bone modeling , we compared the skeletal response to loading in medaka and zebrafish ( Fig 1 ) , two common laboratory fish species differing in their bone-tissue type—being anosteocytic and osteocytic , respectively—but otherwise similar in size , ecology , and swimming mode . Because fish are almost neutrally buoyant , their skeletons are loaded primarily by muscle forces . We regulated skeletal loading conditions using a custom-built swim-training system , requiring fish to swim against a semilaminar water current of tightly controlled velocity ( videos of swim training are shown in S1 Video ) . Strenuous swim training increases the load applied by the powerful axial paravertebral muscles and tendons , which attach to the vertebrae and are responsible for the oscillatory movements of swimming [18 , 19] , providing a tractable framework for examination of the modeling response due to exercise ( S1 Fig ) . To visualize new bone formation , we injected individuals intraperitoneally with calcium-binding fluorochromes ( alizarin red at time zero and calcein green after 6 weeks of strenuous swimming ) , then compared the extent and spatiotemporal dynamics of new bone formation in caudal vertebrae between experimental ( swim-trained ) and control ( non-swim-trained ) fishes and between the osteocytic and anosteocytic species . The current osteocyte-centric dogma of bone biology argues that modeling in the osteocyte-rich skeleton of zebrafish should be far more efficient and spatially targeted , with osteocytes sensing loads locally throughout the bulk of the tissue and directing osteoblasts to build new tissue in appropriate locations experiencing maximal strains [21 , 22] . In contrast , the anosteocytic bones of medaka should lack the ability to mechanosense and regulate modeling , and therefore , modeled bone should be randomly distributed or at least not correlated with tissue strains . However , our results indicate that the modeling behavior is strikingly similar in the 2 species . In both medaka and zebrafish , we observed minimal bone formation in the vertebrae of untrained ( nonexercised ) individuals ( Fig 2A ) . In contrast , swim-trained fish of both species exhibited new bone formation of similar extent and spatial distribution ( Fig 2B ) . Our nanoindentation results show that in both zebrafish and medaka , the stiffness ( Young’s modulus ) of the vertebral bone tissue is not significantly different between swim-trained and untrained fish ( S2 Fig ) . The locations of new growth in swim-trained fish correspond closely with regions where high loads are expected: regions of muscle attachment ( e . g . , along the neural arch and proximal spine ) and of articulation with adjacent vertebrae ( e . g . , the cranial and caudal edges of the vertebral body bordering the intervertebral joint space ) . In order to assess the correlation between regions where high loads are expected and locations of peak strains , we performed finite element analysis ( FEA ) on a 3D computer model that simulated a loaded medaka vertebra . The model incorporated detailed structural and material data collected by high-resolution tomography scans and mechanical testing and enabled calculation of the 3D strain distribution in a swim-trained medaka vertebra . The results of these simulations confirm that new tissue deposition , as demonstrated by fluorochromes , is prominent in regions predicted by FEA to experience peak strains ( the presumed stimulus for modeling [21 , 22]; see Fig 2B and 2C ) . Vertebral modeling in response to swim training , coupled with the close agreement between strain-distribution predictions and tissue-deposition patterns seen in our experimental data , argues that bone modeling in medaka vertebrae is not random , despite the lack of osteocytes , but rather closely correlated to the strain environment arising in the bone during swimming . More significantly , this mechanically relevant tissue response confirms that modeling is possible and directable even without the presence of numerous mechanosensors located within the bulk of the bone tissue . This would suggest that modeling in anosteocytic medaka either does not require internal strain information and relies only on sensors on the external surfaces of the bone or uses external sensors to read internal strains ( perhaps via the array of the densely packed , hypomineralized fibers that perforate fish bone [23] ) . Detailed results of FEAs furthermore indicate that peak strains occur primarily near the external surfaces of the vertebral bone ( Fig 3 ) , supporting the potential efficiency of surface sensors . In mammals , one of the major regulators of bone’s response to mechanical loads is the SOST gene , which is expressed almost exclusively by osteocytes and encodes the protein sclerostin , a potent suppressor of bone building by osteoblasts [24–26] . During skeletal loading in mammals , SOST expression by osteocytes is down-regulated , releasing osteoblast inhibition [13 , 25] and promoting local bone deposition . Therefore , modulation of SOST expression by osteocytes is considered an important bellwether for bone modeling in mammals . The absolute lack of osteocytes in medaka bones guarantees that their bone-modeling regulatory pathways ( cellular , molecular , or both ) differ from those of mammals . The cellular and molecular pathways of modeling regulation in fishes , however , are unknown , hampering a broader and phylogenetic perspective on the physiology of bone . Demonstration of SOST expression in medaka , e . g . , would indicate SOST-mediated modeling regulation is possible but by a nonosteocytic cell , whereas lack of SOST expression would suggest other molecular regulation pathways ( i . e . , via other genes or gene products ) . We found , using in situ hybridization ( ISH ) on medaka vertebrae sections , that SOST was indeed expressed in the vertebrae of untrained ( control ) medaka , with particularly high expression levels along the margins of the intervertebral regions ( IVRs ) and neural spines ( NSs ) , as well as within the core of the adjacent fin radials ( Rs; the elements that support the dorsal fin rays and consist of a cartilaginous core surrounded by a bony collar ) ( Fig 4B , 4C , 4F and 4G ) . We established that SOST was expressed by a diversity of cell types in these regions , using histological staining ( hematoxylin–eosin [HE] , Fig 4A and 4E ) and double fluorescent ISH for an osteoblast marker ( collagen type I alpha 1 [Col1a1] ) and a chondrocyte marker ( collagen type II alpha 1 [Col2a1] ) on serial sections ( Fig 4D and 4H ) . SOST-expressing cells in the core of fin Rs were chondrocytes , as confirmed by their morphology and Col2a1 expression ( Fig 4B and 4C ) . The SOST-positive cells on the surfaces of both the spine and the fin R , however , were osteoblasts , as evidenced by their expression of Col1a1 or both Col1a1 and Col2a1 , as well as their position , morphology , and gene expression patterns ( Fig 4D ) [27] . SOST-expressing cells in the IVR were identified as chordoblasts , based on their expression of Col2a1 , their location , and their morphology [28 , 29] ( Fig 4H ) . Surprisingly , double fluorescent ISH performed on sections of the osteocytic vertebrae of zebrafish showed results similar to those seen in the anosteocytic vertebrae of medaka , with SOST expression seen primarily in chordoblasts and osteoblasts and weakly , if at all , in osteocytes ( Fig 5 ) . The finding that SOST is expressed in anosteocytic bone by osteoblasts , chondrocytes , and chordoblasts is intriguing since it raises the possibility that these cells may be involved in modeling regulation . Evidence of SOST expression , however , does not necessarily mean that SOST expression levels are dependent on loading . We therefore compared SOST expression levels in untrained medaka with those of medaka swim trained for 1 hour and for 10 days . Qualitative comparisons of double fluorescent ISH of SOST and Col1a1 ( Fig 6A ) showed a decrease in SOST signal intensity after short-term swim training , suggesting that as in mammals , loading results in decreased SOST expression . These results were verified by real-time quantitative polymerase chain reaction ( RT-qPCR ) , showing SOST expression levels decreased significantly ( implying reduced inhibition of osteoblast activity ) after 1 hour of swim training compared to those in the vertebral column of untrained fish ( Fig 6B ) . Concomitantly , Col1a1 expression began to increase , though not to a level significantly different from controls , implying the beginning of osteoblast recruitment . After 10 days of swim training , SOST expression had returned to baseline , whereas Col1a1 expression had significantly increased ( Fig 6B ) . All original raw data obtained by RT-qPCR analysis can be found in S1 Data . The asynchrony of SOST expression and Col1a1 production suggests that bone loading promotes rapid down-regulation of SOST , but that the effect of inhibition release on osteoblast proliferation takes time to take effect . The increase in SOST expression once osteoblasts have reached peak stimulation ( manifested by massively increased expression levels of Col1a1 ) points to a negative feedback mechanism in medaka skeletons , probably a sclerostin-mediated defense against excessive bone deposition , as employed by mammalian osteocytes embedded in newly modeled or remodeled bone [30 , 31] . These results point to SOST expression levels serving the same regulatory function of the bone-modeling process in anosteocytic fish bone as in osteocytic mammalian bone despite the different cellular origin of SOST . To further verify that SOST expression levels in medaka are directly linked to bone formation , we injected fish with a custom-designed vivo-morpholino ( MO ) construct once every 3 days for 2 and a half weeks . The morpholino was designed to cause splice modification of the SOST gene ( confirmed by gel electrophoresis; see Fig 6C ) , thereby mimicking the physiologic result of skeletal loading by effecting a knockdown of SOST expression and a decrease in sclerostin levels , albeit without actual skeletal loading . These fish were therefore not swim trained in order to ensure that any observed changes in bone deposition were the direct result of SOST down-regulation and not some other byproduct of exercise . Injections were performed close to the ventral aspect of a caudal vertebra ( Fig 6D ) , and bone growth was visualized with fluorochrome staining at the beginning and end of the experimental period . Intense bone formation was indeed observed close to the injection site , where SOST expression had been locally knocked down ( Fig 6D ) . This is in contrast to the lack of new growth at the same vertebral site in control animals , medaka injected with standard control ( mismatch ) morpholino . Vertebral regions remote from the site of injection ( e . g . , the dorsal aspect of the injected vertebra ) also acted like controls , showing no increase in bone formation .
Our results provide convincing evidence for the regulatory role of SOST in modeling of the anosteocytic skeleton by showing the association between decreasing SOST-expression levels and increased bone deposition in the medaka skeleton ( Fig 6 ) . In this study , we present evidence for the existence of a successful osteocyte-independent mechanism that does not require a dense network of interconnected sensors in the bulk of the material but relies on regulation by surface cells that express SOST ( Fig 7 ) . In this way , our findings illustrate that the dogma of osteocyte exclusivity in bone-modeling regulation does not apply to all vertebrates , in the process also raising doubts as to its accuracy among mammals . By evincing a shared bone-building response mechanism in osteocytic and anosteocytic bone types , our observations provide experimental validation for recent speculations that vertebrate skeletal mechanobiology is less osteocyte-centric than currently believed [11 , 12 , 32] . Sporadic reports have noted SOST expression by several nonosteocytic cells in mammals—by hypertrophic and osteoarthritic chondrocytes [33 , 34] , osteoblast-like osteosarcoma cells [35 , 36] , and even in normal osteoblasts , albeit at low levels [25 , 37]; however , these observations were not linked to bone-modeling regulation . Mammalian chondrocytes and osteoblasts are both mechanoreceptive , although osteoblasts were somewhat less sensitive to strain and fluid shear than osteocytes [38 , 39] . Results of our FEAs indicate that peak strains occur primarily near the external surfaces of the vertebral bone ( Fig 3 ) . This finding lends credence to the possibility that mechanosensors like osteoblasts , located only on bone surfaces , can provide information that is sufficient to orchestrate a “mechanically smart” response to loading despite the absence of osteocytes and therefore of strain data from within the bone matrix . The involvement of SOST in osteogenesis regulation in both fishes and mammals and in bones with and without osteocytes indicates a fundamental conservation across vertebrates at the level of the molecular machinery—but not the cellular agents—controlling the adaptation of bone to mechanical loads . Our findings establish a mechanistic link between skeletal loading , local modulation of SOST expression , and bone modeling in both anosteocytic and osteocytic fishes ( Fig 7 ) . The demonstrated multifunctionality of fish osteoblasts , chondrocytes , and chordoblasts—sensing loads and regulating tissue deposition via SOST , even when osteocytes are present—provides the first experimental support for speculations that nonosteocytic cells can assume some duties of mammalian osteocytes [3 , 12 , 17 , 40] . The existence of an effective , osteocyte-independent modeling process in fishes raises intriguing possibilities with regard to the evolution of bone modeling , the roles played by various mesenchymal cells , and how these roles may have changed . On the one hand , fish and mammals may simply have different cellular effectors of bone modeling , even if they share a dependence on SOST . It is difficult to determine which cellular agents mediated bone modeling in stem vertebrates . However , given the apparent lack of reliance on osteocytes in fishes , the “mammal-like” osteocyte-dependent mechanism of bone modeling may have arisen or increased in prominence during vertebrate evolution , perhaps in the water-to-land transition , when gravitational loads on the skeletal changed drastically [41] . Similarly , the “fish-like” osteocyte-independent mechanism of bone modeling may have been lost . Alternatively , such a mechanism may exist in mammals , perhaps acting synergistically with osteocytic regulation but unrecognized until now because of the research emphasis on osteocytes . Osteocytes are derived from osteoblasts , which in turn arise from pluripotent , mesenchymal stem cells capable of becoming osteoblasts or chondroblasts . Our illustration of SOST expression by osteoblasts in the bones of both osteocytic and anosteocytic fishes suggests an ancient association of SOST with the osteoblast–osteocyte line . Some characteristics typically associated with osteocytes could therefore have been inherited from osteoblasts and osteochondroprogenitor cells . Demonstration of SOST expression by multiple cell types and identification of SOST as an evolutionarily conserved key player in bone modeling in fishes expands the relevance of sclerostin to modeling regulation for both paracrine and autocrine signaling . Currently , a wave of new sclerostin-targeting therapies are being explored in an effort to control sclerostin’s antianabolic properties in the treatment of osteoporosis and other skeletal disorders [42–44] . Our findings suggest great potential for the massively speciose clade of fishes , with both osteocytic and anosteocytic members , as a powerful and relevant platform for research in bone physiology , as well as fracture healing and bone therapeutics .
All in vivo fish experiments were approved by the ethics committee of the Hebrew University of Jerusalem , permit # MD-16-14844-3 . Young adult ( 8–12 months old ) medaka ( Oryzias latipes ) and zebrafish ( Danio rerio ) were obtained from commercial fish suppliers ( Aquatic Research Organisms , Hampton , NH , USA , and Aquazone , Tzofit , Israel , respectively ) . The fish were maintained in a controlled environment under a 12 hour:12 hour light/dark cycle at 28°C , in accordance with standard guidelines , and fed appropriate commercial fish feed [45] . For each experiment , the medaka used were laboratory-reared males , hatched in the same month and phenotypically similar . Since zebrafish were obtained from a commercial supplier and thus included both males and females , they exhibited somewhat greater variation in total length . S1 Table and S3 Fig provide details regarding the age and length data of medaka and zebrafish used in the various experiments . Measurements of standard lengths of the fish used in this study can be found in S1 Data . The investigation reported in this manuscript included 5 separate experimental set-ups , which consisted of the following: Mechanical loading of fish vertebral columns was achieved by swim training , using a custom-built swim-training device . The device consists of a water pump , a reservoir tank , overflow exits , and 4 training chambers ( see videos of untrained and trained fish in S1 Video ) . The flow rate in each training chamber is separately controlled by a valve and continuously measured by flow meters . Flow through the training chambers is made uniformly semilaminar by the upstream placement of an array of 10-cm–long straws . A mesh screen located downstream , at the end of each chamber , prevents fish from leaving the swim chambers . Prior to the initiation of swim-training experiments , 3 medaka and 3 zebrafish were tested for their critical velocity ( swimming velocity at which these fish fatigue ) , using a previously published protocol [46 , 47] . The optimal velocity for swim training was then defined as 45% of the critical velocity . During experiments , each of the 4 training chambers contained a different group of fish ( control and swim-trained medaka and control and swim-trained zebrafish , n = 12 in each group ) . Temperature and photo period in the swim-training device were kept the same as in the holding tanks . Fish were fed twice daily ( before and after training ) . Untrained ( control ) fish were kept at a minimal flow rate . The training protocol involved sustained swimming at a constant velocity ( 26 cm/s for medaka and 33 cm/s for zebrafish , which equals approximately 11 body lengths/s for each species ) for 7 hours per day , 5 days per week for 6 weeks . In order to study the bone-formation process , medaka ( 12 control and 12 swim-trained fish ) and zebrafish ( 12 control and 12 swim-trained fish ) were double labeled by intraperitoneal injections of 2 different fluorochromes . The injections consisted of alizarin red ( Sigma Aldrich , St . Louis , MO , USA ) on the first day of the experiment ( t = 0 ) and of calcein green ( Sigma Aldrich ) at the end of the experiment ( t = 6 weeks ) . All fluorochromes were prepared for injection by modification of the method described previously by Atkins and colleagues [15] . Briefly , alizarin red and calcein green solutions were prepared with 0 . 2% bicarbonate buffer . Prior to administration , the solutions were sterilized using 0 . 2 μm Minisart high-flow filters ( Sartorius , Göttingen , Germany ) . Fish were anesthetized with 0 . 02% tricaine methane-sulfonate ( MS-222; Sigma Aldrich ) prior to fluorochrome injections . Alizarin red and calcein green were injected into the peritoneal cavity under the guidance of a stereo dissection microscope at a dose of 50 mg/kg and 0 . 5 mg/kg , respectively , using a microsyringe ( Microliter syringe; Hamilton , Reno , NV , USA ) . After injections , fish were allowed to recuperate in an isolated tank containing clean water . All fish recovered uneventfully from all injections , except for 1 medaka that was excluded from the experiment . At the end of the swim-training experiment , fish were removed from the training chambers and killed with an overdose of tricaine methane-sulfonate ( MS-222; Sigma Aldrich , USA ) . The caudal part of the vertebral columns was gently dissected and manually cleaned of soft tissue using a stereo dissection microscope . Caudal vertebrae are numbered from caudal to cranial such that the first caudal vertebra is the most caudal . We used the fourth caudal vertebra ( shown in Fig 1 ) in all experiments , except for ISH and RT-qPCR experiments , as detailed below . The harvested tissues were further processed as described below . The fourth caudal vertebrae of swim-trained and control medaka and zebrafish were imaged by confocal microscopy ( Leica SP8 microscope , Leica , Wetzlar Germany ) to study the precipitation patterns of the injected fluorochromes . The excitation/emission wavelengths used to observe fluorochromes were 543/580–670 nm and 488/500–535 nm for alizarin red and calcein green , respectively . Z-stack images of a comparable caudal vertebra were viewed using ImageJ/FIJI ( FIJI v . 1 . 51r , NIH , Bethesda , MD , USA ) in the Maximum Intensity Projection ( MIP ) mode in order to evaluate bone formation during the experiment . The fourth caudal vertebrae of trained and untrained fish ( dissected from fluorochrome-stained medaka and zebrafish ) were scanned with a desktop micro-CT scanner ( 1172 scanner , SkyScan; Bruker , Kontich , Belgium ) in order to obtain their 3D morphology . The X-ray source was set at 50 kV and 200 μA . 4 , 000 projections were acquired for each scan over an angular range of 360 degrees . The scans had an isotropic voxel size of 2μm , and exposer time was 3 . 5 seconds . All scans were performed with a 0 . 5 mm aluminum filter , in order to decrease beam-hardening effects . Scans were reconstructed using commercial software ( NRecon Skyscan software , SkyScan; Bruker Kontich , Belgium ) . Reconstructed scans were volume rendered ( Amira software v . 6 . 3 , FEI , Hillsboro , OR , USA ) to visualize the 3D morphology of the selected vertebra or segmented and meshed to create the geometry for an FE model ( described below ) . In order to study the 3D morphology of the paravertebral musculature , the caudal half of a medaka ( including soft tissues ) was gently cleaned from scales and skin , using a stereo dissection microscope . The tissue was fixed overnight in 4% PFA and dehydrated in increasing concentrations of ethanol ( 25% , 50% , and 70% ) . In order to improve the contrast of the soft tissues , the sample was stained with 0 . 3% phosphotungstic acid solution ( PTA; Sigma Aldrich ) in 70% ethanol for 6 . 5 days . After PTA staining , the sample was washed in 70% ethanol to remove residues of PTA and scanned using a micro-CT scanner ( XRadia MICRO XCT-400; Zeiss , Thornwood , NY , USA ) . The X-ray source was set at 40 kV and 200 μA . In total , 1 , 200 projections were acquired over an angular range of 180 degrees . The scans were made with an isotropic voxel size of 2 . 57μm and an exposure time of 3 seconds . Scans were reconstructed using XRadia software , using a filtered back-projection algorithm . The reconstructed scan was then volume rendered using Amira software v . 6 . 3 ( FEI ) in order to visualize the musculature–bone inter-relationships . For higher-resolution tomography and visualization of submicron features , the fourth caudal vertebrae of trained and untrained medaka and zebrafish were scanned using synchrotron-based microtomography ( SμCT ) . Scans were performed at beamline ID19 of the European Synchrotron Radiation Facility ( ESRF , Grenoble , France ) . The samples were scanned using X-ray photon energy of 34 keV . A total of 4 , 000 radiographic projection images were recorded over 180° with an exposure time of 0 . 2 seconds and an effective pixel size of 650 nm . Propagation-based X-ray phase-contrast enhancement was induced using a sample-detector distance of 88 mm . ESRF in-house code was used to reconstruct the data , where voids and interfaces were enhanced by means of Paganin-based filtering [48] . The reconstructed scans were viewed with Amira software v . 6 . 3 ( FEI ) . For 3D visualization of osteocytic lacunae , mineralized tissue and voids were separately segmented and rendered with different colors . Both zebrafish and medaka vertebrae underwent the exact same semiautomated segmentation . In order to determine the mechanical properties of the bone material , the fourth caudal vertebrae of 6 medaka ( 3 controls and 3 swim-trained fish ) and 6 zebrafish ( 3 controls and 3 swim-trained fish ) were dehydrated with increasing concentrations of ethanol and embedded for 8 hours in polymethylmethacrylate ( PMMA ) , which was polymerized in an oven at 60°C . The embedded vertebral columns were cut for indentation as ca . 0 . 5-mm–thick transverse and longitudinal sections using an Isomet slow-speed water-cooled diamond-blade saw ( Buehler , Lake Bluff , IL , USA ) . The slices were ground with 3 μm and 1 μm grit SiC lms ( Buehler ) , then polished with nap cloth soaked with diamond suspension ( 0 . 25 μm; Struers , Cleveland , OH , USA ) or alumina suspension ( 0 . 25 μm; Buehler ) . The polished sections were nanoindented using a scanning nanoindenter ( Ubi 1 , Hysitron , Billerica , MA , USA ) with a Berkovic indenter tip [49] . An optical microscope , aligned with the nanoindenter tip , was used to locate regions of interest on the evenly polished bone surface . The following load function was used: maximum load of 2 . 5 mN , loading at 0 . 5 mN s−1 , holding at maximum force for 60 seconds , and unloading to 0 . 5 mN at a rate of 0 . 2 mN s−1 , followed by a second holding time of 20 seconds , and finally unloading to 0 mN at a rate of 0 . 1 mN s−1 . Young's modulus of the material was calculated using the Oliver–Pharr method , based on the slope of the unloading curve in the region between 20% and 95% of the maximum load [50] . Following indentation , the samples were coated with gold and examined with a scanning electron microscope ( JCM 6000 benchtop SEM; Jeol , Peabody , MA , USA ) to verify the quality and position of the indentations . Unacceptable indents ( e . g . , indents that were in the embedding material ) were discarded . Original raw data for all indentation-determined Young’s moduli values are presented in S1 Data . The reconstructed micro-CT scan of a representative caudal vertebra of medaka was imported into Amira software v . 6 . 3 ( FEI ) . All 2D slices of the scan were semiautomatically segmented by selecting an appropriate threshold and manually correcting when necessary , and a 3D model of the vertebra was created . The same software package was used to mesh the model with tetrahedral elements , resulting in 386 , 289 10-node tetrahedral elements . The meshed model was then exported into an FEA software package ( Patran 2017r1; MSC , USA ) . Fig 3 shows detailed features of the FE model . The bone material in the model was assumed isotropic and linearly elastic . Poisson’s ratio was taken to be 0 . 3 . Young’s moduli were assigned based on nanoindention results and attenuation values in the micro-CT scan . Specifically , the attenuation values of the micro-CT scan were divided into 10 equally spaced bins , and a custom-written MATLAB code ( MATLAB R2016b , The MathWorks , Natick , MA , USA ) identified each tetrahedral element with the corresponding attenuation value of the voxel in its position . The range of Young's moduli obtained by nanoindentation of several vertebrae was between 6 GPa and 28 GPa , although the majority of elements had moduli in the range of 18 GPa to 22 GPa ( see S2 Fig ) . The range of values was similarly divided into 10 bins , and each of the 10 bins of attenuation values was assigned a corresponding Young's modulus value . As a result , each element of the model received one of 10 Young's modulus values , distributed according to the level of mineralization in different regions of the vertebra . The main challenge in creating a valid FE model of a physiologically loaded fish vertebra is to apply physiologically reasonable forces ( magnitudes and directions ) . Such data are not available in the literature , and therefore the contrast-enhanced scan of the caudal vertebrae described above ( see muscles and vertebrae in S1 Fig ) was studied to determine the approximate size , fiber orientation , and regions of insertion of the paravertebral muscles attached to the caudal vertebra selected for the FE model . These data provided approximate muscle force application regions and muscle force directions . The force magnitudes used in the model were based on scaling muscle forces described in previous publications [51 , 52] , such that the ratios of muscle forces reflected their relative cross-sections . It should be noted that since the objective here was to find the relative distribution of strains in the vertebra and since the model is linearly elastic , only the relative magnitudes of the muscle forces are needed . For histology and ISH , the caudal regions ( caudal vertebrae 1–10 ) of medaka and zebrafish ( after skin and scale removal ) were fixed in 4% paraformaldehyde ( PFA/PBS ) for 24 hours at 4°C while being shaken gently . After fixation , the tissues were decalcified for 24 hours in 0 . 5 M EDTA ( pH 7 . 4 ) , dehydrated in increasing concentrations of ethanol , and imbedded in paraffin . The embedded tissues were cut into 7-μm–thick sagittal sections , which were mounted onto glass slides . HE staining was performed following standard protocols . The RNA probes for ISH were prepared by in vitro transcription of the reverse transcriptase cDNA fragments by using T7 RNA polymerase . Single nonfluorescent ISH was performed using a digoxigenin ( DIG ) -labeled probe for medaka SOST . Double fluorescence ISH ( FISH ) was performed using fluorescein- and DIG-labeled probes . ISH and FISH were performed following the protocol described by Shwartz and Zelzer [53] . After hybridization , slides were washed , quenched , and blocked . Hybridization probes for single nonfluorescent ISH were detected by incubation with alkaline-phosphatase–conjugated anti-digoxigenin antibody ( anti-DIG-AP; Roche , Basel , Switzerland ) . Hybridization probes for double FISH were detected by incubation with peroxidase-conjugated anti-digoxigenin antibody ( anti-DIG-POD , 1:300; Roche ) and peroxidase-conjugated anti-fluorescein antibody ( anti-fluorescein-POD , 1:200; Roche ) followed by Cy2- and Cy3-tyramide-labeled fluorescent dyes according to the instructions of the TSA Plus Fluorescent Systems Kit ( Perkin Elmer , Waltham , MA , USA ) . All primers that were used to generate the probes are listed in S2 Table , some of them based on previous publications [54–57] . ISH results for 2 vertebral regions in medaka and zebrafish are shown in black and white in S4 Fig and S5 Fig , respectively . For gene expression analyses , 2 separate swim-training experiments were conducted . In order to evaluate the immediate response to swim training , medaka were trained for 1 hour , allowed to rest for 3 hours , and then killed . To evaluate the late response to swim training , medaka were trained for 10 days , allowed to rest overnight , and then killed . SOST and Col1a1 expression levels were analyzed to evaluate the effect of mechanical loading on SOST expression and osteoblastic activity . Caudal vertebrae 1–10 of 1-hour swim-trained medaka , 10-day swim-trained medaka , and untrained medaka ( control ) were separately homogenized in TRI-reagent ( Sigma Aldrich ) using a tissue homogenizer . Total RNA was isolated from the tissues according to manufacturer’s instructions . RNA quality and concentration were verified by NanoDrop spectrophotometry . One μg of RNA was reverse transcribed to cDNA with a qScript cDNA Synthesis Kit ( Quanta Biosciences , Gaithersburg , MD , USA ) . Transcript levels of SOST and Col1a1 were analyzed using PerfeCTa SYBR Green SuperMix ( Quanta Biosciences ) on a StepOne Real-Time PCR system ( Applied Biosystems , Foster City , CA , USA ) and normalized to housekeeping gene ( RPL-7 ) levels . All primers used for gene expression analysis are listed in S2 Table . Relative expression was calculated using the delta-delta Ct standardization method . Statistical analysis of the results employed Student t test ( two-sided ) . P-values < 0 . 05 were considered to be statistically significant . Custom splice-site–targeted MO oligonucleotides for the SOST gene ( 5′- AAAAGGACACTTACTATATGAAACTGT-3′ ) and standard control oligonucleotides were purchased from Gene-Tools ( Philomath , OR , USA ) . The custom MO was designed against the donor splice site to block SOST gene pre-mRNA splicing in adult medaka . Two μl of the MO ( 0 . 5 mM diluted with PBS [pH 7 . 4] ) were injected to the ventral side of the fourth caudal vertebra using a microsyringe ( Microliter syringe , Hamilton ) under the guidance of a stereo dissecting microscope . The semitransparent body wall of medaka allowed clear visualization of the vertebra and the site of injection . Injections of SOST-targeted and standard control MOs were made every 3 days for 2 . 5 weeks in 5 medaka and 4 medaka , respectively . For evaluation of the effect of SOST knockdown on bone formation , we injected alizarin red prior to MO injections and calcein green at the end of the experiment . Fluorochrome staining was imaged by confocal microscopy ( as described above ) . In order to validate the efficiency of the splice-blocking SOST MO , both the ventral and dorsal sides of the caudal vertebral column of 4 control medaka and 10 MO-injected medaka were injected with 1 . 5 μl standard control or SOST MO , respectively . One to 4 hours after a single injection , fish were killed , and their caudal vertebral columns were harvested . Each 2 vertebral columns of fish from the same group were homogenized together in TRI-reagent ( Sigma Aldrich , USA ) using a tissue homogenizer . Total RNA was isolated from the tissues according to manufacturer’s instructions . RNA quality and concentration were verified by NanoDrop spectrophotometry . One μg of RNA was reverse transcribed to cDNA with a qScript cDNA Synthesis Kit ( Quanta Biosciences ) . Primers ( listed in S2 Table ) for reverse transcription PCR ( RT-PCR ) were designed to detect the intron insertion of the morpholino-affected transcripts . RT-PCR products were separated by electrophoresis in 2% agarose gels and visualized by ethidium bromide staining . The splice modification was verified by RT-PCR reaction , using an E1 and an i1 , which showed insertion of the intronic sequence into to the morphant transcripts ( see S6 Fig ) . All primers used here were specific to SOST on chromosome 19 . Two other paralogues of SOST—on chromosomes 16 and 11 , respectively—were not used in this study , though we do not exclude the possibility that the 2 additional paralogues may also participate in the regulation of modeling . | Bone is a “smart” tissue , able to sense loads within its bulk and change its morphology when needed by a process named bone modeling . This process is carried out by bone-depositing cells ( osteoblasts ) and bone-resorbing cells ( osteoclasts ) and is regulated by osteocytes—cells that reside in small cavities within the bone tissue . Osteocytes are considered to function as mechanosensors , detecting areas of high loads that require modeling , and master regulators of osteoblasts and osteoclasts . Curiously , evolutionarily advanced fish do not have osteocytes in their bones , although more basal fish and all other bony vertebrates have them . In this paper , we show how the bones of advanced fish can respond to load in a mechanically efficient way despite the absence of osteocytes . We describe the molecular mechanism , which we found to be the same as in all other vertebrates; however , we show that the cellular effectors are different . The protein sclerostin , which is produced by osteocytes in mammals and is a potent suppressor of bone building by osteoblasts , is produced by a variety of nonosteocytic cells in medaka and zebrafish , and nonosteocytic skeletal cells serve as sensors and responders in these species . These results challenge current paradigms of osteocyte exclusivity in the regulation of bone modeling . | [
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] | 2019 | A novel nonosteocytic regulatory mechanism of bone modeling |
Classical laboratory strains show limited genetic diversity and do not harness natural genetic variation . Mouse models relevant to Alzheimer’s disease ( AD ) have largely been developed using these classical laboratory strains , such as C57BL/6J ( B6 ) , and this has likely contributed to the failure of translation of findings from mice to the clinic . Therefore , here we test the potential for natural genetic variation to enhance the translatability of AD mouse models . Two widely used AD-relevant transgenes , APPswe and PS1de9 ( APP/PS1 ) , were backcrossed from B6 to three wild-derived strains CAST/EiJ , WSB/EiJ , PWK/PhJ , representative of three Mus musculus subspecies . These new AD strains were characterized using metabolic , functional , neuropathological and transcriptional assays . Strain- , sex- and genotype-specific differences were observed in cognitive ability , neurodegeneration , plaque load , cerebrovascular health and cerebral amyloid angiopathy . Analyses of brain transcriptional data showed strain was the greatest driver of variation . We identified significant variation in myeloid cell numbers in wild type mice of different strains as well as significant differences in plaque-associated myeloid responses in APP/PS1 mice between the strains . Collectively , these data support the use of wild-derived strains to better model the complexity of human AD .
Alzheimer’s disease ( AD ) is the most common cause of adult dementia , with approximately 6 million Americans diagnosed with either clinical AD or mild cognitive impairment in 2017[1] . Age is the greatest risk factor and currently we have the largest aging population that has ever been on this planet [2] . Globally , there are 50 million people living with dementia and this number is expected to reach 152 million by 2050 . Low- and middle-income countries are the hardest hit , comprising 66% of global cases [3] . AD is pathologically characterized by the accumulation of beta amyloid ( β-amyloid ) plaques , neurofibrillary tangles , and widespread neuronal loss . Another prominent feature is the neuroinflammatory response by a variety of cells including astrocytes and microglia . Multiple studies have identified two forms of AD: familial AD ( FAD , also known as early-onset AD ) and sporadic AD ( also known as late-onset AD ) . Widely-used mouse models of AD utilize FAD mutations in amyloid precursor protein ( APP ) and presenilin 1 and 2 ( PSEN1 and PSEN2 ) . However , a recent review on the current status of AD clinical trials has suggested that the failure of these trials , in part , is due to the inability of current AD mouse models to translate to humans [4] . While FAD mouse models have been vital to understand early drivers of amyloidosis , to date , they do not effectively model all hallmarks of AD , particularly frank neurodegeneration . This has led some to question the utility of mouse models as preclinical models for AD and other diseases of complex etiologies . Studies including the Dominantly Inherited Alzheimer’s Network ( DIAN ) and the Religious Order Study and Memory and Aging Project ( ROSMAP ) show significant variation in age of onset and rate of disease progression in individuals who inherit the same FAD mutations[5 , 6] . Furthermore , new work performing a genome-wide association study ( GWAS ) [7] on individuals with a family history of AD identified multiple novel variants . This suggests that the underlying genetic contribution of many cases of FAD are also due to multiple interacting variants , not simply the single strong variants such as those in APP , PSEN1 and PSEN2 . Therefore , the same is likely true in mouse models . Murine models relevant to AD have been almost exclusively developed on a single genetic background , C57BL/6 ( B6 ) . Few studies have assessed FAD mutations in a limited number of alternative genetic backgrounds including 129S1/SvImJ [8] , A/J and DBA/2J ( D2 ) [8–10] . These studies showed genetic background altered β-amyloid deposition and seizure incidence , but modifications to neuronal cell loss were not reported . Supporting the potential of incorporating genetic variation in AD mouse models , a recent study used F1 crosses between B6 and thirty classical inbred strains to show that the phenotypes observed from a heterozygous null mutation related to neurological function were not generalizable across strain [11] . Interestingly , there were multiple cases in which there were inverse effects of the same allele on phenotypic outcomes . Another recent publication showed greater transability of the mouse to human Alzheimer’s through the development of a new mouse panel known as the AD-BXDs . This panel was developed by crossing congenic B6 5xFAD mice with BxD males ( B6xD2 ) , greatly increasing the genetic diversity in the context of 5 aggressive familial mutations . Aging and characterization of these mice indicated a greater range in AD related phenotypes such as plaque pathology and cognitive deficits , and a greater transcriptional overlap with human AD [12] . These studies highlight the likely huge potential for generating more translatable AD mouse models through the use of different genetic contexts . Therefore , to take full advantage of the level of natural genetic variation available in mice , we employed genetically distinct wild-derived strains . Historically , the lineage of commonly used classical laboratory strains can be traced to domesticated fancy mouse stock developed on a farm in Massachusetts in the early 1900s [13] . Due to this , classical laboratory strains are undefined genomic mixtures of two or more subspecies of Mus musculus ( including Mus musculus domesticus and Mus musculus molossinus ) . They exhibit limited inter-strain polymorphisms ( less than 5 million differences between a classical inbred strain when compared to B6/J ( [14] and Fig 1 ) , and do not represent any animal that exists in nature . To overcome the limitations of classic laboratory strains , ‘wild-derived’ strains were introduced as research models in the 1980s . Wild-derived strains are genetically distinct subspecies of Mus musculus ( e . g . Mus musculus musculus and Mus musculus castaneous ) . Founders of each strain were caught from well-established wild mice populations from around the world ( see methods ) , and then inbred [15] . Wild-derived strains show a much greater degree of genetic variation compared to B6 than other classical inbred strains do ( between 6 and 17 million differences ) including millions of private variations . Importantly , the genetic variation encompassed in these strains and interactions of different gene networks evolved , thus , are likely physiologically relevant to the natural world . This variation includes genes previously associated with AD including Apoe , Trem2 and Tyrobp , and these strains also show variation in phenotypes relevant to AD risk factors including cardiovascular health [16] , insulin secretion [17–19] , gut microbiota [19 , 20] and circadian rhythm [21] . In this study , we hypothesized that incorporating FAD mutations into genetically distinct , wild-derived mouse strains would establish more clinically-relevant AD mouse models compared to those on classic laboratory strain backgrounds . To test this , two commonly used FAD mutations ( APPswe and PSEN1de9 , herein referred to as APP/PS1 ) were introduced into three wild-derived strains representative of the three Mus musculus ( mus ) subspecies: WSB/EiJ ( WSB , M . mus domesticus ) , PWK/PhJ ( PWK , M . mus musculus ) , and CAST/EiJ ( CAST , M . mus castaneus ) . Assessment of AD-relevant phenotypes showed that the effects of the APP/PS1 transgenes are strain-dependent and sex-dependent , with significant differences in amyloid deposition , neuronal cell loss and cerebral amyloid angiopathy ( CAA ) . Transcriptional profiling and neuropathological assessment suggested myeloid cell responses are major contributors to the variation in AD phenotypes we observed in the wild-derived AD strains .
Three wild-derived AD mouse models were created by backcrossing for at least six generations the APP/PS1 transgenes from B6 to the genetically distinct substrains WSB , PWK and CAST ( Fig 1 ) . The presence of both the APPswe and PSEN1de9 transgenes was confirmed by PCR ( S1 Fig ) . For each strain , balanced cohorts of female and male wild type ( WT ) and APP/PS1 mice were established and aged to 6 months–an age window when the majority of plaques have seeded and are in an exponential growth phase in B6 . APP/PS1 [22–24] . APP/PS1 and randomized WT litter mate controls from each strain were tested sequentially in the following order: ( 1 ) PWK , ( 2 ) WSB , ( 3 ) CAST and ( 4 ) B6 . For this first characterization of these new strains , a set of metabolic and functional assays were selected that spanned across a wide-range of AD-relevant phenotypes . Significant strain- , sex- or genotype-specific differences were observed in body weight , body temperature and body composition ( S2 Fig ) . In addition , significant differences were observed in activity measured using both piezoelectric floor monitoring and open field arenas ( S3 Fig ) . WT mice from all three wild-derived strains were significantly more active than B6 WT mice . Also , irrespective of genetic context , all APP/PS1 strains showed the previously reported increase in activity [25] compared to their WT counterparts . Cognitive function was assessed in wild-derived strains and B6 using spontaneous alternation ( working memory ) and novel spatial recognition ( short-term memory ) in a Y-maze . Given the increased behavioral ‘wildness’ [26 , 27] of the wild-derived strains , the Y-maze was modified to include specially fabricated covers ( see Methods ) to minimize likelihood of escape . This was the first time that these tasks had been employed by us for either aging B6 mice or wild-derived strains of any age . However , these tasks had been previously validated using young B6 male mice [28] and further validated here using PWK ( the first wild-derived strain to be tested ) . For spontaneous alternation ( S4A–S4C Fig ) , percent alternation exceeded 50% for all strains irrespective of genotype . Despite hyperactivity phenotypes observed in open field in wild-derived mice , there were no transgenic-related differences in activity levels as measured by total arm entries , thus , increased activity does not confound the interpretation of this task . Furthermore , we found no correlation between number of arm entries and performance . Therefore , these data suggest working memory was not affected by the APP/PS1 transgenes . For novel spatial recognition ( S4D–S4F Fig ) , strain- , sex- and genotype-specific differences were observed . For the PWK strain , a robust preference for the novel arm after a 30-minute delay was shown for both male and female WT and APP/PS1 mice indicating an intact short-term memory . In contrast , for WSB females and CAST males , WT but not APP/PS1 mice showed a preference for the novel arm suggesting working memory was impaired in both female WSB . APP/PS1 and male CAST . APP/PS1 mice . Highlighting the challenges of identifying tasks that can be performed by diverse strains , short-term memory using this task could not be determined for male WSB . APP/PS1 , female CAST . APP/PS1 , and male and female B6 . APP/PS1 , as the strain-matched and sex-matched WT counterparts were unable to perform the task . Next , to assess neurodegeneration , NEUN+DAPI+ cell counts were performed across all strains , sexes and genotypes in a region of the superior cortex and in the CA1 region of the hippocampus , two brain regions commonly affected early in human AD ( Fig 2 , S1 Table ) . Interestingly , even in the absence of the APP/PS1 transgenes , strain background was a significant driver of the overall neuronal cell number in the CA1 region . Importantly , there was a significant loss of NEUN+DAPI+ cells in female WSB . APP/PS1 in the cortical region and CA1 compared to WT WSB females . There was also significant loss of neurons in male and female CAST . APP/PS1 mice in the CA1 region . There was no detectable NEUN+DAPI+ loss in either B6 . APP/PS1 ( as previously published in [10 , 29 , 30] ) or PWK . APP/PS1 strains in the two regions studied . Despite the presence of neurodegeneration in CAST . APP/PS1 and female WSB . APP/PS1 , there was no evidence of increased tau pathology using AT8 , a marker of early tau hyperphosphyloration ( S5 Fig ) . Amyloidosis was assessed in all four strains using ThioS staining , ELISA and Western blotting . Surprisingly , numbers of cortical ThioS+ plaques were significantly decreased in all three of the wild-derived APP/PS1 strains in comparison with B6 . APP/PS1 ( Fig 3A–3C , S2 Table ) . Numbers of hippocampal ThioS+ plaques were also significantly decreased with the exception of WSB . APP/PS1 females . No plaques were observed in WT mice from any of the four strains in any brain region . Plaque morphology appeared different between B6 . APP/PS1 and wild-derived APP/PS1 strains . Specifically , there was an absence of small ThioS+ plaques in wild-derived APP/PS1 compared to B6 . APP/PS1 mice . Despite the reduced numbers of plaques , there was a significant increase in Aβ42 ( measured by ELISA ) in both female CAST . APP/PS1 and WSB . APP/PS1 compared to B6 . APP/PS1 ( Fig 3D ) . This increase cannot be accounted for simply by differences in mutant APP production as Western blotting using 6e10 ( antibody to human mutant APP ) showed similar APP protein levels across all strains ( Fig 3E ) with the exception of male PWK . APP/PS1 ( significant difference between male B6 . APP/PS1 and male PWK . APP/PS1 , p ≤ 0 . 01 ) . Therefore , our data suggest that at 8 months , plaques in the wild-derived APP/PS1 strains may be further along in the rapid growth period previously defined for B6 . APP/PS1 mice [24] . Another prominent amyloid phenotype observed in the wild-derived strains was ThioS+ vessels , suggesting the occurrence of cerebral amyloid angiopathy ( CAA ) . Brain sections from all strains , sexes and genotypes were examined for the presence of ThioS+ vessels and by silver staining . CAA was pronounced in vessels of CAST . APP/PS1 and WSB . APP/PS1 , but not B6 . APP/PS1 or PWK . APP/PS1 mice ( S6 Fig ) . There was no evidence of vascular staining of ThioS in WT animals . CAA has been associated with cerebrovascular damage in human AD and recent studies support a more prominent role of cerebrovascular decline in AD pathogenesis [31 , 32] . To test the relationship between CAA and cerebrovascular integrity , brain sections from WSB . APP/PS1 were assessed as they showed the greatest percentage of ThioS+ vessels . Cerebrovascular integrity was determined using antibodies to fibrin ( ogen ) , a protein that is ordinarily present in blood but its presence in the brain is indicative of blood brain barrier compromise . Fibrin was present outside of the microvessels in brain sections from WBS . APP/PS1 , but not in B6 . APP/PS1 ( Fig 4 ) . To provide insight into the strain-specific differences that may be driving the phenotype differences observed between strains , transcriptional profiling by RNA-seq was performed on the left brain hemispheres from WT and APP/PS1 male and female mice from all strains ( 93 samples in total ) . Sequencing depth ( S7 Fig ) and expression levels of the APPswe and PSEN1de9 transcripts in APP/PS1 mice ( S8 Fig ) were consistent between strains . Principle Component Analysis ( PCA ) identified strain as the greatest driver of gene expression variance across samples , consistent with the genetic distinctness of strains ( Fig 5A ) . To identify modules of genes that were differentially expressed between groups , Weighted Gene Co-expression Analysis ( WGCNA ) was performed . The majority of modules were driven by strain , independent of APP/PS1 genotype ( S9 Fig ) . However , one module ( termed ‘light yellow’ ) was driven by APP/PS1 genotype and seen in all strain backgrounds ( Fig 5B ) . This module contained 35 genes that are enriched for the Lysosome and Osteoclast Differentiation KEGG pathways ( Fig 5C ) . The light yellow module included App and Psen1 supporting the fact that this module is likely an amyloid response module . The majority of other genes in the module are expressed in myeloid cells ( either resident microglia and/or monocytes/macrophages ) . Many of these genes have been previously implicated in AD-relevant processes such as amyloid deposition and synaptic loss including C1qa , Csf1r , Tyrobp , Cx3cr1 , Cd68 and Ctsz . Importantly , DNA variations in two genes in the light yellow module , Trem2 and Cd33 , have previously been associated with human AD suggesting these genes may be early drivers of AD pathogenesis . Assessment of the eigenvalues for the light yellow module revealed two major findings . First , there was great variation in the eigenvalues when comparing WT samples between strains . For instance , eigenvalues were lowest for WT samples from WSB and CAST . This was reflected in the normalized expression levels of genes in the module . Trem2 , Tyrobp and Ctss showed the lowest expression in WT samples from WSB and CAST ( Fig 5E ) –strains that showed neuronal cell loss in the presence of amyloid ( Fig 2 ) . The second major finding was that there were marked differences in eigenvalues comparing WT to APP/PS1 samples . The greatest difference between WT and APP/PS1 samples was observed in PWK . Again , these differences were also observed at the level of individual genes within the light yellow module ( Fig 5B ) . This suggests the ability of myeloid cells to respond to amyloid is strongly influenced by genetic context . Together , these data suggest that there are intrinsic differences between myeloid cells in WT samples from different strains and that these cells respond differently to amyloid deposition . Both these factors are likely critical in determining whether or not a strain is susceptible to amyloid-induced neurodegeneration . A major and unexpected finding from the transcriptional profiling was that transcript levels of myeloid cell genes were significantly lower in WT WSB and CAST mice compared to B6 and PWK mice . This suggests that myeloid cells vary between strains , even in the absence of amyloid . To test this , IBA1+ myeloid cell numbers were determined . There was a significant difference in the numbers of IBA1+DAPI+ cells in WT mice of different strains ( Fig 6 ) . WT mice from CAST and WSB mice showed significantly fewer IBA1+ cells compared to B6 ( Bonferroni’s multiple comparison test vs B6: Male WSB p ≤ 0 . 0001 and CAST p ≤ 0 . 01; Female WSB p ≤ 0 . 01 and CAST p ≤ 0 . 05 ) . This supports the transcriptional profiling data ( Fig 5 ) . As expected , there was a significant sex and region-specific increase in IBA1+ cells in mice carrying the APP/PS1 transgenes compared to their WT counterparts ( Fig 6 , S3 Table ) . Transcriptional profiling also predicted that plaque-mediated myeloid cell responses would differ between strains . To assess this , the numbers of myeloid cells surrounding plaques were determined for each APP/PS1 strain . For each mouse , the numbers of IBA1+DAPI+ cells ( myeloid ) were determined around five plaques of similar relative size in 6 mice per strain ( a total of 30 plaques/strain , Fig 7A and 7B ) . The median number of IBA1+ myeloid cells per section was averaged per animal and then compared across strains . For male animals , CAST . APP/PS1 had the greatest number of plaque-associated IBA1+ cells , while WSB . APP/PS1 had the least . For female animals , CAST . APP/PS1 exhibited the greatest number of plaque-associated IBA1+ cells . One myeloid cell response that has recently been highlighted as important is proliferative capacity , and there remains a debate regarding whether this is helpful or harmful in response to injury or in progression of neurodegenerative diseases [33 , 34] . CAST . APP/PS1 mice showed the greatest numbers of myeloid cells around plaques despite having the fewest numbers of myeloid cells in WT animals ( Fig 6 ) . To determine whether this could be due to myeloid cell proliferation , the proliferative marker KI-67 was used . KI-67+IBA1+ cells in CAST . APP/PS1 mice were compared to B6 . APP/PS1 mice . There were significantly more plaque associated KI-67+IBA1+ cells observed in CAST . APP/PS1 compared to B6 . APP/PS1 mice ( t ( 14 ) = 3 . 73 , p = 0 . 002 ) ( Fig 7D ) . This suggests underlying differences in the proliferative capacity of myeloid cells between strains , and may be a factor in neuronal cell loss exhibited by CAST . APP/PS1 .
The work presented here highlights the value and power of increased genetic diversity within mouse models in order to gain insight into the complex etiologies of human disease . Our work shows that in contrast to the B6 . APP/PS1 strain that has been widely used historically , CAST . APP/PS1 , WSB . APP/PS1 and PWK . APP/PS1 represent models that provide a new lens to understanding central features of human AD including amyloid-induced neurodegeneration , neuroinflammation , cerebrovascular integrity and cerebral amyloid angiopathy . Overall , there were three major findings: ( 1 ) Female WSB . APP/PS1 showed significant hippocampal and cortical neuronal cell loss , whole brain elevated levels of Aβ42 , and cognitive impairment in a short-term memory task . This was accompanied by substantial vascular amyloid deposition in the form of cerebral amyloid angiopathy accompanied by vascular compromise . ( 2 ) CAST . APP/PS1 showed hippocampal cell loss and females exhibited whole brain elevated levels of Aβ42 . ( 3 ) Transcriptional profiling corroborated by neuropathology identified strain-dependent baseline differences in the expression of neuroinflammatory ( primarily myeloid-related ) genes and in the magnitude of their response to amyloid . Based on these findings , we predict that a major driver of the phenotype differences observed between strains ( e . g . neuronal cell loss and CAA ) is due to differences in neuroinflammation , particularly myeloid cells . A substantial part of the observed variation in myeloid cell-driven inflammation has been linked to genetic differences between human populations [35] . Wild-derived AD mouse models appear to show important strain- and sex-dependent differences in behavior and pathology that are similar to the human clinical population that show both sex-specific and ethnic differences in terms of prevalence and progression [36] . However , a major challenge of this study was to develop a functional battery that could be used across the strains as they exhibit formidable differences in wildness compared to classical laboratory strains . Wildness score is comprised of measurements of jumping , escape , struggle , squeaking and biting , and strains like B6 and D2 earn a score ranging between 0 . 21 and 0 . 66 , while wild-derived strains range from 1 . 35 for CAST [26] to 2 . 5 or greater for WSB [27] . While it was important to include a range of functional assays , we anticipated there could be issues as traditional behavioral assays have been primarily optimized for typically behaving mice ( e . g . young male B6 ) and likely would not be optimal for testing wild-derived strains . While B6 . APP/PS1 mice have previously been shown to exhibit deficits in assays such as Contextual Fear Conditioning as early as 6 months [37] , we chose to avoid aversive tasks due to the inherent difficulty in handling wild-derived mice and stress caused to the animals with repeated handling . Instead , we chose tasks that utilized the animal’s natural exploratory drive such as y-maze tasks that assess spatial memory . While spatial memory deficits in B6 or B6/C3H mice carrying the APP/PS1 transgenes are typically over 12 months of age [38 , 39] , it would be expected that earlier impairment would be identified in a sensitized genetic context . To our knowledge , this is the first time that many of the tasks included in our battery were used to assess wild-derived mouse behavior , thus , all data is left intact ( no outliers removed ) and presented as individual data points . Unfortunately , in this study , not all WT strains were able to perform the novel spatial recognition task , which may have been due to the length of the memory delay chosen ( 30 minutes ) . This meant that short-term memory could not be assessed for some APP/PS1 strains . Therefore , more extensive functional assays are still required at multiple ages to determine the utility of these strains for studies of cognitive impairment . Given our experiences in this study , it may be necessary to develop and validate strain-specific cognitive assays due to inherent differences in wildness and age-dependent cognitive abilities . This will be of particular importance in tasks that may require food restriction as these strains have vastly different metabolic rates . In the age window tested , there were significant differences in plaque numbers and Aβ42 levels between strains . Taken alone , plaque counts would suggest less amyloid in the wild-derived APP/PS1 mice in comparison with B6 . APP/PS1 . However , the size distribution of plaques varied across the strains , with B6 . APP/PS1 exhibiting many smaller proximal deposits that may correspond to initial seeding of Aβ . This is in contrast to plaques in the brains of wild-derived APP/PS1 mice that were of moderate size . This could be indicative of a more advanced stage of amyloid deposition in 8 month wild-derived mice carrying APP/PS1 in comparison with B6 . APP/PS1 of the same age; and/or , suggest the presence of different conformations of amyloid fibrils . Previous work has shown that identical peptide sequences are capable of forming into different conformations of amyloid fibrils , and that this difference can be detected by seeding efficiencies [40] . The development of cerebral amyloid angiopathy ( CAA ) in WSB . APP/PS1 coupled with fibrin leakage ( Fig 4 ) suggests compromised vascular integrity and/or deficits in amyloid clearance . It is possible that cerebrovascular damage ( measured here by fibrin leakage ) is downstream of amyloid . Conversely , an inherent weakness in cerebrovascular structures in WSB mice may dispose mice to CAA . While CAA has been reported many times before in AD models carrying APP mutations on a B6 background , typically it does not appear with complete banding until mice are over 14 months of age [41–43] . Furthermore , the severity of CAA and risk of associated microhemorrhage progresses with age . There is strong evidence to suggest that the earliest predictors of AD-susceptibility and onset are related to vascular and blood-brain-barrier integrity [44] . The presence of severe CAA and neurodegeneration in WSB . APP/PS1 will allow mechanistic dissection of the relationship between CAA and neurodegeneration . We found that strain is the greatest driver of gene expression variation in these mouse models , even more so than sex or APP/PS1 . This is representative of the inclusion of millions of genetic differences created from wild-derived strains that have never before been explored in context of modeling AD . Mus musculus , also known as the house mouse , are characterized as being commensal animals , meaning that they live in close association with humans , and even though they are able to adapt to a wide-range of environments , are dependent on human shelter or activity for their survival [15] . Each distinct subspecies is from different geographical regions ( CAST was trapped in Thailand , WSB was trapped in eastern shore Maryland , USA and PWK was trapped in the Czech Republic ) , and evolved separately to survive alongside humans in the face of similar region-specific pressures ( exposure to pathogens or infection , climate , diet etc . ) . Therefore , some of the genetic differences between mouse substrains may correspond with genetic variants in different populations of humans . More likely however , the variations driving the phenotype differences will impact similar genes/pathways that are modified by genetic risk variants in the human population . In support of this , many of the genes in the module identified by WGCNA ( Fig 5D ) have previously been implicated in human AD–including sporadic AD–either through genetic association , gene expression studies or functional studies . Therefore , despite the presence of the APP/PS1 transgene artificially driving amyloid accumulation in these strains–the responses appear to be directly relevant to human AD . This suggests that interventions tested in these new AD strains that target factors downstream of amyloid deposition but upstream of neurodegeneration would be expected to be clinically relevant to FAD and LOAD . PWK . APP/PS1 is particularly intriguing as transcriptional data suggest it is the greatest responder to amyloid at 8 months and appears to be a resilient strain ( no neuronal cell loss detected ) . These data may be consistent with a slower progression/transition from amyloid deposition to neuronal cell dysfunction which could become apparent at older ages , or representative of a neuroprotective signature . Two additional and striking phenotypes may also be reflective of the substantial neuroinflammation in PWK . APP/PS1 mice . First , during generation of the experimental cohort , PWK . APP/PS1 had to be separated from WT littermates at 3 months of age due to increased aggression . Second , changes in activity in the piezoelectric chambers were observed in female PWK . APP/PS1 ( S3 Fig ) . These may be behavioral manifestations of increased neuroinflammation in response to amyloid . Agitation and circadian disruption are clinical symptoms that directly interfere with the ability of caregiving to occur in the home and have both been linked with neuroinflammation in humans [45 , 46] . Transcriptional profiles in WT animals suggested that in comparison with B6 and PWK , CAST and WSB show lower baseline expression of the primarily myeloid-related genes in this module . Cell counts confirmed that there was ~50% reduction in the number of IBA1+ cells in both CAST and WSB . Natural inherent differences in neuroinflammation is important given the lack of studies into how genetic variation impacts glial cell development and homeostasis in the human population–an area that might be critical in predisposing to age-related diseases such as AD . Similarly , while there have been renewed efforts to characterize the immune systems in wild-caught mice [47] , there still remains a dearth of knowledge regarding how genetic variation impacts myeloid cell and astrocyte function in inbred wild-derived strains . This may be starting to change as recent work by Christopher Glass and colleagues [48] analyzed macrophages from five inbred mouse strains , including PWK . As in our study , strain was the greatest driver of differences in gene expression in these macrophages . Much of the foundation of mouse genetics has been focused on examination of a single genetic difference while holding all other genetic ( i . e . strain background ) and environmental influences constant . Somewhere along the way , limited resources and a wise desire for standardization restricted this examination to only one or two laboratory strains , despite efforts more than 4 decades ago to develop mouse resources such as the wild-derived strains and periodic suggestions of researchers past to expand beyond one strain [49–51] . Our study represents one of few studies to utilize natural genetic variation in mice to gain further insight in human AD . For the first time , we show neurodegeneration and mixed pathology in wild-derived strains carrying the APP/PS1 transgenes . Interestingly , our data suggests B6 is a ‘resilient’ strain when considering neurodegeneration . This ‘resilience’ may be specifically driven by differences in myeloid-related neuroinflammation , and we predict that differences in myeloid cell biology in these new wild-derived AD mouse models will provide a much-needed platform for identification of novel genes/variants modifying susceptibility to neuronal cell loss . One caveat of these new strains is that amyloid is driven by the APP/PS1 transgenes . Transgenic overexpression of proteins can provide additional side effects and mutations in APP and PSEN1 which may not be ideal to uncover the mechanisms of sporadic AD . A second caveat is that , despite neuronal cell loss , they appear to lack overt TAU pathology ( S5 Fig ) . Therefore , further work is still needed to improve both the construct validity and the face validity of these new mouse models . Research is now focused on inducing sporadic AD in mice in the absence of transgenic overexpression of familial AD mutations . Mice differ from humans in both the APP and TAU proteins . The human APP protein is generally considered to be more amyloidogenic than the mouse and the ratios of the 3R and 4R isoforms of TAU are balanced in human adults , but not in adult mice . This may be a contributing factor to the apparent lack of TAU pathology in wild-derived APP/PS1 strains . Multiple efforts , including our own , are improving the construct validity of AD mouse models by humanizing the App and Mapt loci and incorporating sporadic AD-relevant variants such as APOEE4 and TREM2R47H . The development of gene editing technologies such as CRISPR make these approaches feasible . However , our study and others [12] show it will be important that these efforts incorporate genetic diversity and natural genetic variation to improve both the face and predictive validity of these new mouse models .
All research was approved by the Institutional Animal Care and Use Committee ( IACUC ) at The Jackson Laboratory ( approval number 12005 ) . Animals were humanely euthanized with ketamine/xylazine mixture . Authors performed their work following guidelines established by the “The Eighth Edition of the Guide for the Care and Use of Laboratory Animals” and euthanasia using methods approved by the American Veterinary Medical Association . ” All mice were bred and housed in a 12/12 hours light/dark cycle on pine bedding and fed standard 6% LabDiet Chow . Experiments were performed on four strain genetic backgrounds: C57BL/6J , CAST/EiJ ( JAX Stock #000928 ) , WSB/EiJ ( JR#001145 ) , and PWK/PhJ ( JR#003715 ) . B6 . Cg-Tg ( APPswe , PSEN1dE9 ) 85Dbo/Mmjax ( JAX stock #005864 ) , and referred to in this study as B6 . APP/PS1 mice , were obtained from the Mutant Mouse Resource and Research Center ( MMRRC ) at The Jackson Laboratory and backcrossed with the three different mouse strains for at least 6 generations to produce: CAST . APP/PS1 ( JAX Stock #25973 ) , WSB . APP/PS1 ( JAX Stock #25970 ) and PWK . APP/PS1 ( JAX Stock #25971 ) . Generation of experimental cohorts consisted of 12 mice of each sex and genotype ( APP/PS1 carriers and littermate wild-type controls ) . Due to increased pup mortality in the wild-derived strains , once determined to be pregnant , female mice were removed from the mating and housed individually . During this time , they were also given BioServ Supreme Mini-treats ( Chocolate #F05472 or Very Berry Flavor #F05711 ) in order to discourage pup cannibalism . Animals were initially group-housed during aging and then individually housed at the start of the behavioral testing battery . Due to severe aggression in PWK . APP/PS1 mice , these mice were individually housed earlier at 3 months of age . There was also some cohort loss throughout the behavioral battery ( i . e . seizure lethality mainly in B6 . APP/PS1 ) , so individual data points are shown for all assays where appropriate . Due to a substantial loss in the first cohort of male CAST . APP/PS1 during the behavioral battery , a second cohort was generated and tested . Data were analyzed independently and combined if there were no significant differences in groups . For post-mortem characterization of AD phenotypes , brains from 6 males and 6 females at 8 months ( ±2 weeks ) were assessed , with the exception of female B6 . APP/PS1 , where only an n of 4 survived to harvest . A behavioral and physiological battery was designed in to order to test a wide range of AD-relevant phenotypes ( S1 Fig ) . All testing was conducted by trained technicians in the Center for Biometric Analysis at The Jackson Laboratory . APP/PS1 strains were scheduled as they became available and on average , the battery took about 6 weeks to complete . Testing always involved blinding and randomization of all littermates . An animal’s data was excluded from analysis if there was an indication from the technician that it should be ( i . e . due to animal escaping prior to placement in assay , equipment failure , etc . ) . Summary tables report n’s used in analyses and individual data points are shown in all plots . Animals were then directly taken from the facility to the laboratory for harvesting . Piezoelectric floor monitoring ( Signal Solutions , Lexington , KY ) is a non-invasive , high throughput method of assessing sleep patterns through measurement of breath rate to classify animals as either awake or asleep [52 , 53] . The piezoelectric pad is located on the cage floor and is sensitive to respiratory patterns . Pressure on these sensors converts analog input into an electric/digital signal . The system monitors activity by measuring the amplitude of the electrical signal , and comparing this to a signal threshold in order to classify the animal as either awake or asleep . Data was exported as either hourly percent activity or as hourly activity bout length over 5 days . Activity for the first day was excluded to allow for animal acclimation , and then averaged across sex and genotype . Stress induced Hyperthermia is an assay developed to detect typical stress responses determined by elevation in body temperature as the sympathetic nervous system is activated . Disruptions in this response can be indicative of metabolic dysfunction and/or an anxiety phenotype . The day prior to testing , mice were individually housed in standard cages . On testing day , animals were brought into the testing room and allowed to habituate for 60 minutes . Body temperature was taken at two time points separated by a 10-minute delay via a glycerol lubricated thermistor rectal probe ( Braintree Scientific ) . In between readings , mice were placed back into the home cage . Open field is a measure of exploration and motor activity . Introduction of APP/PS1 on the B6 background has been correlated with hyperactivity , thus , this is an important measure for the battery to ensure that strain-specific differences in other tasks cannot be accounted for by hyperactivity alone . The apparatus used for this test was a square chamber ( ~40 x 40 x 40 cm ) fabricated from clear Plexiglas and illuminated at 400 lux . Data are recorded via a sensitive infrared photobeam system in 5-minute time bins for a 60-minute trial length . Spontaneous Alternation Y-maze task is a widely used task to assess spatial working memory , and relies on the animal’s natural exploratory behavior [54] . Sequence of entries into each arm of the Y-maze is tracked to assess if animals demonstrate intact working memory . The maze is a y-shaped arena constructed of Plexigas with equal arm lengths ( ~30 cm ) , arm lane width ( ~6 cm ) and wall height ( ~15 cm ) . Special arm covers were fabricated to ensure that wild-derived mice could not escape and were used for all strains . A black curtain surrounded the perimeter of the maze in order to minimize additional room cues . A camera mounted above recorded mouse exploration and tracking software ( Noldus Ethovision ) allowed the export of multiple variables such as sequence of arm entries . Mice were allowed to habituate to the testing room for 60 minutes prior to testing . Activity was recorded over an 8-minute period . Each maze was wiped out with 70% ethanol between animals . A correct alternation represented when animals entered three different arms of the Y-maze without returning to a previously visited arm . The initial two arm entries were subtracted from the total to account for the placement of the animal in arm A at the start of the trial . Percent correct was determined by dividing the number of correct alternations by the adjusted total arm entries throughout the trial . This means for our assay , performance at chance is calculated at 22% . Typical performance of a young B6 male is approximately 50% [55] . Spatial working memory y-maze task was conducted at least 2 weeks after spontaneous alternation in the same y-maze arena . This task consisted of two trials separated by a 30-minute delay period . During trial 1 , the start and familiar arms were available for the animals to explore for 10-minutes and featured two distinct intramaze cues . Animals were then returned to their home cage for the 30-minute delay . In trial 2 , all three arms were open for exploration and time spent in each of the arms over the 5-minute period was calculated . An animal was determined to have intact short term memory if it spent significantly more time in the novel arm in comparison with the start and familiar arms . Between trials and between animals the maze was cleaned with 70% ethanol . An additional exclusion criteria for this task was exclusion for failure to explore both arms during trial one ( ≤20% time percentage per arm ) . Body composition measurements were collected at the conclusion of the battery if a significant weight difference was detected . Animals were weighed and placed into a Plexiglas tube of 2 . 5 inches diameter and 8 inches in length . This tube is placed in a nuclear magnetic resonance device ( EchoMRI , Houston , TX ) that uses a 5 gauss magnet in order to pulse a magnetic field in a gradient across the animal to determine body composition consisting of lean muscle , fat and water . Each scan lasts approximately 1–3 minutes . Upon conclusion , animals are returned to their home cage and the tube is cleaned with 70% ethanol . A lethal dose of ketamine/xylazine was administered to mice by intraperitoneal injection , in accordance to IACUC protocols . After transcardial perfusion with 1X PBS ( Phosphate buffered saline ) brains were removed . The left hemisphere was snap frozen for RNA/protein isolation , and the right hemisphere was fixed in 4% paraformaldehyde for sectioning . Protein was extracted with Trizol Reagent ( Life Technologies cat#15596–018 ) following manufacturer's guidelines . Pellets were resuspended in a solution of 1:1 8M urea and 1% SDS . For tissue sectioning , following 48 hours in 4% paraformaldehyde , half brains were kept at 4°C and placed in 10% sucrose for 24 hours . The tissue was then placed in 30% sucrose for an additional 24 hours , or until it sank . Brains were then embedded in optimal cutting temperature ( OCT ) compound , sectioned at 25μm and stored at −80°C until required . Primary antibodies were applied to 1xPBS washed brain sections and incubated for two nights at 4°C . The following primary antibodies were used to characterize neuronal and glial cells phenotypes in the brain: rabbit polyclonal anti-NeuN ( 1:200 , Cell signaling ) , chicken polyclonal anti-GFAP ( 1:200 , Acris Antibodies ) , rabbit polyclonal anti-IBA1 ( 1:200 , Wako ) , and mouse polyclonal anti-Ki-67 ( 1:200 , eBioscience ) . Primary antibodies were diluted in PBT ( 1X PBS , 1% TritonX-100 ) containing 10% normal donkey serum . After primary incubation , sections were washed three times in PBT and incubated with their respective secondary antibody ( donkey anti-chicken Alexa Fluor 633 or donkey anti-rabbit Alexa Fluor 488/594 , 1:1000 dilution , Life Technologies ) for 2 hours at room temperature . All sections were then counterstained with DAPI and mounted with Aqua PolyMount . For Thioflavin S staining , sections were incubated with 1% Thioflavin S ( diluted in a 1:1 water: ethanol ratio ) for eight minutes at room temperature , followed by three washes in 80% ethanol , 95% ethanol , and finally in dH2O , and mounted . For assessment of cerebral amyloid angiopathy , X34 ( 100 uM , Sigma Aldrich ) occurred first . Slides were brought to room temperature , washed with 1XPBS for 5 minutes and then incubated with 500 ul of the X34 solution . Slides were then dipped in deionized water and incubated with 500 ul of 0 . 02M NaOH for 5 minutes . After an additional wash with 1XPBS , primary antibodies rabbit polyclonal anti-fibrin ( 1:200 , Millipore ) and goat polyclonal anti-IBA1 ( 1:300 , Abcam ) were diluted in PBT and applied to the slide . The remainder of the protocol overlaps with that described above , with the exception of DAPI staining . For assessment of tau , X34 steps occurred first , followed by the use of Mouse on Mouse Basic Kit ( Vector Laboratories ) . Tissue was then incubated ON in primary antibody Mouse Phospho-Tau ( Ser202 , Thr205 ) monoclonal antibody ( AT8 , 1:250 , Thermofisher ) , goat anti-IBA1 ( same as above ) and rabbit Anti-NEUN ( Abcam , 1:500 ) diluted in 10% normal donkey serum and PBT . Secondaries used overlap with description above . To ensure AT8 staining worked , a positive control slide of a 13 month hTau ( B6 . Cg-Mapttm1 ( EGFP ) Klt Tg ( MAPT ) 8cPdav/J , JR# 005491 ) was stained alongside APP/PS1 strains . Images of IHC were taken using either the Leica SP5 confocal microscope , Leica SP8 confocal microscope or the Zeiss Axio Imager Z2 . Quantitative analyses of plaques , microglial cells , astrocytes and neurons were performed in WT and APP/PS1 mice of each of the four strains . The number of plaques present in the entire cortical region from three central sections for each mouse was determined . To quantify the number of IBA1+ microglia and GFAP+ astrocytes , 12 images ( 20X , 1388 X 1040 microns ) were taken for each brain ( for cortex: 3 images/section for 3 sections– 9 images in total; for hippocampus: 1 image in CA1/section for 3 sections– 3 images in total ) with a Zeiss Axio Imager fluorescent microscope , and cells were manually counted using the cell counter plugin from ImageJ ( 1 . 47d ) software . For counting NEUN+/DAPI neurons in the parietal cortex , three images ( 20X , 447 X 335 microns ) were randomly taken in similar areas for each brain from each mouse , images were stacked using ImageJ and cropped altogether to 274 . 13 X 225 . 75 microns ( including only cortical layers II and III ) . For quantification of pyramidal neurons in the hippocampus , images of the CA1 region were taken at 20X ( 447 X 335 microns ) and cropped to 225 . 75 X 129 . 00 microns . NEUN+/DAPI+ cells in the cortex and hippocampus images were manually counted with the cell counter plugin from ImageJ ( 1 . 47d ) software . All image analyses were performed blind to the experimental conditions . For IBA1+ cells surrounding plaques , five plaques per brain were imaged ( using 20x optical lens ) . Images were processed and cells counted using the cell counter plugin for ImageJ/FIJI . For each mouse , IBA1+ cells around each plaque from the three images were totaled and then averaged across mice . Protein levels of human APP were assessed using western blotting . Briefly , DC assay ( Bio-Rad ) was used to determine protein concentration . Protein samples were heated to 95°C for 5 minutes and a total of 10 ng of protein was loaded onto a 12% TGX stain-free gel ( Bio-Rad ) . Protein samples were transferred to a nitrocellulose membrane ( Life Technologies ) using an iBlot ( Thermo Fischer Scientific ) following manufacturers’ instructions . Blots were then incubated overnight with 6E10 antibody ( 1:2000 , Covance/BioLegend ) and 5% milk/PBS-tween at 4°C . After incubation with the appropriate secondary antibodies ( Anti-Mouse IgG 1:30 , 000 , Millipore ) for 2hrs at room temperature , ECL detection reagents ( GE Healthcare ) were used to develop the chemiluminescence signal . Blots were further probed with anti-GAPDH ( 1:1000 , Millipore ) after treatment with 0 . 1% sodium azide and incubation with secondary antibody ( Anti-mouse IgG 1:30 , 000 , Millipore ) for 2 hours at room temperature , washed , and detected . Quantification of blots was determined using Fiji ImageJ software . Human amyloid β-42 ( Aβ42 ) levels were determined using the ELISA detection kit from Life Technologies ( cat#KHB3442 ) following the manufacturer’s instructions . To ensure that urea and SDS levels in the protein samples were compatible with the ELISA kit ( see protein isolation ) , protein samples from 8 months old mouse brains were diluted 1:50 in standard diluent buffer . Samples were then compared to a standard curve and Aβ42 concentrations were established against the samples’ protein concentrations following manufacturers recommendations . Left brain hemispheres ( n = 93 ) were snap frozen at harvest and then samples corresponding to mice for pathological assessment were sent to The Jackson Laboratory Genome Technologies core for further processing . RNA was isolated from tissue using the MagMAX mirVana Total RNA Isolation Kit ( ThermoFisher ) and the KingFisher Flex purification system ( ThermoFisher ) . Tissues were lysed and homogenized in TRIzol Reagent ( ThermoFisher ) . After the addition of chloroform , the RNA-containing aqueous layer was removed for RNA isolation according to the manufacturer’s protocol , beginning with the RNA bead binding step . RNA concentration and quality were assessed using the Nanodrop 2000 spectrophotometer ( Thermo Scientific ) and the RNA Total RNA Nano assay ( Agilent Technologies ) . Libraries were prepared by the Genome Technologies core facility at The Jackson Laboratory using the KAPA mRNA HyperPrep Kit ( KAPA Biosystems ) , according to the manufacturer’s instructions . Briefly , the protocol entails isolation of polyA containing mRNA using oligo-dT magnetic beads , RNA fragmentation , first and second strand cDNA synthesis , ligation of Illumina-specific adapters containing a unique barcode sequence for each library , and PCR amplification . Libraries were checked for quality and concentration using the D5000 assay on the TapeStation ( Agilent Technologies ) and quantitative PCR ( KAPA Biosystems ) , according to the manufacturers’ instructions . Libraries were pooled and sequenced by the Genome Technologies core facility at The Jackson Laboratory , 100 bp paired-end on the HiSeq 4000 ( Illumina ) using HiSeq 3000/4000 SBS Kit reagents ( Illumina ) at an average sequencing depth of ~100 million reads per sample ( S1 Fig ) . 2x100 base length paired end reads were quality trimmed and filtered using Trimmomatic tool [56] and reads passing the quality filtering were mapped to the mouse mm10 reference genome using ‘STAR’ aligner [57] . Custom genomes were generated for CAST , PWK and WSB strains by incorporating REL-1505 variants into the reference genome [58] . RSEM software package [59] was used to estimate expression levels for all genes in Transcripts Per Million ( TPM ) unit based on Ensembl Release 84 transcriptome . HTSeq Python package was used to calculate raw read counts per transcript . Genes that have an HTSeq estimated raw read count of less than 10 in more than 90% of the samples were considered noise and excluded from the analysis . Downstream analyses were performed on 17408 genes that passed the read count threshold . We applied variance stabilization transformation ( vst ) on raw read counts using DESeq2 R package . Principal Component Analysis ( PCA ) was applied on vst transformed read counts to identify clusters of samples and any potential outliers . We then applied Weighted Gene Co-expression Network Analysis ( WGCNA ) algorithm [60] to identify co-expressed gene modules in our dataset . We extracted module’s eigengenes , which are equivalent to the first principal component , to represent the overall expression profiles of the modules . GO pathway enrichment analysis was performed using Homer tool [61] . For behavioral analyses , groups of 12 mice per sex per strain per genotype allowed us to detect effects of greater than 1 . 15 standard errors at 80% power . Due to some premature death , not all groups contained 12 mice but sample sizes are stated clearly in the associated tables and all graphs show individual data points . For neuropathology and biochemistry , six biological replicates were assessed for all groups with the exception of B6 female APP/PS1 ( n = 4 ) and PWK female WT ( n = 5 ) . Individual data points are shown on all graphs and associated tables include sample sizes . For data within strain and sex were analyzed as non-parametric using Mann-Whitney Rank Sum Test to account for departure for normality or using unpaired t-tests . One-way multifactorial analysis variance ( ANOVA ) followed by Bonferroni post-hoc tests for multiple comparisons were utilized for across strain differences . All statistical tests are labeled within data tables . Data were analyzed using GraphPad Prism software . P values are provided as stated by GraphPad and significance was determined with P values less than 0 . 05 . When significance values were more than two decimal values , P values were presented as follows: p ≤ 0 . 01 ( ** ) , p ≤ 0 . 001 ( *** ) or p ≤ 0 . 0001 ( **** ) . These new strains are available through The Jackson Laboratory and all associated data is being made available through GEO and Accelerating Medicines Partnership-Alzheimer’s disease ( AMP-AD ) knowledge portal . | Despite the rise in incidence of Alzheimer’s disease ( AD ) , it has been over a decade since a new drug treatment has been introduced . Recently , a number of pharmaceutical giants have shut down their AD research units . One issue that these companies and researchers have struggled with is the lack of translatability of preclinical studies to the clinic . One aspect that has come under heavy scrutiny is whether the mouse can be an appropriate model for a complex human disease such as AD . Current mouse models of AD have incorporated well-known early onset AD mutations on a single genetic background , C57BL/6J , which does not develop all features of human AD- namely marked neurodegeneration . Here we sought to improve the utility and translatability of mouse models through the use of three genetically distinct , wild-derived inbred mouse strains , CAST/EiJ , WSB/EiJ and PWK/PhJ . These mice encompass millions of genetic differences that have never before been explored in the context of modeling AD . Wild-derived mice that carried the early onset AD mutations exhibited robust differences in immune response to amyloid , evidence of mixed pathology and early neurodegeneration , better recapitulating what happens in human AD than previous models . | [
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] | 2019 | Enhancing face validity of mouse models of Alzheimer’s disease with natural genetic variation |
Hsp100 family chaperones of microorganisms and plants cooperate with the Hsp70/Hsp40/NEF system to resolubilize and reactivate stress-denatured proteins . In yeast this machinery also promotes propagation of prions by fragmenting prion polymers . We previously showed the bacterial Hsp100 machinery cooperates with the yeast Hsp40 Ydj1 to support yeast thermotolerance and with the yeast Hsp40 Sis1 to propagate [PSI+] prions . Here we find these Hsp40s similarly directed specific activities of the yeast Hsp104-based machinery . By assessing the ability of Ydj1-Sis1 hybrid proteins to complement Ydj1 and Sis1 functions we show their C-terminal substrate-binding domains determined distinctions in these and other cellular functions of Ydj1 and Sis1 . We find propagation of [URE3] prions was acutely sensitive to alterations in Sis1 activity , while that of [PIN+] prions was less sensitive than [URE3] , but more sensitive than [PSI+] . These findings support the ideas that overexpressing Ydj1 cures [URE3] by competing with Sis1 for interaction with the Hsp104-based disaggregation machine , and that different prions rely differently on activity of this machinery , which can explain the various ways they respond to alterations in chaperone function .
The protein disaggregation machinery of microorganisms and plants is driven by an Hsp100-family chaperone that cooperates with Hsp70 and its nucleotide exchange factor ( NEF ) and J- protein ( Hsp40 ) co-chaperones [1] . This machinery promotes cell survival after environmental stresses that cause proteins to aggregate by extracting proteins individually from aggregates [2]–[4] . Organisms encode multiple Hsp70s , Hsp40s and NEFs and there is much to learn about how these chaperones cooperate and regulate each other's activity to effect protein remodeling and reactivation , and how different combinations of chaperones and co-chaperones determine efficiency and specificity of the machinery . In yeast , this Hsp104-based resolubilization process also targets prions , which are cellular proteins that propagate as highly structured fibrous protein aggregates called amyloid [5]–[7] . The widely studied prions [URE3] , [PSI+] and [PIN+] ( also known as [RNQ1+] ) are composed of the proteins Ure2 , Sup35 , and Rnq1 , respectively [8]–[10] . Propagation of these and other amyloid-based yeast prions requires proper functioning of the disaggregation machinery [5] , [11]–[15] , which promotes prion replication by fragmenting fibers into more numerous pieces , or seeds , that continue to propagate the prion state [4] , [16] , [17] . Hsp70s act in various cellular chaperone machines by binding and releasing hydrophobic surfaces on partially folded proteins . This activity is necessary for essential cellular processes where proteins are partially folded , such as transport across membranes , and for preventing protein aggregation under conditions of stress [18] , [19] . Effective interactions of Hsp70 with substrates rely on its regulation by J-proteins and NEFs ( reviewed in [20] ) . The major yeast cytosolic Hsp40s Sis1 and Ydj1 are structurally related J-proteins that function as dimers [21] . Both have an amino-terminal J domain that mediates physical interaction with Hsp70s and an adjacent glycine-phenylalanine ( GF ) rich region that confers some functional distinction [22] , [23] . Both also have carboxy-terminal regions that bind substrates with a specificity that overlaps Hsp70 [24]–[26] . The class I J-protein Ydj1 has a zinc-finger element within its C-terminal region and a CAAX motif at its extreme C-terminus that directs its farnesylation . This modification localizes much of Ydj1 to membranes and influences cooperation of Ydj1 with Hsp90 , another abundant and highly conserved protein chaperone [27] , [28] . The class II J-protein Sis1 lacks these elements , but has a glycine-methionine ( GM ) rich extension of its GF domain . Altering function or abundance of Sis1 or Ydj1 inhibits propagation of prions , but in different ways . By an undefined mechanism , overexpressing Ydj1 causes cells to lose [URE3] and some variants of [PIN+] , but not [PSI+] [12] , [29] . Increasing expression of Sis1 does not destabilize these prions [30] , [31] . Depleting Sis1 causes [URE3] and [PIN+] to be lost rapidly as cells divide , but causes [PSI+] to be lost gradually and only after a long delay [14] . Additionally , all non-essential functions of Sis1 are dispensable for [PSI+] propagation , but deleting only the GF region of Sis1 causes cells to lose [PIN+] [30] , [32] . Thus , the way these Hsp40s influence prion propagation goes beyond their general roles of regulating Hsp70 . Additionally , when cooperating with E . coli disaggregation machinery in yeast , Sis1 is specifically required for prion propagation and Ydj1 for protecting cells from exposure to lethal heat ( thermotolerance ) [15] . Both Hsp40s are critical for survival and while no other J-protein can compensate for loss of Sis1 , Sis1 and other J-proteins , as well as J-domains alone , can improve growth of cells lacking Ydj1 [14] , [27] , [33] , [34] . What defines these functional differences of Sis1 and Ydj1 is uncertain . Here , we used Sis1-Ydj1 hybrid proteins to identify structural elements that determine the distinct functions of Sis1 and Ydj1 in various cellular processes and systematically assessed the importance of Sis1 activities for propagation of [URE3] and [PIN+] . We found that the C-terminal regions of Sis1 and Ydj1 possessed functional distinctions that directed the action of the Hsp104 machinery in prion propagation and thermotolerance , and the Hsp90 machinery in galactose-induced transcription . We also found that [URE3] was highly sensitive to alterations of Sis1 and that [PIN+] was less dependent on Sis1 than [URE3] , but more dependent than [PSI+] . Our results support the idea that differences in ways prions respond to various chaperone alterations can be due to differences in their dependence on the disaggregation machinery .
Earlier we showed that the E . coli disaggregation machinery ( ClpB , DnaK and GrpE , which are analogous to yeast Hsp104 , Hsp70 , and NEF , respectively ) function in yeast by cooperating with yeast Hsp40s [15] . ClpB , DnaK and GrpE are herein abbreviated B , K and E , respectively . We modified this system to use a DnaK mutant ( R167H , designated K* ) that can interact only with J-proteins that have the compensatory D36N J-domain mutation ( designated Sis1* and Ydj1* ) . BK*E cannot cooperate with wild type J-proteins , so we can monitor interactions of the BK*E machinery specifically with Sis1* or Ydj1* even in the presence of their wild type counterparts . Using this system we showed BK*E cooperates specifically with Sis1* to propagate [PSI+] prions and with Ydj1* to protect cells from exposure to lethal heat ( thermotolerance ) . The ability of Sis1 and Ydj1 to direct activities of the disaggregation machinery could be due to differences in the ways they recruit or regulate Hsp70 components of the machinery or interact with different types of substrates . To determine the basis of these and other functional differences we used Sis1-Ydj1 hybrid proteins . In earlier work using hybrids of Ydj1 and Sis1 each of the CTDs was divided into two parts ( CTDI and CTDII ) and the adjacent GM region of Sis1 was included on the same fragment containing the amino-terminal portion of the Sis1 CTD [35] , [36] . Exchanging this fragment splits the contiguous functionally redundant GF-GM regions of Sis1 [22] , which complicates interpretations of swapping GF domains . In order to simplify comparisons we exchanged only the three most conserved J , GF ( GM ) , and CTD regions to form six hybrid proteins ( see Figure 1A , Materials and methods ) . Hybrids are named according to their structural components . For example , YYS and SSY have their CTDs swapped . All of our J-protein hybrids for the BK*E experiments contain the D36N mutation , which is indicated in their names by an asterisk ( e . g . Y*YS is the D36N mutant of YYS ) . To test for ability of these modified Sis1-Ydj1 hybrid proteins to cooperate with the E . coli chaperones to propagate [PSI+] , they were first expressed in [PSI+] cells that have ClpB in place of chromosomal Hsp104 and express K* , E and Hsp104 from plasmids . They were then assessed for ability to continue propagating [PSI+] after the cells lose the plasmid encoding Hsp104 . The results are presented in Figure 1B . The upper panel shows cells on medium that allows growth of all strains and the lower panel shows medium that allows growth only of cells propagating [PSI+] . The lack of distinction among strains in the upper panel indicates that in the absence of selection [PSI+] propagates too weakly to confer an obvious phenotype , as reported earlier [15] . Nevertheless , the strong confluent growth of cells transferred onto medium selecting for [PSI+] indicates the prions in these cells are mitotically stable . The presence of [PSI+] was confirmed by its dominant phenotype in crosses and by guanidine curability ( see Figure S1A ) . Wild type Sis1 and all hybrids containing the CTD of Sis1 propagated [PSI+] . In contrast , Ydj1 and all proteins with the CTD of Ydj1 were unable to propagate [PSI+] . Thus , the Sis1-specific function that is necessary for these full-length Hsp40s to cooperate with the bacterial ClpB disaggregation machinery to propagate [PSI+] prions resides in the Sis1 CTD . These results were somewhat unexpected because Sis1 lacking its CTD ( Sis1ΔCTD ) propagates [PSI+] in cells expressing Hsp104 [32] . We therefore tested if BK*E could cooperate with the truncated Sis1*ΔCTD to propagate [PSI+] . We found it did , but [PSI+] was very unstable and was lost rapidly when selection for the prion was not maintained ( Figure 1C ) . These results indicate that Hsp40 requirements of BK*E for [PSI+] propagation differ when full-length and truncated Hsp40s are used . Inconsistencies between truncated and full-length Hsp40s have been seen before ( see below ) [22] , [30] , [33] . Because the dimerization domain is at the C-terminus , it was also possible that Y*YS was able to propagate [PSI+] only by combining with the wild type Sis1 present in the cells to form heterodimers that functioned with BK*E . Since Sis1 lacking its dimerization domain ( Sis1ΔD ) propagates [PSI+] in cells with Hsp104 [32] , we tested if the D36N version of Sis1ΔD was able to support [PSI+] propagation with BK*E . At the same time we tested a version of Y*YS lacking its dimerization domain . Both of these proteins were able to cooperate with BK*E to propagate [PSI+] , but with noticeably reduced efficiency compared with their full-length counterparts ( Figure 1C , note slower growth and pink color on medium selecting for [PSI+] ) . Thus , Y*YS did not need to dimerize with wild type Sis1 to support propagation of [PSI+] . Using our hybrids to assess Ydj1 function in thermotolerance we found that , except for Y*SY , proteins containing the CTD of Ydj1 worked better at restoring thermotolerance to hsp104Δ cells expressing BK*E ( Figure 1D ) . Overall , the range of survival conferred by the different hybrids was somewhat broad , which suggests that complex interactions among different parts of the proteins can influence Hsp40 functions in this process . Notably , however , S*SY , in which only the CTD is from Ydj1 , functioned most like Ydj1 in this assay , while Y*YS performed only slightly better than Sis1* . Thus , as with prion propagation , a determinant of functional specificity between Sis1 and Ydj1 that directs BK*E activity in thermotolerance resides in the CTD . Although Sis1 is essential for viability , its J , GF and CTD regions are each dispensable for growth of sis1Δ cells [22] , [30] . However , other work shows that a full-length Sis1-Ydj1 hybrid that contains the substrate-binding region of Sis1 , but not Ydj1 , supports growth of sis1Δ cells [35] . These earlier findings show inconsistencies in the way that Sis1 and Ydj1 sub-regions determine specific Hsp40 functions . The variable dependency of [PSI+] on the CTD of Sis1 in our full-length versus truncated [32] proteins is another example of such differences . An obvious distinction in these experiments is whether the structural regions are deleted or swapped . While deleting regions allows identifying redundant functions , swapping domains of full-length proteins also allows identifying functions that can be influenced by inter-domain interactions , or if the domains specify interactions with other factors or localization of the J-proteins in the cell . In line with the earlier findings using full-length hybrid proteins , our results with the E . coli chaperones show the CTDs can impart specific functionality to these Hsp40s . To assess which regions are involved in determining specificity of cooperation with the yeast chaperone machinery we tested ability of our hybrids to function in place of Sis1 or Ydj1 , which allows investigation of Hsp40 functions other than those needed for prion propagation and thermotolerance . We compared the relative abundance of the hybrid proteins using western blot analysis ( Figure S2 ) . Sis1 and hybrid proteins containing the CTD of Sis1 were less abundant than the others , so we cannot rule out that differences in abundance were contributing to effects in some assays . In addressing this issue by increasing expression using the stronger GPD promoter on both single and high-copy plasmids , we found that hybrid proteins containing the CTD of Sis1 caused growth inhibition of ydj1Δ cells in a dose-dependent manner ( see Figure S3 ) . Therefore , in our complementation assays we expressed proteins regulated by the SIS1 promoter on single-copy plasmids . Despite differences in protein levels , in several experiments the less abundant proteins ( i . e . those with CTD of Sis1 ) functioned better than the others ( see results above and below ) . Therefore , differences in ability of the hybrids to complement functions cannot be explained solely by differences in protein levels . To determine if individual Sis1 sub-regions within full-length proteins are enough to support growth of sis1Δ cells , both [PSI+] and [psi−] versions of strain 930 ( sis1Δ carrying wild type SIS1 on a URA3 plasmid ) were transformed with plasmids encoding the hybrid proteins and then grown as patches on medium that allows cells to lose the URA3 plasmid encoding wild type Sis1 . These were then replica-plated onto medium containing FOA to select for cells having lost that plasmid . Regardless of prion status , all hybrids that contained the Sis1 CTD , and only these hybrids , supported growth ( Figure 2A , left panels ) . Therefore , in the context of our full-length hybrids , the CTD of Sis1 was necessary and sufficient to provide essential Sis1 activity . These same hybrids supported propagation of [PSI+] , which is consistent with our earlier findings that propagation of [PSI+] is minimally dependent on Sis1 and that any Sis1 mutant that supports growth also supports [PSI+] [32] . As expected , these same hybrids supported growth of isogenic sis1Δ strain 1385 ( used to monitor [URE3] ) . However , only those containing both the CTD and GF regions of Sis1 ( i . e . Sis1 and YSS ) supported propagation of [URE3] ( Figure 2B ) . Thus , propagation of [URE3] depended on the GF/GM and CTD regions of Sis1 , but not on the Sis1 J-domain . The Ydj1 GF region did not function in place of Sis1 GF/GM ( i . e . in SYS ) to support prion propagation . These latter observations are reminiscent of earlier work showing that a short stretch of the GF region in Sis1 , which is absent in Ydj1 , is important for propagation of [PIN+] [23] , and they suggest the same function is important for propagation of [URE3] . Aside from its role in protecting cells from exposure to lethal heat , Ydj1 is important for cell growth under all conditions [27] . Cells lacking Ydj1 are viable , but they grow very slowly at 25°C and do not grow at 34°C . Elevating expression of Sis1 and other J-proteins , or even J-domains alone , can improve growth of ydj1Δ cells [14] , [34] , which suggests that the functions of Ydj1 in its roles important for viability are more general . Nevertheless , we tested if distinct domains of Sis1 and Ydj1 conferred Ydj1-specific functions important for growth by repeating the plasmid shuffle using ydj1Δ strain MR502 , which has YDJ1 on a URA3 plasmid . We found that all hybrid proteins containing the CTD of Ydj1 restored growth noticeably , even at 34°C ( Figure 3A ) . The substantial ability of YSY and SSY to restore growth indicates that the Ydj1 GF region is effectively dispensable for its functions in cell growth and its J-domain has a small contribution . Compared with the empty vector , Sis1 also improved growth weakly at 30°C , which is in line with earlier data showing increased expression of Sis1 compensates for loss of Ydj1 [27] , but it did not support growth at 34°C ( Figure 3A ) . Cells with YSS grew slightly better than those with the empty vector at 25°C and 30°C , but the other hybrids containing the Sis1 CTD failed to improve growth , even at 25°C . These results suggest that in the context of our full-length proteins the CTD of Ydj1 possesses Ydj1-specific functions important for growth . As indicated above , increasing expression of proteins with the CTD of Sis1 inhibited growth of ydj1Δ cells in a dose-dependent manner ( Figure S3 ) . One explanation for this effect is that hybrids with the Sis1 CTD were able to form defective heterodimers with wild type Sis1 and a resulting impairment of Sis1 function would exacerbate the growth defect caused by lack of Ydj1 . To test this possibility , we repeated the experiments using hybrids lacking the dimerization domain ( ΔD ) . Even though the abundance of these truncated proteins was at the low level of their counterparts ( Figure S2C ) , YSSΔD improved growth considerably at 30°C and allowed weak growth at 34°C ( Figure 3B ) . SYSΔD and YYSΔD also supported growth at 30°C , but only slightly . These results are consistent with the growth inhibition being caused by dominant inactivation of wild type Sis1 and suggest that lack of complementation was not necessarily due to lower protein abundance . Deleting the dimerization domain of wild type Sis1 also improved its ability to suppress the ydj1Δ defect , although not enough to support growth at 34°C . Thus , monomeric Sis1 is better at performing Ydj1-specific functions than Sis1 dimers . Together these results agree with earlier work showing a general ability of truncated J-proteins to complement Ydj1 function better than intact proteins [34] . They also suggest dimerization might specify or restrict Hsp40 activities . Ydj1 is also a critical component of the Hsp90 molecular chaperone system necessary for activating galactose-inducible gene promoters [37] . This machinery is thought to remove nucleosomes from the promoter region to allow access to transcription factors . Deleting YDJ1 abolishes galactose induction by disrupting this process . When assessed for ability to function in galactose induction ( Figure 4 ) , all of the hybrids that had the CTD of Ydj1 , and only these hybrids , restored GAL expression . Thus , the CTD of Ydj1 determined specificity of Ydj1 in a process that involves its cooperation with the Hsp90 machinery . We next tested whether the ability of the Hsp40s to exhibit functional discrimination in vivo was reflected in discrimination in vitro . To do this we monitored protein reactivation of two different substrates . When heat-inactivated GFP-38 , a GFP fusion protein containing a C-terminal 38 amino acid peptide , was used as the substrate , Sis1 in combination with Hsp104 and Ssa1 restored about 30% of the GFP-38 after an hour ( Figure 5A ) . With Ydj1 in place of Sis1 in the reaction , there was little reactivation ( <2% ) . The hybrid protein containing the CTD of Sis1 , YYS , was able to reactivate GFP-38 with Hsp104 and Ssa1 , but the rate of reactivation was ∼50% that of Sis1 ( Figure 5A and B ) . There was no detectable reactivation of GFP-38 by SSY under the same conditions . These results show that reactivation of GFP-38 by Hsp104 and Ssa1 requires a Sis1-specific function and that the CTD of Sis1 , when appended to the J-GF of Ydj1 , was sufficient to provide this function . In contrast , with heat-inactivated luciferase as substrate , Ydj1 in combination with Ssa1 promoted reactivation and Sis1 with Ssa1 was inactive ( Figure 5C ) . SSY was as active as Ydj1 in reactivating luciferase with Ssa1 . YYS in combination with Ssa1 was unable to reactivate heat-denatured luciferase . Together these results show that Sis1 and Ydj1 discriminate between protein aggregates and discrimination is a function of the Sis1 and Ydj1 CTDs . The extent that [URE3] depends on Sis1 has not been evaluated systematically . We monitored [URE3] in sis1Δ strain 1385 , which carries a URA3-based plasmid encoding Sis1 to support viability . We expressed previously described versions of Sis1 engineered to contain deletions or point mutations from a TRP1-based plasmid [32] . Deletions remove defined structural domains , the H34Q substitution in a conserved histidine-proline-aspartate ( HPD ) motif comprising residues 34–36 in the J-domain disrupts a critical interaction with Hsp70 [38] , [39] , and the K199A substitution in the CTD disrupts substrate binding [40] ( see Figure 6A ) . To assess evolutionary conservation of Hsp40 function we also included the human Sis1 homolog DnaJB1 ( also known as Hdj1 ) . When expressed in place of Sis1 , DnaJB1 supports cell viability and propagation of certain variants of [PIN+] and [PSI+] [23] , [32] , [41] , [42] . Depleting functional Ure2 into [URE3] prion aggregates makes our strains grow slowly [15] , which is evident when comparing sizes of [ure-o] and [URE3] colonies ( see Figure 6B ) . When Sis1 proteins lacking the GF region or containing the H34Q point mutation were expressed with wild type Sis1 they had obvious dominant inhibitory effects on [URE3] , seen as appearance of red [ure-o] colonies ( Figure 6B ) . Because the H34Q mutation disrupts physical interaction of J-proteins with Hsp70 , the dominant inhibition of [URE3] propagation by the H34Q mutant might be caused by its forming defective hetero-dimers with wild type Sis1 or by competing with Sis1 for substrate . To test these possibilities , we combined H34Q with alterations that interfere with ability of Sis1 to dimerize ( ΔD ) or to bind substrate ( K199A ) . Both mutations reduced the dominant anti-[URE3] effect to a similar extent ( from ∼23% to ∼6% [ure-o] colonies ) , but did not eliminate it ( Figure 6B , rightmost images ) . Thus , inhibition of [URE3] by Sis1-H34Q depended partially on each of these Sis1 functions , suggesting it could be acting by making defective dimers with wild type Sis1 or by competing with Sis1 for binding to substrate , which in this system would be Ure2 amyloid . Although blocking dimerization can affect cooperative interaction of Sis1 with substrates in vitro [24] , these results suggest that Sis1-H34Q can interfere with functions of wild type Sis1 in multiple ways . To determine if the mutant Sis1 proteins could support propagation of [URE3] , we counter-selected against the URA3 plasmid encoding wild type Sis1 as described in Figure 2A ( see Figure 6C ) . Because several mutant Sis1 proteins appeared incapable of supporting [URE3] when this plasmid shuffling was done on plates without selecting for the prion , we also replica-plated the same patches of cells onto a series of similar plates lacking adenine to ensure recovery of cells capable of propagating [URE3] , but weakly ( Figure 6D ) . Cells will grow on FOA lacking adenine only if the Sis1 mutant supports both growth and [URE3] propagation . Cells expressing each of the mutant Sis1 proteins , except those containing the lethal H34Q mutation , grew on the FOA plate that contained adenine ( Figure 6C ) , showing the mutant proteins supported growth in place of Sis1 . However , only the cells carrying wild type Sis1 had a normal white [URE3] phenotype on this plate , indicating [URE3] was lost rapidly from cells expressing any of the mutant Sis1 proteins as soon as the plasmid encoding wild type Sis1 was lost . Accordingly , when cells from this plate were streaked for isolated colonies on medium containing adenine , only the cells expressing wild type Sis1 gave rise to uniformly white [URE3] colonies ( Figure 6E ) . Therefore , stable propagation of [URE3] depended on all of the Sis1 activities tested . Although cells expressing Sis1ΔD , Sis1-K199A and the mutant with both of these mutations propagated [URE3] when selection for the prion was maintained ( Figure 6D ) , they all lost [URE3] rapidly when grown on medium containing adenine ( Figure 6F , right panels ) . On medium lacking adenine , [URE3] cells expressing most of the mutant proteins formed colonies at a rate similar to those expressing wild type Sis1p ( Figure 6F , left panels ) , suggesting that the rapid loss of the prion under non-selective conditions was not due to the prion causing a disproportionate inhibitory effect on growth . However , it was evident that the Sis1ΔCTD [URE3] cells grew much more slowly than the others on medium selecting for [URE3] ( Figure 6D , 6F , images on left ) . Since [ure-o] cells expressing Sis1ΔCTD grew like wild type [ure-o] cells ( compare Figure 6E middle right image with upper left image ) this slower growth was caused by the presence of [URE3] , suggesting that the CTD region of Sis1 protects cells from toxic effects of [URE3] . Similar prion-associated toxicity was seen for [PSI+] cells expressing Sis1ΔCTD in place of Sis1 [32] , [41] . We did not recover cells expressing Sis1ΔGF or Sis1ΔGMCTD on FOA plates lacking adenine . Thus , in agreement with results using the hybrid proteins , [URE3] requires the Sis1 GF region to propagate . The inability to recover [URE3] cells expressing Sis1ΔGMCTD might indicate [URE3] is even more toxic in these cells . Alternatively , [URE3] could be unable to propagate in cells expressing only JGF even under conditions selecting for the prion . DnaJB1 propagated [URE3] only under selective conditions , and even then only weakly . Overall , our results indicate that [URE3] depends much more on Sis1 than [PSI+] does . We showed earlier that BKE ( with wild type DnaK ) supports [PSI+] propagation [15] , so we tested if this system would also be useful for studying [URE3] . BKE did not support [URE3] in hsp104Δ cells ( Figure S4 ) . Because wild type DnaK should be able to interact with J-proteins other than Sis1 , any or all of the cytosolic J-proteins might be able to compete with Sis1 for interaction with DnaK . Since [URE3] has a stringent requirement for Sis1 , a resulting reduction in ability of Sis1 to interact with the BKE machinery probably explains the inability of [URE3] to be propagated . Alternatively , as stable propagation of [URE3] depends critically on which Hsp70 is present [43] , [44] , the failure might reflect a requirement for a specific Hsp70 activity lacking in DnaK . Although the underlying mechanism of how overexpressed Ydj1 cures cells of [URE3] is uncertain , interaction with Hsp70 is critical because Ydj1 mutants unable to interact with Hsp70 do not cure and the J-domain alone of Ydj1 or other yeast Hsp40s is enough to cure [14] , [45] . Since all parts of Ydj1 except the J-domain can be mutated or deleted without disrupting curing , we expected the CTD of Ydj1 would not have a major influence on the curing of [URE3] . Instead , we anticipated that if a hybrid cannot function in place of Sis1 with the disaggregation machinery that replicates [URE3] prions , then it will interfere with this function if it can compete effectively with Sis1 for interaction with the Hsp70 component of this machinery . When overexpressed , SYS and YYS cured like Ydj1 ( see Figure 7A ) . SYY and YSY cured somewhat less effectively , and YSS cured inefficiently . Thus , the SYS and YYS hybrids that did not propagate [URE3] cured [URE3] very effectively , while YSS , which supported [URE3] , cured only weakly . Since SYS and YYS possess the dimerization region of Sis1 , their ability to cure [URE3] when overexpressed again might be related to an ability to form non-productive dimers with endogenous Sis1 , which could contribute to the curing by partially depleting cytosolic Sis1 function . In agreement with this explanation , although disrupting dimerization of Ydj1 does not affect curing considerably [45] , hybrids with the CTD of Sis1 that lacked the dimerization domain were significantly reduced in their ability to cure [URE3] ( Figure 7B ) . The residual curing by monomeric SYS and YYS could be explained by their competing with endogenous Sis1 for interaction with Hsp70 or with [URE3] as a substrate . Our curing data add to much previously published work [14] , [15] , [23] , [45] , [46] that support the explanation that overexpressing Ydj1 cures [URE3] by competing with Sis1 . If so , then increasing abundance of Sis1 should allow it to compete more effectively for the disaggregation machinery and reduce the curing . In line with this prediction , overexpressing Ydj1 cured [URE3] much less effectively in cells with elevated expression of Sis1 ( Figure 7C ) . It remained possible that elevating Sis1 reduced this curing through some general stabilizing effect on [URE3] . However , if increasing Sis1 protects [URE3] from Ydj1 curing specifically by improving ability of Sis1 to compete with Ydj1 , then increasing Sis1 would not be expected to protect [URE3] from being cured by other ways of impairing disaggregation machinery activity , such as inhibiting Hsp104 . Overexpressing the dominant negative Hsp104-2KT mutant [5] , which inhibits Hsp104 activity , cured [URE3] very effectively ( Figure 7C ) . Elevating Sis1 expression did not affect this curing . Thus , Sis1 specifically counteracted curing by overexpressed Ydj1 , which again is consistent with the idea that Ydj1 cures [URE3] by competing with Sis1 for interaction with the disaggregation machinery . We repeated the plasmid shuffle in [psi−] [PIN+] strain 930a to assess effects of our panel of Sis1 mutants on propagation of [PIN+] prions ( Figure 8 ) . We monitor [PIN+] by the fluorescence status of Rnq1-GFP , which is regulated by the RNQ1 promoter on a single-copy plasmid . Rnq1-GFP is punctate in [PIN+] cells and diffuse in [pin−] cells . [PIN+] propagated in cells with wild type Sis1 regardless of which of the mutant proteins was also expressed ( Figure 8A ) . However , while wild type cells had single bright foci , those co-expressing the mutant proteins , except for Sis1ΔD and H34Q , had multiple foci ( multi-dot ) . Therefore , several mutant Sis1 proteins dominantly affected propagation of [PIN+] . Cultures expressing Sis1-H34Q had a mixture of cells that possessed either single foci or completely diffuse fluorescence , indicating that H34Q inhibited the wild type Sis1 enough to cause [PIN+] to be lost from some cells . Combining ΔD or K199A with the H34Q mutation led to a multi-dot phenotype and reduced the proportion of [pin−] cells . These effects resemble the way the Sis1 mutants dominantly inhibited [URE3] and again show that the prion-curing effect of Sis1-H34Q depends partially on its dimerization and substrate-binding functions . Unlike [URE3] , [PIN+] was not dramatically destabilized by co-expression of Sis1ΔGF , here regulated by the SIS1 promoter on a single copy plasmid . However , overexpressing Sis1ΔGF in [PIN+] cells of another strain background is toxic and causes [PIN+] to be lost [47] . In agreement with earlier work [23] , [30] , Sis1ΔCTD supported [PIN+] in cells without wild type Sis1 , but Sis1ΔGF did not ( Figure 8B ) . However , unlike the earlier work that showed Sis1ΔGMCTD ( i . e . Sis1 JGF alone ) propagated [PIN+] , we did not observe [PIN+] foci in cells expressing Sis1ΔGMCTD . This difference might be due to differences in yeast strain backgrounds or by our variant of [PIN+] being more dependent on Sis1 for its propagation . In an earlier study the single-dot character of [PIN+] aggregates was frequently transformed by Sis1ΔGMCTD to multiple-dots , which were inheritable after transfer to wild type cells [30] . It is possible that the altered Sis1 activity causing this change is related to the loss of [PIN+] in our strains expressing Sis1ΔGMCTD , or that the action of Sis1ΔGMCTD on [PIN+] might be different in the two yeast strain backgrounds due to variation in expression of other chaperones . [PIN+] also propagated stably enough to be detected among most cells expressing the substrate-binding and dimerization mutants ( Figure 8B ) , showing that while these functions are important for [PIN+] propagation , [PIN+] depended less on these Sis1 activities than [URE3] . Keeping in mind that variations among strains of yeast and prions can influence prion stability , our data showing this intermediate sensitivity of [PIN+] to impairment of Sis1 activity is consistent with it being less sensitive than [URE3] , but more sensitive than [PSI+] , to curing by overexpressed Ydj1 . Finally , we confirm earlier findings that DnaJB1 ( Hdj1 ) supports propagation of [PIN+] [30] , [32] , [41] .
Hsp40s bind misfolded proteins and regulate Hsp70 activity , so the ability of Sis1 and Ydj1 to specify functions of the disaggregation machinery are likely to be mediated through interactions with substrate and Hsp70 . Our findings here show that C-terminal domains of Sis1 and Ydj1 can determine their functional differences in prion propagation , thermotolerance , galactose induction and their specific and general roles in supporting cell growth . Because the primary sites in Sis1 and Ydj1 that interact with substrates are contained within the CTDs , our data support the view that functional distinctions among Hsp40s can be due to differences in substrate specificity [34] , [35] . The CTD of Sis1 also interacts with Hsp70 , however , [47] , [48] and although not fully characterized , this interaction likely influences Hsp70 functions . Likewise , functions influenced by the zinc-finger and farnesylation of the CTD of Ydj1 are important for the transfer of substrate to Hsp70 and for protecting cells from a [PIN+] prion-related toxicity [49] , [50] . Also , Ydj1 can interact physically with Hsp104 in vitro [2] , although the relevance of this interaction in the cell is unclear . While these other activities can be expected to contribute to specificity of these Hsp40s , our in-vivo and in vitro results indicate that the CTDs alone of Sis1 and Ydj1 allow them to discriminate between specific substrates , which is in line with earlier data [35] . A plausible explanation for the functional distinctions we observe would be that the CTD of Ydj1 interacts more readily with amorphous aggregates of stress-denatured proteins while that of Sis1 targets the more structured and homogeneous prion polymers . Sis1 and Ydj1 both bind to prion proteins , although Sis1 seems to bind more avidly , and prion proteins can differ in the number or location of general and distinct binding sites recognized by different Hsp40s [50]–[54] . Additionally , because substrate specificity of Hsp40s can overlap , competition among Hsp40s for substrates could contribute to determining functions of the chaperone machinery . As seen earlier [47] , we found the GF region can confer prion-specific Hsp40 functions . We show that the GF region of Sis1 was needed to propagate [URE3] . [PIN+] also depends on an activity of the Sis1 GF region that cannot be complemented by the Ydj1 GF [22] , [23] , [30] . However , all testable activities of Sis1 are dispensable for [PSI+] propagation [32] , which shows that functions of the Sis1 GF are not necessary for propagation of all prions . Nevertheless , when appended to the JGF of their counterparts , the CTDs of Ydj1 and Sis1 generally were enough to allow the hybrid proteins to perform distinctly and effectively in place of intact Ydj1 and Sis1 . Thus , the J and GF regions of Sis1 and Ydj1 possess activities that overlap enough to perform similarly in several distinct tasks . Evidently , more work is needed to learn how the GF region specifies Hsp40 functions in its effects on prions and perhaps other Hsp40-dependent cellular processes . Much evidence points to Sis1 playing a key role in the replication of yeast prions by acting as a component of the Hsp104 disaggregation machinery that fragments prion fibers [14] , [30] , [46] , [55] , [56] . The varying degrees by which the prions depend on Sis1 agree with the supposition that different prions , and even different strains of the same prion ( see [46] , [57] ) , require varying degrees of disaggregation machinery activity to be fragmented . Together with the insensitivity of [PSI+] to Sis1 mutation , our finding that [URE3] is acutely sensitive to alteration in any Sis1 activity helps explain why depleting Sis1 causes cells to lose [URE3] much faster than they lose [PSI+] [14] . Our finding that [PIN+] had an intermediate dependency on Sis1 activity is also consistent with the intermediate rate of loss of [PIN+] seen upon Sis1 depletion . Overall our findings are consistent with earlier suggestions of a hierarchical dependency of these and other prions on the disaggregation machinery [57] . Extending this reasoning , our data strongly support an earlier suggestion that curing of [URE3] by Ydj1 or J-domains alone might be a result of competition for interaction with Hsp70 [14] . If Ydj1 cannot cooperate effectively with the disaggregation machinery to propagate [URE3] , then by displacing Sis1 from the Hsp70 component of this machinery , less Hsp104 would be directed toward fragmenting prion polymers . This mechanism also explains why J-domains alone are enough to destabilize [URE3] and aligns with the idea that certain intact J-proteins don't cure as effectively because they are normally recruited to defined locations in the cytosol , such as ribosomes , by their other distinct functional domains . Among the three prions tested [URE3] is most sensitive to reductions in Sis1 function , so one might expect that its propagation would be most affected by such competition . In line with a more stringent requirement of the disaggregation machinery for [URE3] replication , the average number of [URE3] prions per cell is lower than that for [PIN+] and [PSI+] [14] , [58] , [59] . The different seed numbers among variants of [PIN+] also could reflect differences in susceptibility to fragmentation , which in turn might underlie variation in sensitivity to curing by overexpressed Ydj1 [29] . Differences in susceptibility to fragmentation can be due to subtle differences in the structures of the amyloid that form the prions [60] . Such variation in the amyloid structures that determine differences among variants of [PIN+] might also have a bearing on the distinct pattern of variants of [PSI+] they induce [29] , [61]–[63] . The similar intermediate sensitivity of [PIN+] to depletion of Sis1 seen in earlier work and to specific mutations of Sis1 seen here suggests the variants of prions used are similar and that their prion character is largely independent of strain background . Nevertheless , findings might differ if other variants of prions or other strain backgrounds propagating them were compared directly . It is becoming evident that the differences in ways prions respond to J-proteins and other Hsp70 co-chaperones likely reflect differences in the ways prions depend on Hsp70 . Altering activity of Hsp70 directly by mutation or indirectly by altering its co-chaperones can influence prion propagation in the same ways , which supports this idea [64]–[66] . Stability of [SWI+] prions is also highly sensitive to altered expression of Hsp40s and J-domains , which seems to be related to a strict dependency on optimal Hsp70 activity [67] . Yeast has four highly homologous Ssa Hsp70 paralogs and prion phenotypes vary greatly when different Hsp70s are the sole source of Ssa protein [43] . These differences likely reflect differences in the way the Hsp70s interact with or are regulated by the Hsp40s or other factors . Hsp70 also can be a primary factor in recruiting the disaggregation machinery to prion polymers [7] . Notably , however , it is not Hsp70 substrate-binding per se , but the regulation of this binding , presumably by co-chaperones , that specifies distinctions in Hsp70 functions with regard to [URE3] propagation [44] . NEFs can also affect prion propagation through their ability to regulate Hsp70 [13] , [68] , [69] . Because Hsp70 is a critical component of the Hsp104-based disaggregation machinery , altering Hsp70 or its co-chaperones can be expected to affect propagation of prions by influencing composition and activity of this machinery . The distinct susceptibilities of prions to alterations in various disaggregation machinery components might therefore reveal differences in the ways various chaperones combine to act most effectively on them as specific substrates . Understanding why prions respond differently to the various chaperone machinery components including J-proteins , NEFs and Hsp70s should help us understand both fundamental and subtle ways that these components interact to produce effective protein remodeling machines .
Yeast strains used are isogenic to strain 779-6A ( MATa , SUQ5 , kar1-1 , ade2-1 , his3Δ202 , leu2Δ1 , trp1Δ63 , ura3-52 ) [70] , which is used for monitoring [PSI+] and [PIN+] . Knockouts and replacements of chromosomal genes were done using standard transformation procedures [71] . Strain MR386 has E . coli CLPB in place of the chromosomal HSP104 gene [15] and contains plasmids expressing E . coli dnaKR167H ( pMR150LG-R167H ) and E . coli GrpE ( pMR134H ) under the control of the GPD ( glyceraldehyde-3-phosphate dehydrogenase - TDH3 ) and FES1 promoters , respectively . [PSI+] is maintained in strain MR386 by pJ312 , which is HSP104 on a URA3 plasmid [72] . Strain MR502 has ydj1::KanMX and carries p316YDJ1 , which is YDJ1 on a URA3 plasmid . [PSI+] [PIN+] strain 930 has sis1::KanMX and carries plasmid pYW17 , a URA3-based plasmid encoding wild type SIS1 [22] , [32] . Strain 930a is a [psi−] [PIN+] version of 930 that carries plasmid p313Rnq1-GFP . It was cured of [PSI+] by transient growth on medium containing 3 mM guanidine and then [PIN+] clones among [psi−] isolates were identified by punctate Rnq1-GFP fluorescence . Our [PIN+] variant is uncharacterized , but of the single-dot type , which typically has sturdier fibers and a lower seed number per cell than multi-dot [PIN+] [61] . Isogenic strain 1075 , for monitoring [URE3] , has ADE2 regulated by the DAL5 promoter ( PDAL5::ADE2 , see below ) in place of ade2-1 [43] . Strain 1385 is strain 1075 with sis1::KanMX and plasmid pYW17 , which is SIS1 on a URA3 plasmid . Strains 1408 and 1410 are hsp104Δ versions of strain 779-6A and 1075 , respectively [15] . Both carry pJ312 . Our parental strains carry only one variant of [PSI+] , [URE3] or [PIN+] . SIS1 plasmids used contain wild type and mutant SIS1 alleles on the pRS314 single-copy TRP1 vector [22] , [32] . Plasmid pRU4 is LEU2-based single-copy plasmid pRS415 containing the GAL1 promoter and CYC1 terminator flanking the polylinker sites SpeI and XhoI . For Gal-induced expression , YDJ1 and hybrid alleles were inserted into pRU4 as BamHI-SalI fragments . All plasmids used in this study are listed in Table 1 . Plasmids encoding E . coli genes or yeast Hsp40 genes with D36N mutations are described [15] . With the exceptions that 1/2YPD plates contain 5 g/L yeast extract , YPAD plates contain 400 mg/L ( excess ) adenine and solid defined media contain 10 mg/L ( limiting ) adenine , standard media and growth conditions were used [71] . Sis1 and Ydj1 have clearly defined and characterized J-domains , glycine-phenylalanine ( GF ) rich middle domains , and a C-terminal region that contains two major elements ( CTDI and CTDII ) and a dimerization domain . The main structural differences between Sis1 and Ydj1 are a glycine-methionine-rich ( GM ) extension of the GF domain in Sis1 , which has GF-redundant functions , and a Zn-finger domain ( ZF ) embedded between beta-strands 2 and 3 of the CTDI of Ydj1 that is absent in Sis1 [73] . Because the ZF of Ydj1 is an integral part of CTDI , we designed our Sis1-Ydj1 hybrids using the GF-CTDI junction to restrict the number of domain swaps and avoid using complicated junctions to swap the ZF region . Rather than designating the GM region as a separate domain , we combined the functionally redundant GF and GM regions of Sis1 into a single domain . Thus , hybrid alleles were made by swapping three regions: the J-domain , the glycine-rich region and the C-terminal portion , which includes CTDI , CTDII and the dimerization domains , herein referred to simply as the CTD ( see Figure 1A , [30] ) . Hybrid genes were synthesized by GENEWIZ , Inc ( South Plainfield , NJ ) and sub-cloned into variants of pRS414 that placed the ORF under the control of the SIS1 , YDJ1 or GPD promoters and a downstream CYC1 transcriptional terminator [74] . All constructs contained a c-terminal c-myc tag that had no noticeable affect on functions in vivo . Depletion of the ribosome release factor Sup35 by its sequestration in [PSI+] prion aggregates causes nonsense suppression . We monitored [PSI+] by suppression of the ade2-1 nonsense allele in our strains . [PSI+] cells are Ade+ and white , while [psi−] cells are Ade− and when grown on limiting adenine are red due to accumulation of a metabolite of adenine biosynthesis . The presence of [URE3] was monitored similarly by use of an ADE2 allele regulated by the DAL5 promoter ( PDAL5::ADE2 ) [75] , [76] . Under standard growth conditions Ure2 represses transcription of nitrogen metabolic genes , such as DAL5 . [URE3] sequesters Ure2 into prion aggregates , thereby reducing Ure2 function and activating the DAL5 promoter . Thus , [URE3] cells are Ade+ and white , while [ure-o] cells are Ade− and red on limiting adenine . We confirmed that Ade+ phenotypes were due to the presence of prions by their guanidine curability and by crosses with cells lacking prions to produce a dominant , guanidine-curable Ade+ phenotype . We monitored [PIN+] by assessing aggregation status of a plasmid-expressed Rnq1-GFP fusion protein . GFP fluorescence is diffuse in [pin−] cells , but noticeably punctate in [PIN+] cells . In this study we used a typical strong [PSI+] strain and single variants of [URE3] and [PIN+] prions . Microscopic analysis of Rnq1-GFP fluorescence in live cells was done with a Nikon E-800 microscope with log phase cells grown in medium selecting for the plasmid encoding the Rnq1-GFP fusion protein . Images were captured using IVision software and processed using Adobe Photoshop software . Log phase cells grown in medium selecting for plasmids were diluted in fresh medium to an OD600 of 0 . 25 and 100 µL was transferred to 0 . 5 mL test tubes and placed in a PCR machine for thermocycling as indicated . At various times aliquots were removed and placed on ice . Cooled cells were serially diluted and 5 µL drops were spotted onto YPAD plates . Strains 930 and 1385 ( both sis1Δ ) and derivatives of MR502 ( ydj1Δ ) carrying wild type SIS1 or YDJ1 on URA3-based plasmids were transformed by TRP1-based plasmids carrying wild type , mutant or hybrid alleles . Strain MR502 also carries pMR169 for monitoring GAL induction ( see below ) . Transformants were grown as patches of cells on medium lacking both tryptophan and uracil and then replica-plated onto similar medium containing uracil to allow loss of the URA3 plasmid . These were then replica-plated onto medium containing 5-FOA , which kills cells that did not lose the resident URA3 plasmid . For Sis1 , growth on 5-FOA plates shows complementation of functions essential for growth , and growth without adenine shows complementation of Sis1 functions required for prion propagation . To test complementation of Ydj1 function , 5-FOA resistant cells of strain MR502 were grown overnight in medium selecting for the TRP1 plasmids , normalized to the same cell density ( OD600 = 0 . 25 ) and five-fold serially diluted . Aliquots of the dilutions ( 5 µL ) were then dropped onto YPAD plates . Scanned images of the plates were taken after they were incubated at the indicated temperatures for 3–4 days . Aliquots of overnight cultures of MR502 transformants used for growth complementation were transferred to synthetic raffinose medium ( SRaf ) and grown overnight . These cells carried the TRP1-marked hybrid alleles or empty vector control and a HIS3-marked plasmid encoding firefly luciferase under the control of the GAL10 promoter ( pMR169 ) . Cell densities were adjusted to OD600 = 0 . 3 in fresh medium and the initial reading ( t = 0 ) was taken by mixing 100 µL culture with 50 µL of 1 mM luciferin in 0 . 1 M sodium citrate , pH 5 . 0 immediately before reading in a Zylux Femtomaster luminometer , with a 10 s delay and 5 s read time . Galactose from a 20% stock was then added to the cultures to obtain a final concentration of 2% and the cells were incubated on a roller at 30°C . Readings were taken at 30 , 60 and 120 minutes after addition of galactose . All samples in three experiments were tested in triplicate . No significant growth occurred during the course of the experiment . Overnight SD cultures of cells carrying YDJ1 or hybrid alleles on pRU4 for galactose induction were used to inoculate SGal medium to OD600 = 0 . 05 and incubated with shaking at 30°C . Generations were monitored as doublings of OD600 . Cultures were sub-cultured as necessary to keep the OD600 less than 2 . 0 . After 3 , 6 and 9 generations of growth in galactose , aliquots were removed and cells plated onto 1/2YPD plates . Loss of [URE3] was assessed by determining the fraction of entirely red ( i . e . [ure-o] ) colonies after 3 days of incubation at 30°C . To test the ability of overexpressed Sis1 to block Ydj1- or Hsp104- mediated curing of [URE3] , strain 1075 was co-transformed with various combinations of CEN plasmids expressing Sis1 , Ydj1 or Hsp104 under the control of the GPD promoter . Cell lysates for western blots were prepared as described [77] . Briefly , cells were suspended in lysis buffer and broken by agitation with glass beads . For each sample 10 µg of protein was separated on 4–20% SDS-PAGE gels , transferred to PVDF membranes and probed using anti-c-myc antibody ( AbCam #ab9106 ) and chemiluminescence . After developing , the blots were stained by amido-black ( Sigma #A-8181 ) as a loading and transfer control . Hsp104 [78] , Ydj1 [79] , and GFP-38 [80] were purified as described . SSY was isolated as described for Ydj1 [79] . Sis1 was purified as described [81] with some modifications . Briefly , BL21 ( DE3 ) was transformed with a pET11 plasmid containing the Sis1 gene , cultures were grown at 30°C to OD595 0 . 8 and cells were induced with 1 mM IPTG for 3 h . Clarified cell lysates were applied to an S-Sepharose-FF column ( GE Healthcare ) in 20 mM MES buffer , pH 6 . 0 , 0 . 1 mM EDTA and 1 mM DTT . Sis1 was eluted with a linear gradient from 0–1 M NaCl . Peak fractions were pooled , buffer exchanged into 20 mM MES buffer pH 6 . 0 , 0 . 1 mM EDTA and 1 mM DTT . The sample was applied to a monoS column ( GE Healthcare ) and eluted with a linear gradient from 0–1 M NaCl . YYS was purified similarly to Sis1 , except that the buffer used was 25 mM HEPES , pH 7 . 6 , 0 . 1 mM EDTA and 1 mM DTT . For Ssa1 , a pET24 plasmid containing the Ssa1 gene was transformed into Rosetta BL21 ( DE3 ) cells . Cultures were grown to 0 . 8 OD595 at 30°C and induced with 0 . 2 mM IPTG overnight . The clarified lysate was applied to a Q-sepharose FF column ( GE Healthcare ) in 20 mM Tris . HCl , pH 7 . 6 , 40 mM KCl , 0 . 1 mM EDTA and 1 mM DTT . Ssa1was eluted with a linear gradient of 40–400 mM KCl over 20 column volumes . Peak fractions were collected and buffer exchanged into 25 mM HEPES , pH 7 . 6 , 100 mM KCl , 0 . 1 mM EDTA and 1 mM DTT and further purified over a monoQ column ( GE Healthcare ) using a linear gradient of 100–400 mM KCl over 20 column volumes . Peak fractions were collected , analyzed , supplemented with 10% glycerol , frozen on dry ice and stored at −80°C . | The cellular chaperone machinery helps proteins adopt and maintain native conformations and protects cells from stress . The yeast Hsp40s Ydj1 and Sis1 are co-chaperones that regulate Hsp70s , which are key components of many chaperone complexes . Both of these Hsp40s are crucial for growth and Ydj1 directs disaggregation activity of the Hsp100-based machinery to provide stress protection while Sis1 directs this activity to promote prion replication . Ydj1 also cures yeast of certain prions when overexpressed . We show that C-terminal domains that possess substrate-binding function of Ydj1 and Sis1 can mediate these and other functional distinctions and that the degree that prions depend on Sis1 activities could underlie differences in how they respond to alterations of chaperones . These findings support a view that Hsp40s regulate and specify functions of the chaperone machinery through substrate discrimination and cooperation with Hsp70 . The disproportionate evolutionary expansion of Hsp40s ( J-proteins ) relative to their Hsp70 partners led to a proposal that this amplification allows increased regulation and fine-tuning of chaperone machines for increasingly complex processes . Our findings support this idea and provide insight into fundamental aspects of this cooperation . | [
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] | 2014 | Hsp40s Specify Functions of Hsp104 and Hsp90 Protein Chaperone Machines |
In meiosis I , homologous chromosomes segregate away from each other–the first of two rounds of chromosome segregation that allow the formation of haploid gametes . In prophase I , homologous partners become joined along their length by the synaptonemal complex ( SC ) and crossovers form between the homologs to generate links called chiasmata . The chiasmata allow the homologs to act as a single unit , called a bivalent , as the chromosomes attach to the microtubules that will ultimately pull them away from each other at anaphase I . Recent studies , in several organisms , have shown that when the SC disassembles at the end of prophase , residual SC proteins remain at the homologous centromeres providing an additional link between the homologs . In budding yeast , this centromere pairing is correlated with improved segregation of the paired partners in anaphase . However , the causal relationship of prophase centromere pairing and subsequent disjunction in anaphase has been difficult to demonstrate as has been the relationship between SC assembly and the assembly of the centromere pairing apparatus . Here , a series of in-frame deletion mutants of the SC component Zip1 were used to address these questions . The identification of a separation-of-function allele that disrupts centromere pairing , but not SC assembly , has made it possible to demonstrate that centromere pairing and SC assembly have mechanistically distinct features and that the centromere pairing function of Zip1 drives disjunction of the paired partners in anaphase I .
In meiosis I , homologous chromosomes segregate away from each other–the first of two rounds of segregation that allow the formation of haploid gametes . In order to segregate from one another the homologs must first become tethered together as a unit , called a bivalent . As a single bivalent , the partners can attach to microtubules such that the centromeres of the homologs will be pulled towards opposite poles of the spindle at the first meiotic division . Crossovers between the aligned homologs provide critical links , called chiasmata , which allow the homologs to form a stable bivalent ( reviewed in [1] ) . Failures in crossing-over are associated with elevated levels of meiotic segregation errors in many organisms , including humans ( reviewed in [2] ) . However , there are mechanisms , other than crossing-over , that can also tether partner chromosomes . Notably , studies in yeast and mouse spermatocytes have revealed that the centromeres of partner chromosomes pair in prophase of meiosis I [3–6] . In budding yeast , it has been shown that this centromere pairing is correlated with the proper segregation of chromosome pairs that have failed to form chiasmata . But the formal demonstration that centromere pairing in prophase directly drives disjunction in anaphase has been difficult , because the mutations that disrupt centromere pairing also disrupt other critical meiotic processes [7 , 8] . The protein Zip1 in budding yeast localizes to paired centromeres in meiotic prophase and is necessary for centromere pairing ( Fig 1A ) [7–10] , and similar observations have been made in Drosophila oocytes and mouse spermatocytes [3 , 6 , 11] . Zip1 is expressed early in meiosis and first appears as dispersed punctate foci in the nucleus . Some , but not all , of these foci co-localize with centromeres , and indeed , Zip1 mediates the homology-independent association of centromeres at this stage of meiosis , a phenomenon called centromere-coupling ( Fig 1A , green arrowhead ) [10 , 12] . Zip1 later acts as a component of the synaptonemal complex ( SC ) –a proteinaceous structure that assembles between the axes of the homologous partners as they become aligned in meiotic prophase ( Fig 1A , blue arrowhead ) [13] . In budding yeast and mouse spermatocytes , when the SC disassembles in late prophase Zip1/SYCP1 remains at the paired centromeres , leaving the homologous partners visibly joined by only chiasmata and centromere-pairing ( Fig 1A ) [3 , 6–8] . Virtually all of the Zip1/SYCP1 appears to have left the chromosomes by the time they begin attaching to the meiotic spindles . The prophase association promoted by Zip1 is correlated with proper segregation in anaphase , as zip1 deletion mutants have no centromere pairing and also segregate achiasmate partners randomly ( Fig 1A ) [7 , 8] . A critical study by Tung & Roeder identified functional domains of Zip1 that are required for SC assembly contributing key information that has contributed to the current model for the structure of the SC [14] . This and other studies [15] have suggested that in the SC , Zip1 is in the form of head-to-head dimers ( Fig 1B ) . These dimers , in turn are thought to assemble in a ladder-like structure with the N-termini in the center of the SC and the C-termini associated with the axes of the homologous partners ( Fig 1B ) . This model has been extrapolated to other organisms because the basic structure of transverse filament components , like Zip1 , are believed to be conserved even though their amino acid sequences have diverged ( reviewed in [16] ) . Tung and Roeder ( 1998 ) used an ordered series of in-frame deletions of ZIP1 to identify ways in which different regions of the protein contributed to SC structure and function ( Fig 1C ) . This was before the discovery that Zip1 is also involved in promoting centromere coupling and centromere pairing . We have re-constructed this deletion series to evaluate the ways in which different regions of Zip1 contribute to these centromere-associated functions . This information could be used to reveal relationships in the underlying mechanisms of centromere coupling , centromere pairing , and SC assembly , and identify separation-of-function alleles that would reveal more contributions made to these processes by specific sub-regions of Zip1 . These approaches make clear that centromere coupling , centromere pairing , and SC assembly all require certain parts of the Zip1 protein that are not required by the others–suggesting mechanistic differences in these phenomena ( Fig 1D ) . Second , they provide a clear demonstration that centromere pairing in prophase , distinct from other SC-related functions of Zip1 , drives disjunction of achiasmate partner chromosomes in anaphase I .
A series of nine in-frame deletion mutants ( Fig 1C ) were tested to determine which regions of the ZIP1 coding sequence are essential for the homology independent centromere coupling that occurs in early meiotic prophase . Centromere coupling was assayed by monitoring the numbers of kinetochore foci ( Mtw1-MYC ) in chromosome spreads from prophase meiotic cells [10 , 12] ( Fig 2A ) . Diploid yeast have sixteen pairs of homologous chromosomes . When the centromeres of the thirty-two chromosomes are coupled they form on average sixteen Mtw1-MYC foci ( Fig 2B , ZIP1 , blue line ) . Mutants that are defective in coupling exhibit higher numbers of Mtw1-MYC foci . In this and previous studies [17] we see that deletion of ZIP1 results in average of about nineteen-to-twenty foci per strain ( Fig 2B , zip1Δ , red line ) less than the maximum of thirty-two expected if centromere coupling was abolished . This may reflect overlap of some of the centromeres , associations of centromeres by a Zip1-independent mechanism , or failure to score some of the unpaired centromeres , as the fluorescence signal is reduced by half for unpaired centromeres . However , in our hands and elsewhere [9 , 10 , 12 , 17–19] , the zip1 deletion strain consistently yields significantly higher numbers of centromere foci in chromosome spreads , thus providing a reliable assay for centromere coupling . The experiment was done in strains lacking SPO11 , which encodes the endonuclease responsible for creating programmed double strand DNA [20] ) . This blocks meiotic progression beyond the coupling stage and prevents the homologous alignment of chromosomes [12 , 18] . The strains also featured GFP-tagged copies of the centromeres of chromosome I . Briefly , 256 repeats of the lac operon sequence ( lacO ) were inserted adjacent to the centromere of chromosome I ( CEN1 ) and the cells were engineered to express lacI-GFP , which localizes to the lacO array [21] . In the centromere coupling stage , the two CEN1-GFP foci are nearly always separate because coupling is usually between non-homologous partner chromosomes ( Fig 2A ) [10] . The mutants could be assigned to one of three groups based on their coupling phenotypes ( Fig 2B and S2 Table ) , indistinguishable from ZIP1 ( proficient for coupling; blue histograms ) , indistinguishable from zip1Δ ( loss of coupling; red and orange histograms ) , or intermediate ( green histogram ) ( Fig 2B ) . The results make it possible to assign functional roles to several portions of Zip1 . First , a portion of the N-terminus and adjacent coiled-coil ( NM1 region , amino acids 164–242 ) is critical for centromere coupling ( Fig 1D ) . This region was shown previously to be largely dispensable for SC assembly and sporulation [14] . Second , a portion of the C-terminus ( C1 region , amino acids 791–824 ) shown previously to be essential for SC assembly [14] , is also critical for centromere coupling . Third , two mutants that are unable to assemble SC ( zip1-C2 and zip1-M1; [14] ) are indistinguishable from wild-type cells for centromere coupling . We conclude that Zip1 contains some regions that are critical for centromere coupling but not SC formation and vice versa . Though centromere coupling and centromere pairing both require Zip1 , they have distinct genetic requirements suggesting they may operate by ( at least partially ) different mechanisms [17] . To determine the regions of Zip1 that are required for achiasmate segregation we monitored the meiotic segregation of a pair of centromere plasmids that act as achiasmate partners in meiosis . Each plasmid carries an origin of DNA replication and the centromere of chromosome III , allowing the plasmids to behave as single copy mini-chromosomes in yeast . One plasmid is tagged with tdTomato-tetR hybrid proteins at a tet operon operator array [22] , the other is tagged with GFP , as described above for chromosome I . Previous work has shown that such achiasmate model chromosomes disjoin properly in most meioses [23–25] and this segregation at anaphase I is correlated with the ability of their centromeres to pair late in prophase [5] . To increase the synchrony of meiotic progression in this experiment , NDT80 , which promotes the transition out of prophase and into pro-metaphase , was placed under the control of an estradiol-inducible promotor [26–28] . Meiotic cells were allowed to accumulate in pachytene of prophase , then induced to synchronously exit pachytene and enter pro-metaphase . We scored segregation of the plasmids in the first meiotic division by monitoring the location of their GFP and tdTomato-tagged centromeres in anaphase I cells , identified by their two separated chromatin masses ( Fig 3A ) . Wild-type cells , under these conditions , exhibited 28% non-disjunction of the CEN plasmid pair ( Fig 3B ) . The loss of Zip1 function can result in a pachytene arrest in some strain backgrounds [29] including the strain used in these experiments . Reducing the sporulation temperature to 23°C , as was done here , can permit a partial bypass of the arrest [29] . Still several of the mutations ( zip1Δ , zip1-C2 , zip1-C1 , and zip1-NM2 ) yielded very few anaphase cells , and failed to sporulate , presumably due to the pachytene arrest . These observations are consistent with previously published work [14] . Of the remaining mutants , three ( zip1-N1 , zip1-M1 , zip1-MC1 ) showed significantly elevated non-disjunction of the centromere plasmids ( Fig 3B ) ( Fig 1D ) . Among these three the zip1-N1 was of particular interest . The zip1-M1 and zip1-MC1 mutants have disruptions in multiple processes related to Zip1 function including SC assembly , meiotic crossing-over and meiotic progression [14] , thus with these mutants it would be difficult to resolve whether the observed increase in achaismate segregation is due to a specific failure in centromere behavior or a more general deficit in Zip1 activity . In contrast , the zip1-N1 mutants appear to be proficient for the essential meiotic functions of Zip1 . Previous work had shown that zip1-N1 mutants exhibit sporulation efficiency , meiotic recombination behavior , and spore viability distinguishable from WT strains [14] . This work also suggested that in zip1-N1 mutants Zip1 deposition in SC is less continuous than in wild-type cells . Super-resolution microscopy revealed this to be true in our strain background as well ( S1 Fig ) and quantification of Zip1 deposition revealed that in zip1-N1 mutants SC is characterized by more , and shorter , patches of Zip1 ( S2 Fig ) . This reduction in Zip1 loading is not due to a dramatic reduction of Zip1 expression or stability in the zip1-N1 strains ( S3 Fig ) . As in previous studies spore viability in zip1-N1 mutants was indistinguishable from wild-type strains suggesting that chromosome segregation occurs with high fidelity in zip1-N1 mutants ( S2 Fig ) . Together the zip1-N1 phenotypes suggest that amino acids 21–163 ( deleted in zip1-N1 ) are more critical for mediating the segregation of achiasmate partners than for other aspects of SC assembly and function . For this reason , we focused our further analyses on this allele . Because achiasmate segregation in anaphase is correlated with prior centromere pairing in prophase [7 , 8] , we tested whether the zip1-N1 mutants were proficient in centromere pairing . Wild-type and zip1-N1 cells containing GFP and tdTomato tagged centromere plasmids were induced to sporulate and harvested five—seven hours later when pachytene cells are prevalent . Chromosome spreads were then prepared and the distances between the tdTomato and GFP foci were measured in spreads exhibiting the condensed chromatin typical of pachytene cells ( Fig 4A ) . The average centromere-centromere distance was significantly greater in zip1-N1 mutants ( Fig 4B ) consistent with a loss of pairing . When spreads with an inter-centromere distance of less than 0 . 6 μm were scored as “paired” ( see example in Fig 4A ) , the zip1-N1 mutation was found to exhibit a significant reduction in the frequency centromere pairing between the achiasmate plasmids ( Fig 4C ) . Failure of centromere pairing in the zip1-N1 mutant could be due to a failure of Zip1 to associate with centromeres . To test this , we analyzed the co-localization of the Zip protein with kinetochores in ZIP1 and zip1-N1 strains . The experiments were done in a zip4Δ strain background to allow visualization of Zip1 localization independently of an SC structure . Images were collected using structured illumination microscopy and the level of co-localization was determined ( see Materials and Methods ) . Briefly , the images of Mtw1 and Zip1 were converted to binay images and the overlap of the Mtw1 and Zip1 foci was determined using ImageJ software and the plug-in JACoP [30] . The positions of the foci were then computationally randomized within the area of the chromosome spread one thousand times , and Costes’ P-value was then calculated to evaluate the statistical significance of the difference between the frequency of observed versus random overlap [31] . Every ZIP1 spread showed significantly more co-localization of Zip1 and Mtw1 than was found in a randomized simulation ( Fig 5A ) , consistent with earlier work [9 , 10 , 12] . By contrast , many of the zip1-N1 spreads showed no significant co-localization above the randomized control ( Fig 5B ) . Consistent with these results , zip1-N1 chromosome spreads , as a group , showed significantly lower levels of co-localization of Mtw1 with Zip1 than was seen in ZIP1 strains ( Fig 5C ) . The reduced localization of Zip1-N1 protein to natural centromeres , above , and the failure of pairing of plasmid centromeres in zip1-N1 strains ( Fig 4 ) raised the question of whether the zip1-N1 mutation compromises the pairing of natural chromosomes . To assay centromere pairing we counted the numbers of kinetochore foci ( Mtw1-GFP ) in chromosome spreads from ZIP1 , zip1-N1 and zip1Δ cells , in the above experiment ( Fig 5 ) using structured illumination microscopy . Prior work had shown that in zip4 mutants , with no SC , kinetochores are held in close proximity by centromere pairing . When ZIP1 is deleted , the centromeres can resolve into two foci in chromosome spreads [32] . The ZIP1 strain gave an average of 13 . 9 kinetochore foci per spread , consistent with pairing of the 32 kinetochores . The zip1-N1 mutant gave significantly higher numbers of kinetochore foci ( average 16 . 4; p<0 . 01 ) signifying a loss of centromere pairing but not as dramatic a loss was observed in the zip1 strain ( average 21 . 3; p<0 . 0001 ) .
Prior work has shown convincingly that the structure that mediates centromere coupling is distinct from mature SC [9 , 10 , 17 , 19] . Several proteins ( Zip2 , Zip3 , Zip4 , Ecm11 , Gmc2 , and Red1 ) known to be essential for SC assembly are not required for centromere coupling . But the domains of Zip1 that are required for centromere coupling have not been defined . The experiments here reinforce that the requirements for Zip1 for centromere coupling and SC assembly are quite different . First , centromere coupling was proficient in zip1-C2 mutants , which have severe defects in SC assembly . But these mutants exhibit little Zip1 expression , which may be due to the lack of a nuclear localization signal [34] . Thus , this result is difficult to interpret other than to suggest that centromere coupling may require far less Zip1 than does SC assembly . Notably , the zip1-M1 mutation , which also blocks SC assembly , is proficient in centromere coupling . The zip1-M1 mutation , which eliminates amino acids 244–511 , has a unique SC defect . The Zip1-M1 protein efficiently localizes to the axes of the homologous partners , but does not efficiently cross-bridge the axes ( Fig 1C; [14] ) . This defect may reflect an inability of Zip1 molecules from opposite axes to associate with one another ( as in Fig 1B ) or may reflect an inability of Zip1 to associate with central element proteins that promote or stabilize the cross-bridging of axes by Zip1 . In either case , such cross-bridging must not be important for centromere coupling , and is consistent with the finding that the central element proteins Ecm11 and Gmc2 are also not required for centromere coupling [19] . Together these findings suggest that centromere coupling is probably not mediated by a structure that includes SC-like cross-bridging . The only protein , beyond Zip1 , that is known to be required for centromere coupling is the cohesin component Rec8 [9] ( the requirements for the other cohesin subunits have yet to be reported ) . It may be that centromere coupling is mediated by the cohesin-dependent accumulation of Zip1 at early prophase centromeres [9 , 32] , followed by interactions between Zip1 molecules that promote the association of centromere pairs . Experiments performed mainly in a mouse spermatocyte model [3 , 6] suggest that the SYCP1 ( the functional homolog of Zip1 ) that persists at paired centromeres , after SC disassembly , is accompanied by other SC proteins . This suggests that centromere pairing could be mediated by a conventional SC structure . But the identity of regions of Zip1 that are critical for centromere pairing , and whether they are distinct from the regions necessary for SC assembly , have not been addressed . Our work suggests that there are significant differences in the requirements for Zip1 function in centromere pairing and SC assembly . We arrive at this conclusion following an evaluation of the centromere pairing phenotypes of the zip1-N1 in-frame deletion . Prior work had shown this allele had no measurable differences from the wild-type ZIP1 allele in spore viability , crossover frequency , and genetic interference , with a slight defect in the continuity of mature linear SC structures [14] . In our strain background the zip1-N1 mutation also exhibited wild-type levels of spore viability , and structured illumination microscopy confirmed the slight discontinuity in some SC structures in the zip1-N1 background ( S1 Fig and S2 Fig . ) . However , in centromere pairing assays the zip1-N1 mutants showed major defects . In the zip1-N1 mutant the centromeres of natural chromosome bivalents were more likely to become disengaged in chromosome spreads than was seen with wild-type controls , but the defect was not as severe as is seen in zip1Δ strains–suggesting that there are regions outside of the N1 region that also promote association of the bivalent centromeres . It could be that these other regions are influencing things like cross-over frequency or distribution , that along with centromere-pairing help keep bivalent centromeres associated in the natural chromosome pairing assays . When we assayed the segregation of achiasmate centromere plasmids , in which such functions cannot contribute to centromere association , then the zip1-N1 phenotype becomes severe . The zip1-N1 mutant showed a dramatic reduction in the pairing of plasmid centromeres . In the systematic analysis of SC assembly in the ZIP1 deletion mutants performed by the Roeder laboratory [14] , zip1-N1 mutants assembled SC with apparently normal kinetics . Our imaging analysis reveals that in zip1-N1 mutants , some chromosomes exhibit SC that features discontinuous deposition of . Zip1 while others exhibit SC that is undistinguishable from that on wild-type chromosomes . Further this SC is sufficient to promote high fidelity segregation of exchange chromosomes . In contrast , the Zip1-N1 protein is completely defective for achiasmate centromere pairing , suggesting that the centromere pairing defect isn’t just due to inefficient SC assembly . Instead , the sensitivity of centromere pairing to the zip1-N1 mutation suggests that the N-terminus imbues functions on the protein that are especially or specifically important for centromere pairing . The mechanism of centromere pairing remains unclear as does the role of the Zip1 N-terminus , but kinetochore co-localization experiments suggest that this region of Zip1 promotes localization to , or maintenance of , Zip1 at the centromeres in late prophase . The fact that early prophase centromere coupling is normal in zip1-N1 mutants reinforces that coupling and pairing are fundamentally distinct processes and that the N1 region is not necessary for localization of Zip1 to centromeres in early prophase when coupling occurs . Experiments in yeast , Drosophila and mice have shown that SC-related proteins persist at paired centromeres after SC disassembly [3 , 7 , 8 , 11] . These observations have been the foundation for the model that centromere pairing promotes subsequent disjunction , especially of achiasmate chromosomes that are only connected at their centromeres . Until this work , it has not been possible to conclusively demonstrate a causal relationship between centromere pairing and non-exchange disjunction , because the genetic analysis of this relationship used mutations that disrupted multiple meiotic processes–not just centromere pairing . The zip1-N1 separation-of-function allele , because it is indistinguishable from wild-type for most assayable functions of Zip1 , has made it possible to formally demonstrate that centromere-pairing in prophase is a requisite step in a process that mediates the segregation of achiasmate partners in anaphase . The mechanistic question of how prophase centromere pairing drives disjunction remains to be answered . The fact that in yeast , mice and Drosophila , the majority of the centromeric SC components have been lost from the centromeres well before the partners begin to attach to microtubules makes this even more mysterious . The zip1-N1 allele , which specifically targets centromere associations of Zip1 , and the centromere pairing process , will be an important tool for addressing these questions .
We created the same nine deletion mutants of ZIP1 that Tung and Roeder had studied for their work in SC formation [14] by using standard PCR and two-step-gene-replacement methods [35 , 36] . All mutant versions of ZIP1 were confirmed by PCR and sequencing . The native ZIP1 promoter was unaltered in these strains allowing each mutant protein to be expressed at the appropriate level and time . Culturing of strains was as described previously [17] . Strain genotypes are listed in S1 Table . Centromere coupling was monitored largely as described previously [12] . Cells were harvested five hours after shifting cultures to sporulation medium at 30°C . Meiotic nuclear spreads were prepared according to [37] with minor modifications . Cells were spheroplasted using 20 mg/ml zymolyase 100T for approximately 30 minutes . Spheroplasts were briefly suspended in MEM ( 100mM MES , 10mM EDTA , 500μM MgCl2 ) containing 1mM PMSF ( phenylmethylsulfonyl fluoride ) , fixed with 4% paraformaldehyde plus 0 . 1% Tween20 and spread onto poly-L-lysine-coated slides ( Fisherbrand Superfrost Plus ) . Slides were blocked with 4% non-fat dry milk in phosphate buffered saline for at least 30 minutes , and incubated overnight at 4°C with primary antibodies . Primary antibodies were mouse anti-Zip1 ( used at 1:1000 dilution ) , rabbit anti-Zip1 ( used at 1:1000 dilution; Santa Cruz y-300 SC-33733 ) , rabbit anti-MYC ( 1:400; Bethyl Laboratories A190-105A ) , mouse anti-MYC ( used at 1:1000 dilution; gift from S . Rankin ) , chicken anti-GFP ( used at 1:500 dilution; Millipore AB16901 ) , rabbit anti-DsRed ( used at 1:1000–1:2000 dilution; Clontech 632496 ) , and rabbit anti-RFP ( 1:500; Thermo Scientific 600-401-379 ) . Secondary antibodies were obtained from Thermo Fisher: Alexa Fluor 488-conjugated goat anti-chicken IgG ( used at 1:1200 dilution ) , Alexa Fluor 568-conjugated goat anti-mouse IgG ( 1:1000 ) , Alexa Fluor 647 conjugated goat anti-rabbit IgG ( used at 1:1200 dilution ) , and Alexa Fluor 568-conjugated goat anti-rabbit IgG ( used at 1:1000 dilution ) . Mtw1 ( an inner kinetochore protein ) foci ( Mtw1-13xMYC ) were quantified in spreads with an area of 15 μm2 or more to ensure centromeres were spread enough to assay . Centromere coupling would theoretically yield 16 kinetochore ( Mtw1 ) foci while complete absence of coupling would yield 32 kinetochore foci . All strains were spo11Δ/spo11Δ to block progression beyond the coupling stage [12 , 18] . The individual performing the scoring was blinded to the identity of the mutation . The average number of Mtw1 foci seen in the chromosome spreads of each in-frame deletion strain was compared to the values obtained from the ZIP1 and zip1Δ control strains , using the Kruskal-Wallis test , performed using Prism 6 . 0 . The statistical data for the experiment are reported in S2 Table . Non-disjunction frequencies of centromere plasmids were determined in a manner similar to previously published assays [7] . Plasmids were constructed with arrays of 256 repeats of the lac operator or tet operator sequence inserted adjacent to a 5 . 1 kb interval from chromosome III that includes CEN3 . These cells expressed a GFP-lacI hybrid gene under the control of a meiotic promoter and a tetR-tdTomato hybrid gene under the control of the URA3 promoter . This produced fluorescent foci at the operator arrays [35 , 36] . Cells were sporulated at 23°C ( rather than 30°C ) as this has been shown to allow by-pass of the pachytene arrests triggered by some ZIP1 mutations [29] . Even at this temperature cells with the zip1-C1 , zip1-C2 , zip1-NM2 and zip1Δ mutations mainly arrested in pachytene , so no anaphase segregation data were gathered for these strains . Harvested cells were either assayed fresh or were frozen in 15% glycerol and 1% potassium acetate until the time at which they were assayed . Preparation for assaying the cells included staining the cells with DAPI and then mounting the cells on agarose pads for viewing as described previously [38] . Anaphase I cells were identified by the presence of two DAPI masses on either side of elongated cells , indicating that the chromosomes had segregated . To avoid scoring cells with duplicated or lost CEN plasmids , only cells with one GFP focus and one tdTomato focus were assayed . Images were collected using the 100X objective lens of a Zeiss AxioImager microscope with band-pass emission filters , a Roper HQ2 CCD , and AxioVision software . Centromere pairing in pachytene was assessed using published methods [7] but with the centromere plasmids described above . Sporulation was done at 30°C . Chromosome spreads were prepared as described in [39] , with the following modifications: Cells were harvested 5–7 hours after induction of sporulation at 30°C . After chromosome spreads were created and dried overnight , the slides were rinsed gently with 0 . 4% Photoflo ( Kodak ) . Each slide was then incubated with PBS/4% milk at room temperature for 30 minutes in a wet chamber . Milk was drained off of the slide , and primary antibody diluted in PBS/4% milk was incubated on the slide overnight at 4°C . A control slide with PBS/4% milk was used for each experiment . The following day , the slides were washed in PBS , and incubated with secondary antibody diluted in PBS/4% milk for 2 hours in a wet chamber at room temperature . The slides were gently washed in PBS . DAPI ( 4' , 6-diamidino-2-phenylindole , used at 1μg/ml ) was added to each slide and allowed to incubate at room temperature for 10 minutes . Slides were then washed gently in PBS and 0 . 4% Photoflo , then allowed to dry completely before a coverslip was mounted . Antibodies are described in the previous section . Only cells that exhibited “ropey” DAPI staining were scored in this assay , and were disqualified for assessment if there was more than one GFP focus or more than one tdTomato focus . In these cells , the distance between the center of the green focus and the center of the red focus was measured using AxioVision software . The distributions of distances in the ZIP1 and zip1-N1 strains were determined to be significantly different with the Kolmogorov-Smirnov test ( Kolmogorov-Smirnov D = 0 . 4032; P = 0 . 0002 ) using the Prism 6 . 0 software package . As in previous work [7] , foci with center-to-center distances less than or equal to 0 . 6 μm were scored as paired ( these foci are typically touching or overlapping ) . The frequency of pairing ( distance less than 0 . 6 μm ) in the ZIP1 ( 32 of 50 ) and zip1-N1 ( 14 of 63 ) chromosome spreads was found to be significantly different ( p<0 . 0001 ) using Fisher’s Exact test performed with the Prism 6 . 0 software package . Chromosome spreads were prepared according to the protocol of Grubb and colleagues [39] as described above , and harvested from sporulation cultures five hours after placing cells in sporulation medium at 30°C . To visualize the axial elements ( Red1 ) and transverse elements ( Zip1 ) of the SC by indirect fluorescence microscopy , chromosome spreads were stained with following primary and secondary antibodies: guinea pig anti-Red1 antibody ( 1:1000 ) , goat anti-Guinea pig Alexa 488 antibody ( Invitrogen ) ( 1:1000 ) , and rabbit anti-Zip1 antibody ( 1:800 ) , donkey anti-rabbit Alexa 568 antibody ( Invitrogen ) ( 1:1000 ) . Chromosome spreads were imaged with a Deltavision OMX-SR structured illumination microscope ( SIM ) . Chromosome spreads were prepared according to the protocol of [39] as described above . All strains carried the zip4Δ to prevent SC assembly . Chromosomes were stained with primary antibodies: mouse anti-MYC ( Mtw1-13xMYC ) ( Developmental Studies Hybridoma Bank ) at 1:20 dilution and rabbit anti-Zip1 antibody at 1:1000 dilution and secondary antibodies Alexa 488 donkey anti-mouse ( Invitrogen ) at 1:1000 dilution and Alexa 568 goat anti-rabbit ( Invitrogen ) at 1:1000 dilution . Zip1 antibodies were raised against amino acids 611–875 from the carboxy terminus of Zip1 , shared by both wild-type Zip1 and Zip1-N1 proteins . The Zip1-GST fusion for antibody preparation was expressed from plasmid R1640 , generously provided by Shirleen Roeder [13] . Chromosome spreads were imaged with a Deltavision OMX-SR structured illumination microscope ( SIM ) . Acquired images were converted to binary images using ImageJ software and the number of overlapping Mtw1-13xMYC and Zip1 foci were scored using the ImageJ plugin , JACoP [30] . To determine whether co-localization occurred at frequencies that were significantly higher than expected for random overlaps , given the number of Mtw1 and Zip1 foci in each image , the foci in each image were randomized in one thousand simulations using Costes’ randomization in JACoP , then the frequency of random overlaps was determined and compared to the observed overlap frequency [30] . Costes’ P-value was then calculated to evaluate the statistical significance of the difference between the frequency of observed versus random overlap [31] . In addition , the average co-localization observed for all of the ZIP1 spreads ( 26 spreads , 238 Mtw1 foci , 33 co-localized with Zip1 ) and all of the zip1-N1 spreads ( 18 spreads , 279 Mtw1 foci , 12 co-localized with Zip1 ) was determined and the statistical significance of the difference determined using Fisher’s two-tailed exact test ( p = 0 . 0001 ) . The experiment presented is one of two performed , both with the same outcome ( significantly reduced Mtw1-Zip1 co-localization in the zip1-N1 mutant ) . The chromosome spreads used in the experiment above were used to assay the number of distinct Mtw1-13xMYC foci in ZIP1 , zip1-N1 and zip1Δ chromosome spreads . With complete pairing of the homologous chromosomes , the thirty-two kinetochores should appear as sixteen Mtw1-13xMYC foci . In the absence of pairing , kinetochores from the paired homologs can sometimes separate far enough to be resolved as individual foci ( the homologs remain tethered by crossovers and probably other constraints ) , thus giving higher numbers of Mtw1-13xMYC foci–in theory up to thirty-two foci . The SIM images described in the preceding section were converted to binary images using ImageJ software and the number of Mtw1-13xMYC foci tallied for each spread using the Analyze Particles function in ImageJ . The average number of Mtw1-13xMYC foci per spread was determined for each genotype ( ZIP1 , zip1-N1 , and zip1Δ ) and the statistical significance of the observed differences between the genotypes was calculated with one-way ANOVA and multiplicity adjusted P values were obtained with Sidak’s multiple comparisons testing using Prism 7 . 0 . To induce meiosis , diploid cells were grown in YP-acetate for 18 hours and then shifted to 1% potassium acetate sporulation medium at 1x108 cells/ml . Following transfer to sporulation medium , 0 . 5 ml of culture was harvested at 0 , 3 , 5 , 7 , 9 , 11 , and 24-hour time points . Cells were disrupted in cold 16 . 6% trichloroacetic acid and 0 . 5mm glass beads ( Biospec Products ) in a Bullet Blue Blender for 5 minutes at speed setting 8 following the manufacturer’s instructions . Protein precipitates were washed in cold 95% ethanol , pelleted in a microcentrifuge , and resuspended in SDS-PAGE buffer . Samples were boiled for 10 minutes and pelleted at 16 . 4 x g for 15 minutes at 4°C . Equal volumes of each sample were loaded onto 8% polyacrylamide gels in duplicate . For one gel , blotted proteins were detected using primary antibodies against Zip1 ( rabbit anti-Zip1 antibody raised against the C-terminal 264 amino acids of Zip1; used at a 1:3000 dilution ) and goat anti-Rabbit horse radish peroxidase ( HRP ) conjugated antibody ( Fisher PI31460; 1:5000 dilution ) . For the duplicate blot , Pgk1 was detected using mouse anti-Pgk1 ( Molecular Probes 22C5; used at a 1:3000 dilution ) and donkey anti-Mouse HRP secondary antibody ( Jackson 715-035-151 , 1:5000 dilution ) . Images were collected on an Azure c600 Imager . | The generation of gametes requires the completion of a specialized cell division called meiosis . This division is unique in that it produces cells ( gametes ) with half the normal number of chromosomes ( such that when two gametes fuse the normal chromosome number is restored ) . Chromosome number is reduced in meiosis by following a single round of chromosome duplication with two rounds of segregation . In the first round , meiosis I , homologous chromosomes first pair with each other , then attach to cellular cables , called microtubules , that pull them to opposite sides of the cell . It has long been known that the homologous partners become linked to each other by genetic recombination in a way that helps them behave as a single unit when they attach to the microtubules that will ultimately pull them apart . Recently , it was shown , in budding yeast and other organisms , that homologous partners can also pair at their centromeres . Here we show that this centromere pairing also contributes to proper segregation of the partners away from each other at meiosis I , and demonstrate that one protein involved in this process is able to participate in multiple mechanisms that help homologous chromosomes to pair with each other before being segregated in meiosis I . | [
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] | 2018 | A ZIP1 separation-of-function allele reveals that centromere pairing drives meiotic segregation of achiasmate chromosomes in budding yeast |
Hypoxia is an important physiological stress signal that drives angiogenesis , the formation of new blood vessels . Besides an increase in the production of pro-angiogenic signals such as vascular endothelial growth factor ( VEGF ) , hypoxia also stimulates the production of anti-angiogenic signals . Thrombospondin-1 ( TSP-1 ) is one of the anti-angiogenic factors whose synthesis is driven by hypoxia . Cellular synthesis of TSP-1 is tightly regulated by different intermediate biomolecules including proteins that interact with hypoxia-inducible factors ( HIFs ) , transcription factors that are activated by receptor and intracellular signaling , and microRNAs which are small non-coding RNA molecules that function in post-transcriptional modification of gene expression . Here we present a computational model that describes the mechanistic interactions between intracellular biomolecules and cooperation between signaling pathways that together make up the complex network of TSP-1 regulation both at the transcriptional and post-transcriptional level . Assisted by the model , we conduct in silico experiments to compare the efficacy of different therapeutic strategies designed to modulate TSP-1 synthesis in conditions that simulate tumor and peripheral arterial disease microenvironment . We conclude that TSP-1 production in endothelial cells depends on not only the availability of certain growth factors but also the fine-tuned signaling cascades that are initiated by hypoxia .
The growth of tumor depends on its surrounding vascular supply , which is commonly stimulated by the overexpression of tumor-secreted pro-angiogenic factors including VEGF [1] . Given the importance of the pro-angiogenic pathway downstream of VEGF , inhibiting the VEGF signaling axis has proven an effective therapy for patients with solid tumors and neovascular age-related macular degeneration; drugs such as bevacizumab and aflibercept which sequester circulating VEGF have shown efficacy in some of these clinical situations [1 , 2] . Research has also found that human and animals can produce various anti-angiogenic molecules . An example of a potent endogenous anti-angiogenic protein is thrombospondin-1 ( TSP-1 ) [3] . TSP-1 was the first protein identified as a naturally occurring inhibitor of angiogenesis . It is a large matricellular protein that interacts with various ligands and receptors , including components of the extracellular matrix , growth factors , cell surface receptors and cytokines [4] . One of the major pathways that TSP-1 negatively regulates to inhibit angiogenesis is the VEGF-VEGFR2 axis . It is reported that secreted TSP-1 binds to its high affinity receptor CD47 and disrupts the association of VEGFR2 with CD47 , thereby downregulating the pro-angiogenic signals downstream of VEGF; another mechanism proposed to explain the inhibitory effect of TSP-1 on VEGF-mediated angiogenesis involves the TSP-1 receptor CD36 and endothelial cell apoptosis pathways [5] . Although the detailed mechanism of action of TSP-1 as an anti-angiogenic protein is not fully understood , the potential of TSP-1 and its analogs as therapeutics against cancer has already been demonstrated by several preclinical and clinical studies [6–9] . The expression of TSP-1 in tumors is often found dysregulated . In some tumors with negative TSP-1 expression , tumor vascularity is significantly higher and this is associated with worse prognosis than patients with TSP-1 positive tumors [10 , 11] . Because of its strong anti-angiogenic effect , the role of TSP-1 in ischemic vascular diseases has also been investigated . Interestingly , in the plasma and tissue samples collected from patients with peripheral arterial disease ( PAD ) , TSP-1 is highly upregulated [12 , 13] . Hypoxia is also reported to increase TSP-1 synthesis in non-tumor conditions in various cell types including endothelial cells ( ECs ) , fibroblasts , renal tubular epithelial cells and vascular smooth muscle cells [14–17] . This effect may be parallel to the induction of VEGF in hypoxic conditions , suggesting a potential negative feedback loop that limits angiogenesis in certain conditions . Besides the direct intervention of TSP-1/VEGFR/CD47 interactions on the cell surface , another potential therapeutic strategy to harvest the anti-angiogenic potential of TSP-1 that is underexplored is the modulation of its intracellular synthesis [5] . In addition to the transcriptional regulation by promoters and repressors such as HIF-2α and Myc , TSP-1 expression is also tightly regulated by several microRNAs including miR-18a [14 , 18–20] . The HIF-let7-AGO1 pathway is shown to limit microRNA biogenesis in hypoxic conditions and is likely a contributing factor to the downregulation of miR-18a in hypoxia [21–23] . The abundance of miR-18a is also regulated by Myc while Myc expression is repressed by HIF-1 through multiple mechanisms [24 , 25] . Therefore , formulating and analyzing the signaling axis that connects HIF , Myc , microRNA and TSP-1 in hypoxia may provide insights into the complex dynamics of TSP-1 induction and help screen therapeutic strategies that can efficiently modulate TSP-1 synthesis to regulate angiogenesis . TSP-1 can activate the latent TGFβ ( transforming growth factor beta ) molecule , a multifunctional cytokine that plays a key role in inflammation , wound healing , cell proliferation and immune response [26] . The ligand TGFβ promotes the synthesis of TSP-1 via a positive feedback , possibly through downstream SMAD signals [27] . Another possible mechanism of how TGFβ mediates TSP-1 synthesis is through the influx of calcium upon TGFβ ligation and the subsequent calcium-mediated activation of NFATc1 ( nuclear factor of activated T-cells 1 ) which is found to be a TSP-1 promoter [28 , 29] . Summarizing both the hypoxic and TGFβ stimulation of intracellular TSP-1 , our mechanistic model presented in this study is the first computational model that considers pathway interactions between the different modes of TSP-1 regulation discussed above . Previous models of TSP-1 studied its interaction with receptors on the cell membrane or TGF-β in the extracellular matrix and paid minimal attention to the complex story of TSP-1 regulation within the cell , but we consider it very relevant to TSP-1 dysregulation in diseases [30 , 31] . Thus the focus of this work is restricted to hypoxia- and TGFβ-mediated pathways that regulate TSP-1 expression in ECs . We also explored the potential application of the model in more than one cell type , because of the fact that different groups of cells might be responsible for the synthesis of TSP-1 in different pathological conditions [32] . Assisted by the model , we have identified several key characteristics of intracellular TSP-1 regulation , focusing on the interactive signaling events during receptor activation and hypoxia , as well as the hierarchical regulation of TSP-1 mRNA orchestrated by different intermediate species and microRNAs . We also simulated the model under selected conditions that mimic certain protein profiles observed in tumors and PAD and tested different therapeutic interventions to restore the dysregulated TSP-1 expression back to baseline . The findings presented in this study should help design future experimental and computational research to further investigate the mechanistic regulatory networks that contribute to the abnormal TSP-1 expressions in cancer and in ischemic vascular disease .
The computational model presented in this study describes intracellular synthesis of TSP-1 in ECs under the control of multiple signaling axes ( Fig 1 ) . The detailed reaction networks are divided into two subparts , ( A ) intracellular TSP-1 regulation and ( B ) TGFβ activation of TSP-1 , and the diagrams are shown in Fig 2A and 2B . TGFβ pathways have been reported to play profound roles in cancer and cardiovascular diseases; in both situations , the anti-angiogenic effect of the downstream target TSP-1 can be harnessed therapeutically [33–35] . Established models of TGFβ signaling are available in the literature and they cover a wide range of biological details including TGFβ receptors , SMADs and phosphatases in different cellular compartments [36–38] . Due to model complexity concerns , the TGFβ signaling pathway in our model is an adapted version of the work by Nicklas and Saiz , where they included receptor binding , trafficking , SMAD activation , shuttling and feedback [39] . In addition , we implemented a different module of SMAD7-induced feedback and added the detail of SMAD7-mediated SMAD4 degradation , while SMAD4 is a co-SMAD that binds receptor-regulated SMADs ( R-SMADs ) [40 , 41] . Also , a component of the TGFβ-induced calcium signaling network is included in our model with a few rule-based reactions dictating the rate of calcium influx and outflux upon TGFβ activation ( see S1 Fig and S1 Table ) . Calcium binds and activates calmodulin and calcineurin sequentially , and activated calcineurin rapidly dephosphorylates the inactive NFATc1 in the cytoplasm [42] . Dephosphorylated NFATc1 is then shuttled into the nucleus and it promotes TSP-1 transcription; NFATc1 may be phosphorylated again and it aggregates in the cytoplasm in its inactive form [28 , 43] . The current model does not include the potential contribution of calcium to the TGFβ-dependent SMAD activations or the direct binding between calcium and TSP-1 [44 , 45] . It is important to note that although the signaling events downstream of TSP-1/receptor ligation are not covered in this model , they are reported to be the major effectors of the anti-angiogenic and pro-inflammatory properties of TSP-1 by regulating various molecules including but not limited to reactive oxygen species , Myc , nitric oxide , cyclic guanosine monophosphate ( cGMP ) and cyclic adenosine monophosphate ( cAMP ) [26 , 46–50] . The intracellular regulation of TSP-1 synthesis in the model is primarily driven by hypoxia , an important stress signal in tumors and in PAD , through multiple signaling cascades that connect to the HIFs . The oxygen sensing module is similar to the one described by Zhao and Popel , in which they included hydroxylation of HIF mediated by iron , 2-oxoglutarate , PHD ( prolyl hydroxylase domain-containing protein ) and FIH ( factor inhibiting HIF ) as key species and processes during HIF stabilization [22] . The mechanism of HIF-2α stabilization in hypoxia is similar to that of HIF-1α , but the HIF-2 dimer , compared to HIF-1 dimer , is suggested to be a more dominant activator of TSP-1 transcription [14 , 51 , 52] . Hypoxia-driven induction of HIF-1α promotes the transcription of let-7 , a hypoxia-responsive miR ( HRM ) , while the ability of HIF-2α to induce HRMs is similar to that of HIF-1α and thus is not included considering model complexity reduction [23] . Myc and tumor protein 53 ( p53 ) , whose expressions are shown to be affected by hypoxia , have been identified as upstream regulators of TSP-1 with opposing impacts [53 , 54] . Accumulated HIF-1α potently regulates the expression of Myc by directly promoting its degradation and inducing the MXI-1 ( MAX interactor 1 ) protein which downregulates the transcriptional activity of Myc [25] . We assumed that MXI-1 exerts opposite transcriptional activity with respect to Myc on all of its target genes in the model . Myc is considered a weak transcriptional repressor of TSP-1 [18] . Besides this direct interaction , the downregulation of Myc can significantly contribute to TSP-1 induction by upregulating Prosaposin ( PSAP ) which leads to increased expression of p53 , a positive promoter of TSP-1 transcription , and by downregulating the microRNAs that target TSP-1 mRNA [55 , 56] . HIF-1α accumulated in hypoxia represses the proteasomal degradation of p53 [57 , 58] . The microRNAs described in the model include let-7 and miR-18a . MicroRNA let-7 plays a master role in the regulation of AGO1 and Dicer which together strongly limit the global microRNA biogenesis in hypoxia [23 , 59 , 60] . Myc also negatively regulates the abundance of let-7 by inducing the Lin28B ( Lin-28 Homolog B ) protein which impairs the processing of let-7 primary transcripts in the nucleus [61 , 62] . The other microRNA , miR-18a , is included in our model to represent the few confirmed TSP-1-targeting miRs and it is reported to be a direct repressor of TSP-1 mRNA in ECs , colonocytes and cardiomyocytes [19 , 63 , 64] . It is found that expression of miR-18a strongly depends on the transcriptional activity of Myc , which might be part of an indirect mechanism in the Myc-mediated TSP-1 repression [65] . All the biochemical reactions involving the mechanistic activities of miRs follow the detailed miR biogenesis/targeting mechanisms modeled previously by Zhao and Popel [22] . The two model subparts converge on the gene transcription of TSP-1 , which depends on the activities of multiple transcription factors including HIF-2α , Myc , nuclear phosphorylated SMAD2-SMAD4 complex , nuclear active NFATc1 , and p53 in an multiplicative manner [14 , 18 , 27 , 28 , 55] . One potential connection between the intracellular TSP-1 regulation and the TGFβ activation of TSP-1 is through the activation of the TGFβ pathway which represses the activity of Myc [66] . In the model , the synthesis of Myc is regulated by the signal downstream of TGFβ activation , which is simplified as the nuclear phosphorylated SMAD2-SMAD4 complexes [67 , 68] . Another model assumption is that only the proteins/miRs located in the cytoplasm can undergo degradation , and the phosphorylation of SMADs takes place only in the cytoplasm . In microarray data that profile mRNA expression in C57BL/6 mouse with or without experimental hindlimb ischemia , TSP-1 and NFAT are among the top 5% most upregulated genes and MDM2 ( Mouse double minute 2 homolog , E3 ubiquitin-protein ligase ) , which promotes p53 degradation , is in the top 5% most downregulated genes in the ischemic group compared to the non-ischemic group; in addition , MYCT1 ( Myc target protein 1 ) , whose transcription is directly influenced by Myc availability , is also modestly downregulated in the ischemic group [69 , 70] . This evidence supports our model formulation hypothesis that NFAT , Myc and p53 are potential key players in the intracellular regulation of TSP-1 . The model contains over 100 species and nearly 200 parameters ( see S1 Table and Methods for details ) ; except the small portion of parameters whose values have been measured and calculated in previous studies , the rest of the parameters are estimated by conducting model optimization and validation against literature experimental data ( a total of 41 time-course expression trajectories of pathway signature molecules including over 200 data points ) . Model parameters are optimized as described in the Methods Section ( see S1 Table ) and model simulations are compared with experimental data obtained by different research groups . Valdimarsdottir et al . quantified the phosphorylated SMAD1 and SMAD2 in bovine aortic endothelial cells ( BAECs ) , pretreated with and without the protein synthesis inhibitor cycloheximide ( CHX ) , in response to 1 ng/ml TGFβ ( 4e-5 μM ) [39 , 71] . In the simulation , the protein synthesis rates of all species are set to zero to mimic the effect of CHX . Fig 3A–3D compare the model simulation with experimental data , and the results imply that CHX treatment prolongs the plateaus of phosphorylated SMAD1 and SMAD2 . Fig 3E compares the model-generated dose response curve of total phosphorylated SMAD2 with data obtained in BAECs [72] . Fig 3F–3L compare the model simulations of time-course protein expressions of various species including HIF-1α , HIF-2α , AGO1 , Dicer , p53 and TSP-1 with corresponding experimental data obtained in human ECs [14 , 16 , 23 , 59 , 73] . Both the experimental data and our model simulations show that HIF-1α , HIF-2α , p53 and TSP-1 protein expressions are induced in hypoxia while AGO1 and Dicer protein levels are downregulated . The simulated calcium and NFAT dynamics are compared to HUVEC ( human umbilical vein endothelial cell ) data in S1 Fig in which the simulated overall trend of NFAT activation following a single calcium transient mimics the experimental data [74] . To show that the basic EC model can be further modified to explain fibroblast data as a proof-of-concept analysis , additional model calibration using a different set of parameters optimized against experimental data obtained from fibroblasts are shown in S2 Fig . Given the novelty and the complexity of the model , validation is carried out in a way that the model simulations should reach qualitative agreements with uncalibrated experimental data obtained from a variety of different cell types ( ECs , cancer cell lines , etc . ) , in order to partially resolve the issue of model parameter uncertainties . We have gathered additional experimental data from literature on the expression profiles of pathway signature molecules and the comparisons are shown in Fig 4A–4O . Without further calibration against these data , our model that runs with the parameter set obtained from the optimization process discussed above produces simulations that are able to match the trends and relative expression changes of those key molecules with satisfying accuracy . The “test dataset” shown in Fig 4 and the results in S2 Fig together suggest that the dynamics of key molecules in our model are qualitatively consistent in ECs , fibroblasts and certain cancer cell lines . This proof-of-concept step serves as a concrete theoretical basis for future experimental validations of our experiment-based computational model . The SMAD proteins are the major effectors downstream of TGFβ . The R-SMADs , which typically refer to SMAD1/5 and SMAD2/3 , are represented by SMAD1 and SMAD2 in the model [41 , 80 , 81] . SMAD7 induction follows the activation of the TGFβ pathway , and it associates with the R-SMAD-receptor complex to prevent phosphorylation of these R-SMADs by internalized receptors ( Fig 5A ) . SMAD7 induction leads to a downregulation of SMAD4 in the cell by promoting its degradation ( Fig 5B ) [40] . In response to the rapid build-up of SMAD7 resulting from TGFβ receptor ligation , total SMAD4 level experiences an initial decay followed by a phase of slow restoration as TGFβ signal diminishes ( Fig 5B ) . The tail expression of phosphorylated RSMAD-SMAD4 after 20 hrs in Fig 5C and 5D is an outcome of the reduced inhibitory effect of SMAD7: when SMAD7 expression is reduced , some of the sequestered R-SMAD-receptor complex is freed and is able to re-initiate the activation signaling cascade . SMAD7 primarily exerts its inhibitory effect during the peak of TGFβ activation , so a block of its synthesis should enhance R-SMAD phosphorylation and prolong the R-SMAD activation signal following the peak ( Fig 5D ) . The tail expression of nuclear phosphorylated RSMAD-SMAD4 is not present when SMAD7 synthesis is inhibited due to the reduced binding between SMAD7 and R-SMAD-receptor complex . Given the dependency of TSP-1 promoter ( SMAD2 , NFATc1 ) activation on TGFβ-mediated signaling events , the TSP-1 synthesis curves produced by the model in response to different doses of TGFβ have similar trends compared to the time-course activation of R-SMADs , and the peak TSP-1 levels evaluated at around 10 hrs are shown to be dose dependent ( Fig 5E and 5F ) . Model simulations suggest that TSP-1 protein synthesis is significantly elevated compared to the baseline level at TGFβ doses greater than 1 ng/ml , which supports the experimental findings by Nakagawa et al [27] . The dynamic cooperation between transcriptional and posttranscriptional regulation of TSP-1 may be critical in its induction in response to hypoxia . Fig 6A shows the different TSP-1 induction profiles under different oxygen tensions . The increase in transcriptional activity gives rise to the increase in TSP-1 mRNA available for translation in hypoxia ( Fig 6B ) . Another factor that the model hypothesizes to have contributed to the high expression of TSP-1 in hypoxia is the repression of microRNAs ( e . g . miR-18a ) that target TSP-1 ( S3A Fig ) [19 , 21] . According to the simulations , downregulation of miR-18a in hypoxia is associated with a decrease in the production rate of miR-18a primary transcript due to repressed Myc expressions ( S3B Fig ) as well as decreased quantities of miR processing molecules , Dicer and AGO1 ( Fig 6C–6E ) . Less TSP-1 mRNA is under repression in hypoxia while more mRNA is ready for translation due to the increased transcription and decreased miR targeting ( Fig 6F ) . Since the stabilization of HIF is highly nonlinear with respect to oxygen tension and HIF initiates TSP-1 activation by multiple mechanisms , we wonder if there is also a switch-like behavior in the synthesis of TSP-1 at different oxygen tensions [82] . Fig 6G shows the normalized TSP-1 protein level as a function of percent oxygen , and the model predicts a nonlinear relationship when TSP-1 level is measured at both 24 hours and 48 hours; the threshold for induction of TSP-1 is centered around 6–8% oxygen . Since the model is constructed based on in vitro data , it should be noted that physiological tissue oxygen tension in vivo is usually much lower than 21% oxygen ( in vitro normoxia ) , and such a discrepancy may affect our model conclusions when compared with in vivo experimental observations [83] . Research on TSP-1 has established its promising role as future therapeutics in cancer and vascular disorders [85–88] . Computational studies such as our model may help design experiments to select the best strategy to modulate TSP-1 expression in these pathological conditions by running in silico experiments and assessing the results . In the following two subsections , we investigate how different factors contribute to TSP-1 dysregulation in tumors and in PAD and compare the efficacy of different model-motivated therapeutics . We performed global sensitivity analysis using the techniques of Partial Rank Correlation Coefficient ( PRCC , see Methods ) under different simulated conditions to identify parameters that most significantly control the key species in the model [112] . S6 Fig displays the distribution of model parameters and the corresponding experimental measurements [113 , 114] . Most of the parameter values after optimization are within one-two orders of magnitude compared to the experimental median values . Certain parameter values that deviate significantly from the experimental median are calculated based on literature data , such as the constitutive degradation rates of TGFβR ( 0 . 0278 min-1 ) in S6B Fig and the calmodulin concentration ( 5 . 9371 μM ) in S6D Fig [36 , 115] . From the sensitivity analysis , we observed that the HIF-1 dimer level , an indicator of HIF-mediated transcriptional activities in hypoxia , is negatively regulated by an increase in the affinity between HIF-1α and its two hydroxylases , FIH and PHD , which will subsequently promote HIF-1α degradation; as expected , increased binding between oxygen and FIH/PHD-DG-Fe complex ( parameters kf4 , kf8 ) speeds up the degradation of HIF ( Fig 9A ) . Interestingly , although increased dimerization between HIF-1α and HIF-1β ( parameter kf19 ) increases HIF-1 dimer levels , it downregulates the total HIF-1α protein level within the cell , presumably due to a higher synthesis of the HIF-destabilizing protein TTP ( parameter kf18 ) as described by previous studies ( Figs 9A and S7A ) [116 , 117] . Phosphorylation rate of cytoplasmic SMAD2 ( parameter kf75 ) and SMAD4 shuttling rate ( from cytoplasm to nucleus , parameter kf79 ) are the two most influential factors that positively regulate the levels of active , phosphorylated SMAD2-SMAD4 complex in nucleus , which represents the signaling strength of TGFβ pathways ( Fig 9B ) . Besides the rates relating to SMAD7 feedback , increases in other factors such as the binding between TGFβ and its receptor ( kf73 ) and the degradation of SMAD4 ( vm34 ) are both correlated with less total activation of R-SMADs ( Fig 9B ) . Sensitivity analysis of factors that control TSP-1 synthesis indicate that parameters relating to the abundance of transcription factors , including Myc , p53 , HIFs , and NFAT are more influential ( Fig 9C and 9D ) . Besides the strategies of TSP-1 or HIF gene therapies ( parameters vm1 , vm2 , vm20 ) , the model suggests that small molecule inhibitors against Myc and miR-18a can effectively restore and enhance TSP-1 protein expressions in tumorigenic conditions provoked by Myc overexpression ( Fig 9C ) . In Fig 9D , manipulating the expression of miRs ( let-7 or miR-18a alone ) in simulated CLI conditions modulates TSP-1 production to a lesser extent compared to the approaches that directly target the transcription factors , and the results in Fig 8E also support the conclusion derived from the sensitivity analysis that targeting NFAT and p53 together may be a more efficient strategy . Additional results ( S7 Fig ) of model sensitivity in simulated conditions different from the ones presented in Fig 9 are consistent with the results discussed here . Given the potential interactions between different transcription factors ( e . g . NFAT , HIF , p53 , Myc ) at the DNA level and the limited knowledge on how the influence of each individual promoter/inhibitor converge during TSP-1 transcription , the conclusions from the sensitivity analysis are biased by the simplification we made when translating the complex transcriptional activities into mathematical equations . The fact that our model assumed a multiplicative effect of different transcription factors is reflected by the results in Fig 9C and 9D that the TSP-1 protein level is relatively more sensitive to the parameters that control the abundance of its transcriptional promoters/repressors .
We constructed the model based on ordinary differential equations ( ODE ) with a total of 109 species , 195 kinetic parameters and 138 reactions ( Fig 2 ) . Description of reactions , parameter values ( S1 Table ) , and initial conditions for all species ( S2 Table ) are available in the appendices . The model allows translocation for certain species , especially the receptor and SMAD complexes , and distinguishes them by cellular locations–in cytoplasm , nucleus or endosome , since their functions are different in different cellular compartments . Transcriptional activation/repression and Dicer cleaving are modeled as Hill-type or Michaelis-Menten kinetics . Most interactions captured by the model are based on literature evidence . All data including reactions , rates , rules and initial conditions used in the model are compiled using MATLAB SimBiology toolbox ( MathWorks , Natick , MA ) . Simulations are performed using the ode15s and sundials method , which are both ODE solvers provided in MATLAB . Since hypoxia is a focus of the study , the initial conditions of all species are their respective steady-state levels in simulations assuming normoxia ( 21% O2 ) and no TGFβ treatment . For miR treatments simulated by the model , overexpression of the miR mimic increases the initial condition of the corresponding precursor miR; miR silencing is described as the association of miR antagonist with miRISC to form a complex that cannot function . Our ODE-based computational model inherently considers time delays in biological events and is designed to simulate the average dynamical behavior of different biomolecules considering the stochasticity of cellular activities ( binding , transcription , etc . ) , given the reasons stated in one of our previous works [22] . Although it is suggested that stochasticity plays a critical role in gene transcription , many signaling network studies that used deterministic approaches to model transcriptional events have been able to generate insightful results that are further validated by experiments [118–123] . Another reason why we did not use the stochastic approach to model transcription is that our study focuses primarily on the dynamic signal transduction and pathway cooperation within the network that together contribute to the induction/inhibition of TSP-1 protein expression in different circumstances , instead of the details in the transcription factor binding process at the DNA level . ImageJ software ( NIH ) is used to perform densitometry analysis according to the blot analysis protocol in order to obtain the experimental data showed in the model optimization and validation sections . Due to the limited literature on miR and TSP-1 modeling and the fact that this model is the first that describes the complex regulation responsible for TSP-1 synthesis under different physiological conditions , we paid considerable attention to parameter estimation and optimization during model construction . Many of the rate parameters and initial conditions used in the TGFβ signaling subpart are taken from the work by Nicklas and Saiz in which they calculated the values based on experimental measurements [39] . Parameters used in the component describing calcium-mediated NFAT activations are estimated and then optimized to reproduce the qualitative experimental behaviors of calcium and NFAT observed in ECs [74] . Intracellular concentrations of calcium are estimated based on data from [124] . For the initial conditions of miRs , we compared literature data and assumed that miR levels are on the order of 103 to 104 copies per cell in normoxia; the concentrations ( in microMolar ) used as initial conditions in the model are computed using 1 pL cell compartment volumes based on literature measurements [125–128] . Absolute levels of the different proteins in the model are estimated to be on the order of 104 to 106 copies per cell based on experimental measurements of several pathway-related proteins including Myc , p53 and calmodulin [115 , 129 , 130] . We estimated the decay rates of mRNA ( 1 . 2e-3 min-1 ) , miRNA ( 1e-4 min-1 ) , protein ( 2 . 5e-4 min-1 ) , translation rate per mRNA ( 2 . 33 min-1 ) , transcription/mRNA synthesis rate ( 1 . 92e-7 μM/min ) , and the levels of mRNA ( 2 . 8e-5 μM ) and protein ( 0 . 08 μM ) in normoxia so that the final values are within ±2 orders of magnitude compared to the median values ( normalized by cell compartment volumes , and indicated in the brackets ) reported by global quantification studies [22 , 113 , 114 , 131 , 132] . The rest of the parameters and initial conditions are estimated based on previous computational studies ( summarized in S1 and S2 Tables ) [22 , 39 , 84] . The volume concentrations of surface TGFβR are calculated by assuming that the receptors are distributed uniformly within a space of 1 pL given an estimated flat EC surface area of 1000 μm2 [133 , 134] . We used the Levenberg-Marquardt algorithm within the lsqnonlin function in MATLAB for model optimization . Since the related time-course data in ECs are limited , the parameters are optimized by minimizing the sum of squared errors between normalized model simulations and experimental measurements ( see Fig 3 in Results for details ) . The same protocol is repeated in the optimization of the model against the fibroblast dataset . Global sensitivity analysis is performed using the PRCC algorithm , a sampling-based method developed by Marino et al . to quantify uncertainty in the model . The outputs of interest in the sensitivity analysis are the time integrals of the signals computed in the form of AUC over certain durations , and a sample size of 1000 runs is chosen for each module of sensitivity analysis . The distribution of each parameter tested is within a two orders of magnitude range with a center at the parameter’s original value ( e . g . x/10 to 10x ) . Details and examples of the PRCC algorithm can be found in [112] .
In this study , a detailed mass-action based computational model of multiple signaling pathways connecting to TSP-1 regulation is presented . The comprehensiveness and trustworthiness of the model is supported by a careful analysis of literature during model formulation and extensive efforts of model training/validation against experimental data . This work is a continuation of a previous model presented by our group , while in that model VEGF is the major focus [22] . The scope of the current model is not limited to intracellular signaling since we included the module of TGFβ/receptor signaling as an important path of TSP-1 activation . TSP-1 is long known to be an activator of TGFβ , but the potential role of TGFβ on TSP-1 activation has not received much attention [135] . The model connects independent literature evidence and hypothesizes both a direct and indirect TSP-1 activation path initiated by TGFβ stimulation via SMADs and calcium regulation . This potential positive feedback loop that amplifies both TGFβ1 and TSP-1 expression might be an explanation to the paired high TGFβ1 and TSP-1 levels observed in certain pathological conditions [28 , 136] . Although the regulatory roles of TSP-1 in tumor progression is highly cell-type specific , the undesirable anti-angiogenic effect resulting from high TSP-1 expression in PAD is a major interest to cardiologists and vascular biologists [107 , 137 , 138] . Our model proposed that TGFβ might be an underlying factor driving the high expressions of TSP-1 in PAD patients , given the experimental evidence of TGFβ1 elevation in ischemic tissues [106 , 139] . It is interesting to note that hypoxia also upregulates TGFβ1 production in smooth muscle cells in addition to the direct transcriptional induction of TSP-1 via HIFs , suggesting another layer of crosstalk between the pathways that control TSP-1 expression [14] . The biology of hypoxia-induced TSP-1 seems contradictory to the need of angiogenesis when cells are exposed to insufficient oxygen , however , this phenomenon may be more likely an endogenous feedback control developed by the body to contain the angiogenesis driven by pro-angiogenic factors ( e . g . VEGF ) that are radically produced upon hypoxia [140] . The potential therapeutic interventions tested in this study to enhance TSP-1 production in simulated conditions of tumors are based on the assumption that these tumors are induced by Myc hyperactivity . Given the profound role of Myc in growth , proliferation , tumorigenesis and stem cells , the focus of our study , TSP-1 , is only one of the many potential downstream targets of Myc that have correlations with tumor progression [92] . It is worth noting that Myc can induce miR-17/92 cluster which targets key proteins in TGFβ signal transduction and represses gene regulation downstream of TGFβ in multiple cancer cell lines , and that the pro-tumorigenic property of Myc overexpression is lost in TGFβ-deficient xenograft models of colorectal cancer; such evidence suggests that Myc may promote tumor growth primarily by repressing the anti-tumorigenic gene expression ( including TSP-1 ) activated by TGFβ signaling , at least in the context of colorectal cancer [141 , 142] . Moreover , the mutually inhibitory relationship between TSP-1 and Myc may further amplify the signal of one molecule and suppress the other in diseases [95] . VEGF is also shown to be a target activated by Myc [143 , 144] . Although research has shown that TSP-1 overexpression can effectively reduce tumor metastasis , the position of TSP-1 in the entire network of cancer-related genes is relatively downstream , which might imply that targeting TSP-1 to attack tumor may be less efficacious than targeting the genes ( e . g . Myc ) that are more central in the network , since cancer is notorious for developing compensatory pathways to resist targeted therapies [145 , 146] . The failure of TSP-1 analog ( ABT-510 ) in phase II trials against metastatic cancer should not discourage the continuum of research that aims to explore the therapeutic potential of TSP-1 , especially in cardiovascular diseases where its importance has emerged in recent years; on the other hand , multiple phase I studies that explore the targeting of CD47 in cancer , given its inhibitory effect on the immune response , are now under way [147–150] . Still , our simulations proposed that increased TSP-1 synthesis is a possible downstream effector of the tumor-suppressive property of TGFβ signaling , specifically in Myc-dependent tumors; however , the exact role of TGFβ in cancer is quite complex and controversial given its bipolar control of tumorigenesis [151–155] . Sensitivity analysis indicates that TSP-1 production stimulated by hypoxia and TGFβ is strongly influenced by the activity of several transcription factors , namely HIFs , p53 and NFAT . Although the model assumes that HIF-1 does not directly promote TSP-1 transcription , an increase in its abundance , as shown by the sensitivity analysis , has a notable influence on TSP-1 levels comparable to that of HIF-2 , which directly activates TSP-1 [14] . The indirect activation of TSP-1 by HIF-1α might be undesired for gene therapies that use adenoviral HIF-1α to improve angiogenesis and limb perfusion in patients with ischemic vascular diseases [156] . This might also explain the finding that the angiogenic potency of adenoviral HIF-1α is significantly lower than that of adenoviral VEGF [157] . To date , the roles of p53 and NFAT , the two potential therapeutic targets identified by our simulations , in PAD are largely unknown . The limited evidence in the literature agrees with the model hypothesis that NFAT , with TSP-1 as one of its effector molecules , potently participates in the cellular response to hypoxia/ischemia: inhibition of NFAT is found to suppress atherosclerosis in diabetic mice , while a significant increase in NFAT expression is observed in ischemic rat brain [158 , 159] . Tumor protein p53 is long known to be a critical factor in suppressing tumorigenesis and initiating apoptosis; its pro-apoptotic property might render it a promising target in PAD given multiple clinical observations of the increased level of apoptotic events in the serum and tissue of PAD and CAD ( coronary artery disease ) patients [160–163] . Still , the robustness and reliability of our model-based conclusions can be further enhanced by additional model training , calibration and validation when more experimental measurements ( e . g . data of HIFs , miRs , AGOs , Myc , SMADs and TSP-1 expressions in different physiological condition/stimulation ) become available in the near future . The current model describes the dynamics of let-7 and miR-18a in intracellular regulation of TSP-1 , but the model is set up in a way that incorporating additional miRs and their targets is feasible . Besides the miRs that target HIFs such as miR-155 , many other miRs could be potential candidates to consider for future computational models of TSP-1 regulation [164] . In the current model simulations , we assume that inhibition of p53 is achieved by the binding of small molecule inhibitors ( e . g . Cyclic Pifithrin-α ) , while p53 is reported to be a target of several miRs including miR-125b and miR-504 [165–167] . The p53 protein also regulates the expression of certain miRs; an example is miR-194 , a p53-responsive miR which targets TSP-1 in colon cancer cell lines [56] . Liao et al . identified let-7g , one of the HRMs , as a factor that improves endothelial functions with targets including TSP-1 [168] . Future models could also consider alternative pathways relating to TSP-1 regulation that have implications in vascular disorders , such as the axis involving VEGF activation of NFAT in ECs [169] . The signaling pathway involving PI3K/AKT/PTEN is also shown to mediate TSP-1 expression in both cancer cells and ECs [170] . Including the VEGF signaling pathway as a part of intracellular TSP-1 regulation seems to be an exciting next step in the future development of our model , since most studies have focused on the regulatory effect of TSP-1 on VEGF but not the other direction . Besides hypoxia , many other factors relating to cancer and PAD including radiation , high glucose and aging have also been shown to affect TSP-1 expression [171–176] . So far , the model is mostly formulated and validated based on knowledge and data of pathways in ECs , which are shown in experimental studies to express TSP-1 at high levels and that the EC-secreted TSP-1 is critical in certain physiological processes [28 , 177] . Given the fact that the amount of ECs is only a small percentage of all the cells in tissues of tumors or PAD , and other types of cells including smooth muscle cells , stromal fibroblasts and immune cells also secrete TSP-1 , there is a need for further model validation in order to sustain and extend our model conclusion , at least qualitatively , to other cell types of interest [26] . We have already demonstrated the feasibility of this by conducting additional validation against experimental data from other cell types ( e . g . cancer cells , fibroblasts etc . ) , but the related data in smooth and skeletal muscle cells are relatively scarce . A goal of this study is to raise carefully-formulated hypotheses that stimulate future research to produce additional experimental results that either corroborate or refute our predictions . In summary , our model is the first computational study that investigates the complex network of intracellular TSP-1 regulation mediated by hypoxia , microRNA-targeting and receptor signaling . It is an important complementary study to the active research that focuses on the interaction between TSP-1 , VEGF and their receptors at the cell surface , and it also provides insights , from the perspective of intracellular control , into the search for therapeutic strategies that adjust TSP-1 activity in order to modulate angiogenesis in cancer and vascular diseases . Combined with additional modules of pharmacokinetic analysis , our computational model can help identify optimal treatment strategies and design appropriate dosing schemes that progressively reduce or enhance TSP-1 expression in patients depending on the specific indication . | Research evidence show that thrombospondin-1 ( TSP-1 ) is an anti-angiogenic protein which potently inhibits the downstream signaling of vascular endothelial growth factor receptor 2 ( VEGFR2 ) , an important pathway that promotes endothelial cell proliferation , migration and permeability . As demonstrated by numerous studies , expression of TSP-1 is often upregulated in peripheral arterial disease ( PAD ) and downregulated in many solid tumors , primarily because of its inhibitory effect on angiogenesis and tumor growth . Given the established anti-angiogenic property of TSP-1 and its dysregulation in diseases , it holds great value to design novel therapeutic strategies that aim to restore TSP-1 expression in tumors and limit its expression in PAD by regulating the biomolecules that control TSP-1 synthesis . The computational , mechanistic , experiment-based model of TSP-1 intracellular regulation presented here is a solid integration of the current knowledge and is substantially validated against published data . Our model simulations reproduce the experimental time-course dynamics of key proteins within the regulatory network and suggest interesting behavior in hypoxia- and cytokine-driven regulation of TSP-1 . In addition , we assess different model-based strategies to modulate TSP-1 synthesis in silico; our model is the essential module of an integrated computational platform that can provide research insights for future investigations of TSP-1 and angiogenesis . | [
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] | 2017 | Transcriptional and Post-Transcriptional Regulation of Thrombospondin-1 Expression: A Computational Model |
3MC syndrome is an autosomal recessive heterogeneous disorder with features linked to developmental abnormalities . The main features include facial dysmorphism , craniosynostosis and cleft lip/palate; skeletal structures derived from cranial neural crest cells ( cNCC ) . We previously reported that lectin complement pathway genes COLEC11 and MASP1/3 are mutated in 3MC syndrome patients . Here we define a new gene , COLEC10 , also mutated in 3MC families and present novel mutations in COLEC11 and MASP1/3 genes in a further five families . The protein products of COLEC11 and COLEC10 , CL-K1 and CL-L1 respectively , form heteromeric complexes . We show COLEC10 is expressed in the base membrane of the palate during murine embryo development . We demonstrate how mutations in COLEC10 ( c . 25C>T; p . Arg9Ter , c . 226delA; p . Gly77Glufs*66 and c . 528C>G p . Cys176Trp ) impair the expression and/or secretion of CL-L1 highlighting their pathogenicity . Together , these findings provide further evidence linking the lectin complement pathway and complement factors COLEC11 and COLEC10 to morphogenesis of craniofacial structures and 3MC etiology .
3MC syndrome ( MIM 257920;265050;248340 ) is a unifying term amalgamating four rare autosomal recessive disorders with overlapping features namely; Mingarelli , Malpuech , Michels and Carnevale syndromes . 3MC syndrome is characterized by facial features including hypertelorism , cleft lip/palate , high-arched eyebrows , craniosynostosis , developmental delay and hearing loss [1–3] . We previously reported that mutations in COLEC11 and MASP1/3 genes were responsible for several cases of 3MC syndrome [4] . Since then , further novel mutations in MASP1/3 and COLEC11 have been reported in 3MC patients [5–7] . COLEC11 and COLEC10 encode CL-K1 ( also known as CL-11 ) and CL-L1 ( also known as CL-10 ) respectively , members of the collectin family with an N-terminal collagen-like domain linked to C-terminal carbohydrate-recognition domains ( CRDs ) . CL-K1 and CL-L1 are able to bind to microorganisms including bacteria , fungi and viruses , through their CRDs . This binding capacity to antigens , followed by their interaction with MASP proteins , is their main role in lectin complement pathway activation . [8–12] . However the pathogenic mechanism of lectin complement related proteins in 3MC syndrome is not yet understood [4] . CL-K1 and CL-L1 can also work in partnership in complement activation [13] . Usually CL-K1 and CL-L1 form homodimers , as is generally the case with CDR-domain containing proteins but CL-K1 and CL-L1 can form CL-K1/CL-L1 ( also known as CL-LK ) heterodimers in plasma and in vitro . These CL-LK heterodimers can also interact and form complexes with MASP-1 , MASP-2 and MASP3 [10] . MASP1/3 encodes for 3 alternative products MASP-1 , MASP-3 and MAp44 [14] . MASP-1 collaborates with MASP-2 to activate C4 . MAp44 has the MASP1 H domain truncated and inhibits MASP1 and MASP2 complement activation . MASP3 shares H chain domain with MASP1 and have a unique protease domain . The precise role of MASP-3 in complement signalling is still unclear , but it has been proposed to form a complex with CL-LK and MASP-2 [10] . It remains to be determined whether these interactions play a role in embryological development , perturbation of which gives rise to the diverse morphological features of 3MC syndrome . Recently it has been shown that 3MC mutations in COLEC11 inhibit secretion of CL-K1 in mammalian cells , reducing the normal serum levels of CL-K1 and probably disrupting interaction with MASPs or CL-L1 [15] . Another report describes how three exonic polymorphisms in COLEC11 and COLEC10 also have an effect in reducing levels of circulating CL-K1 and CL-L1 in serum [16] . Those findings hint how mutations and polymorphisms in both COLEC11 and COLEC10 , can directly affect CL-K1 and CL-L1 secretion . The skeletal phenotype of 3MC patients is the result of complex embryological processes , including neural crest cell ( NCC ) induction , migration , morphogenesis and differentiation [17] . Correct migration of cNCC is essential for the formation of many tissues in the head from cartilage and bones to muscle and ganglia [18–21] . The regulation and control of NCC migration is complex involving multiple genetic pathways including Wnt , Shh and transcription factors such as Hox and Dlx genes [18 , 22 , 23] . Complement factors , such as C3a , have been recently established to play a role in NCC cohesion during migration . Mayor and collaborators have established how complex collective cell migration of NCC requires complement proteins . For example , C3a and its receptor C3aR work together to co-attract each other in order to maintain the coordinated migration of NCC [24–26] . In the present study we describe mutations in a novel lectin alternative pathway gene , COLEC10 , in 3MC patients , adding to the body of evidence implicating the complement pathway in human development . We also present new COLEC11 and MASP1/3 mutations found in our cohort of 3MC patients . To validate COLEC10 mutations as causative of 3MC syndrome we determine its expression pattern in the developing mouse embryo and we further demonstrate the in vitro functional consequences of COLEC10 mutations , and present evidence that CL-L1 act as a cellular chemoattractant . Finally we propose a pathogenic mechanism for 3MC relating to the failure of CL-L1 function and its developmental consequences in 3MC .
We collected a bank of patient DNA samples comprising diagnoses of Carnevale , Mingarelli , Michels and Malpuech syndromes . Our cohort currently consists of 45 3MC families of Asian , Middle Eastern and European origin . We previously demonstrated that mutations in COLEC11 and MASP1/3 lectin complement pathway related genes are causative of 3MC syndrome in 11 families and 16 patients . Therefore , we screened for COLEC11 and MASP1/3 mutations by Sanger sequencing in the remaining 34 families and 36 patients in this heterogenous group of patients . We found three novel homozygous mutations in COLEC11 ( NM_024027 . 4 ) in three patients and a single homozygous mutation in MASP1/3 ( NM_139125 . 3 ) in one patient ( see Table 1 and Fig 1A and 1B ) . Of these , two patients were from consanguineous families; MC35 . 1 ( Pakistani ) and MC37 . 1 ( Somalian ) . Both harbored non-synonymous homozygous mutations in COLEC11 leading to a predicted premature termination codon , c . 309delT ( p . Gly104Valfs*29 , exon 4 ) and a predicted damaging missense , c . G496A ( p . Ala166Thr , exon 6 ) respectively . For patient M35 . 1 we sequenced the parents , demonstrating that the mutations segregated with the disorder . Parental samples were not available for patient MC37 . 1 . We found in patient MC29 . 1 a deletion of 10 nucleotides in COLEC11 ( c . 89_98delATGACGCCTG , exon 2 ) which predicts a frameshift change and the introduction of a premature stop codon ( p . Asp30Alafs*68 ) . None of the COLEC11 mutations was present in the Exome Aggregation Consortium Database ( ExAC ) , ( Cambridge , MA URL http://exac . broadinstitute . org ) . Overall , two of the new COLEC11 mutations lead to premature terminations ( p . Gly104Valfs*29 and p . Asp30Alafs*68 ) , or the missense mutation p . Ala166Thr . This last missense change lies , within the CRD , as shown in Fig 1A , and probably disrupts its recognition function . In our 3MC cohort we also found a new mutation affecting the second previously described gene mutated in 3MC , MASP1/3 ( NM_139125 . 3 ) . Patient MC27 . 1 , with a consaguinous family , presents a homozygous nonsense mutation ( c . 9G>A ) leading to premature truncation of the protein recently been reported by [6] . These results corroborate our previous finding that genes involved in the lectin complement pathways cause 3MC . However , mutations in COLEC11 and MASP1/3 were excluded in the remaining 30 families and 32 patients . Therefore , we performed whole exome sequencing ( WES ) in six 3MC patients from consanguineous families , without mutations in COLEC11 or MASP1/3 , in order to identify new causative gene associations . We found one patient diagnosed with Michels syndrome harbouring deletions in COLEC10 ( NM_006438 . 4 ) , another member of the collectin family . Despite parental consanguinity in this family , we discovered that the proband , MC19 . 1 harboured compound heterozygous mutations , c . 25C>T; p . Arg9Ter in exon 1 and c . 226delA; p . Gly77Glufs*66 in exon 3 . We confirmed these mutations segregated with disease by Sanger sequencing ( Table 2 and Fig 1D and 1E ) . The affected sibling , MC19 . 2 , also harboured the same compound heterozygous mutations in COLEC10 . Next we Sanger sequenced COLEC10 in the remainder of our patient cohort . These patients were previously screened for COLEC11 and MASP1/3 mutations , with none identified . We identified another patient ( 25 . 1 ) with the p . Arg9Ter COLEC10 mutation accompanied by a new missense mutation c . 528C>G , p . Cys176Trp ( exon6 ) in the other allele ( Table 2 and Fig 1D and 1E ) . The unaffected sibling or parents were not available for testing , therefore we cannot conclusively state that both mutations in patient 25 . 1 could be in -cis . The p . Gly77Glufs*66 mutation is not present in the ExAC database and p . Cys176Trp ( position Chr8:120118124 C / G , not found in dbSNP ) has a frequency of 1 in 120850 chromosomes in the same database . The p . Arg9Ter mutation ( rs149010496 ) is present in only 4 alleles out of 121220 ( ExAC ) . Collectively , these data strongly support the notion that pathogenic mutations in COLEC10 cause a subset of 3MC diagnoses . COLEC10 mutations c . 25C>T; p . Arg9Ter and c . 226delA; p . Gly77Glufs*66 both lead to early termination and are likely to produce either truncated proteins or undergo non-sense mediated decay . However , the missense mutation p . Cys176Trp lies in the CRD domain of CL-L1 ( Fig 1C ) , affecting a cysteine residue Cys176 that forms a disulphide bond with C270 [9] and is predicted by PolyPhen-2 to be damaging ( http://genetics . bwh . harvard . edu ) . We next used the SWISS-MODEL Workspace application ( http://swissmodel . expasy . org ) to predict how the p . Cys176Trp mutation might affect the secondary structure of the CL-L1 protein . Residue 176 on the second helix-loop-helix domain of the protein is predicted to change the tridimensional structure of the protein ( Fig 1F ) , probably affecting the C-type lectin domain function . Table 3 shows detailed clinical features for all of described patients . To further characterise the function of COLEC10 we assessed intracellular localisation of CL-L1 in ATDC5 cells , a murine chondrocyte cell line . Consistent with previous results for COLEC11 [4] , we observed expression of CL-L1 in the Golgi apparatus consistent with a secreted peptide , colocalising with the TGN marker 58K , and with cytosolic expression ( Fig 2A ) . We also found CL-L1 colocalised with laminin , a major component of the basal lamina ( Fig 2B ) . This expression is similar to the cellular colocalisation we found between CL-K1 and laminin ( Fig 2C ) . Next , we analysed the expression of CL-L1 during murine craniofacial development . We detected CL-L1 expression in the epithelium and mesenchyme of the palate shelf and jaw in E18 . 5 embryos ( Fig 2D ) . Moreover , we found by immunofluorescence that this particular mandibular epithelial expression is present as early as E13 . 5 , revealing coexpression between CL-L1 and laminin , where CL-L1 is clearly visible in the basement membrane in the palate area ( Fig 2E ) . We investigated the ability of CL-L1 to act as a chemoattractant in the context of human cells . We spotted 1% ( w/v ) low melting point agarose discs mixed with PBS , BSA or recombinant human CL-L1 . As reported previously , when the same experiment was performed for CL-K1 [4] , cells were observed to invade the protein-containing agarose disc . To quantify this effect , we calculated the cell invasion index as shown in Fig 3A . We found that PBS and BSA containing discs failed to attract any cells ( Fig 3B and 3C and S1 and S2 Movies respectively ) which was in stark contrast to CL-L1 containing discs that exhibited extensive migration/invasion into the discs with an invasion index score of 140 . 0±22 . 9 ( Fig 3C and S3 Movie ) . Having demonstrated a role for CL-L1 in normal craniofacial development we sought to confirm that the mutations found in our 3MC patients were pathogenic . We predicted that COLEC10 mutations c . 25C>T; p . Arg9Ter and c . 226delA; p . Gly77Glufs*66 would lead to either truncated or absent protein . However , we expected that the missense mutation c . 528C>G , p . Cys176Trp , affecting a crucial cysteine residue , would likely lead to abnormal protein folding and possibly affects secretion , as seen with three disease-associated mutations in COLEC11 [15] . To test this hypothesis , we transfected COLEC10WT , COLEC10Arg9Ter and COLEC10Gly77Glufs*66 constructs into HeLa and HEK293 cell lines and detected CL-L1 expression . Immunoblotting demonstrated that CL-L1 protein was present in both cell extracts and supernatants when COLEC10WT plasmid was transfected into HEK293 cells . By contrast , no protein was detected when the mutant plasmids COLEC10Arg9Ter and COLEC10Gly77Glufs*66 were transfected , suggesting that both transcripts underwent nonsense-mediated decay . Transfection of COLEC10Cys176Trp plasmid allowed CL-L1 expression but not secretion as demonstrated by detection of CL-L1 in the cell lysates but not in the supernatant ( S1 Fig ) . Western blot data were further supported by quantitative ELISA ( Fig 3D ) . The results showed highest levels of CL-L1 protein in pellets of cells transfected with COLEC10Cys176Trp plasmid than cells transfected with COLEC10WT ( HeLa COLEC10Cys176Trp 2518 . 3±21 . 3ng/mL vs HeLa COLEC10WT 1823 . 3±7 . 2ng/mL , p<0 . 001; HEK293 COLEC10Cys176Trp 1302 . 7±3 . 7ng/mL vs HEK293 COLEC10WT 632 . 0±3 . 6ng/mL , p<0 . 001 . Fig 3D and S1 Table ) . However , secretion of CL-L1 was severely reduced in the COLEC10Cys176Trp transfected cells compared with COLEC10WT supernatant transfections ( HeLa COLEC10Cys176Trp 12 . 5±0 . 2ng/mL vs HeLa COLEC10WT 200 . 3±1 . 5ng/mL , p<0 . 001; HEK293 COLEC10Cys176Trp 5 . 7±0 . 1ng/mL vs HEK293 COLEC10WT 390 . 2±4 . 1ng/mL , p<0 . 001 . Fig 3D and S1 Table ) . These results suggest that accumulation of CL-L1 in cell pellets in COLEC10Cys176Trp is the result of severely reduced levels of CL-L1 secretion . Besides , no CL-L1 expression was observed for COLEC10Arg9Ter and COLEC10Gly77Glufs*66 transfected cells , which served as a negative control .
We previously showed COLEC11 and MASP1/3 lectin alternative pathway genes were mutated in 3MC patients . Since our initial discovery , several groups reported mutations in COLEC11 and MASP1/3 in their 3MC cohorts [5–7] . Here we report four new mutations for COLEC11 affecting four further 3MC patients from consanguineous families . None of these mutations has been found in the ExAc database , supporting pathogenicity and indicating their private nature in these pedigrees . We also identified another MASP1/3 mutation in the homozygous state , c . 9G>A , in our cohort confirming a prior report of this mutation by Urquhart et al . [6] . These results increase the percentage of patients with known mutations in our 3MC cohort; 23% carry a COLEC11 mutation and 12% now carry a MASP1/3 mutation . In the remaining patients we identified a second member of the collectin family , COLEC10 , found to be mutated in 3MC . The addition of these 2 families in COLEC10 ( 5% ) increase the coverage to 40% of known genes of our patients . Therefore , over 60% of our 3MC cohort is still without molecular confirmation of disease and that at least one further gene remains to be identified . In contrast with COLEC11 patient mutations , all three COLEC10 patients have compound heterozygous COLEC10 mutations , which is slightly surprising as they come from consanguineous families . They all share the terminating mutation c . 25C>T;Arg9Ter , found in ExAc in the general population at a low frequency ( 0 . 00003300 ) ( Table 2 ) , whereas the mutations c . 226delA and c . 528C>G were not present in the ExAc database . In recent years a very well documented evidence implicating cNCC migration in craniofacial cartilage and bone morphogenesis has accumulated ( reviewed in [19] ) . Our data suggests the failure of NCCs to migrate correctly is the principal factor leading to craniofacial abnormalities in 3MC patients . We confirmed that CL-L1 has chemotactic properties , most likely through recognition of carbohydrates on the cell surface , providing a potential explanation on how its absence can lead to abnormal NCC migration in 3MC . This is not surprising as other complement pathway proteins have previously been shown to play important roles in cell migration . For example in the first steps of the regulation of NCCs , crest cells are co-attracted by the complement fragment C3a and its receptor C3aR . When the C3aR function is inhibited enteric neural crest cell adhesion and migration is affected , and there is an increase in NCC dispersion [24 , 26] . It is worth noting that the lectin complement pathway can also induce cleavage of C3 to C3a [25] which in turn can regulate NCC migration . Furthermore , other complement factors also regulate cell migration and morphology . C3 regulates epithelial-mesenchymal transition via TWIST1 activation [27] . C3a also controls radial intercalation during early gastrulation and tissue spreading [28] . An important common functionality of C3a is its capacity to act as a chemoattractant to pull cells together and force them to migrate collectively . In the lectin complement pathway CL-L1 can form a complex with CL-K1 , called CL-LK , and bind to MASP1/3 and MASP2 [10] to activate the lectin complement pathway . We propose here that the role of CL-L1 and CL-K1 lies in regulating cell migration via cell attraction in 3MC syndrome . We know that CL-L1 and CL-K1 can act by themselves to attract cells but both can also form the heteromeric complex CL-LK that can also bind to MASP1/3 and MASP2 with higher affinity than CL-K1 homodimers [10] . Therefore , it is possible that the NCC migration in vivo requires cooperation of heteromeric interactions between CL-L1 and CL-K1 . That is supported by the observations that COLEC11 and COLEC10 genetic variants strongly influence the circulating serum levels of CL-K1 and CL-L1 and that a major proportion of these proteins are circulating in the form of heterocomplexes [16] . As such , whilst we have demonstrated CL-L1 can in itself induce cell migration and invasion , the exact molecular pathway leading to NCC migration regulation requires further investigation . We did not observe any COLEC10 expression in cells pellets and supernatant when overexpressing the mutations 9G>A; ArgXTer and c . 226delA; p . Gly77Glufs*66 ( Fig 3D ) . However , the missense c . 528C>G , p . Cys176Trp mutation did not affect COLEC10 expression , although it did prevent cellular secretion of the protein into the supernatant . Furthermore , 3MC patient mutations in COLEC11 also show a similar secretory phenotype disruption [15] . These data suggest that the mechanism of disease could be linked to abnormal CL-L1 secretion . The fact that we observe continuous expression of CL-L1 in E13 . 5 embryos and P0 pups in the mandibular epithelium could indicate there is an additional role for maintaining cellular adhesion even after NCC migration is complete; further data are required to prove this hypothesis . In summary , we have described here a new gene , COLEC10 , that when mutated causes 3MC syndrome . Further mutations identified in COLEC11 and MASP1/3 further confirm clinical suspicions of disease in several 3MC patients but leaves a sizeable proportion ( 60% ) without molecular confirmation and implicate one or more further genes . We propose that the lectin complement pathway acts as a chemottractant to guide and possibly to maintain cNCC adhesion . We believe that in future more genes linked to the lectin complement pathway and with roles in cellular adhesion and guidance will be found to be mutated in 3MC syndrome patients and other craniofacial conditions .
Patients and families samples were screened by whole-exome sequencing , including the proband and both parents when available . In each case , genomic DNA was enriched for exonic regions using the SureSelect All Exon 50Mb Targeted Enrichment kit ( targeting 202 , 124 exons from 20 , 718 genes ) from Agilent Technologies , according to the manufacturer's protocol . Captured libraries were sequenced on an Illumina HiSeq 2000 instrument using Illumina sBot clustering and HiSeq chemistries v1 . 0 , under a paired-end 100-bp read-length protocol , with four samples per flow cell lane to achieve minimum median coverage of 60× . All exomes for COLEC11 , COLEC10 and MASP1/3 have a coverage of at least x15 . For specific exonic coverage of 3MC family 19 see S1 Methods Table . The variant annotation and interpretation analyses were generated through the use of Ingenuity Variant Analysis software version 3 . 1 . 20140902 from Ingenuity Systems . For the recessive model , homozygous/compound heterozygous variants in the affected individual were retained . Intronic and exonic synonymous variants were filtered out; exonic and splice variants ( up to 2 base pairs into intron or predicted pathogenic on MaxEntScan ) with a public databases ( ExAC , 1000 Genomes and ESP Exomes ) frequency <0 . 01% ( 3MC phenotype ) were retained . All disease causing variants ( COLEC10 ) were validated by Sanger sequencing . Filtering pipelines for variants , ingenuity and a final list of all variants identified are presented in S2 Methods Table , S3 Methods Table and S4 Methods Table . HEK293 and HeLa cells were cultured in DMEM ( Invitrogen ) supplemented with 10% ( v/v ) foetal bovine serum and incubated in humidified 5% CO2 at 37oC . An agarose spot assay was used to assess chemotactic invasion potential of CL-L1 . Briefly , a 2% ( w/v ) solution of low-melting point agarose ( Invitrogen ) in phosphate-buffered saline was boiled and when the solution cooled to around 50oC it was mixed 1:1 with solutions of PBS , bovine serum albumin ( BSA ) , recombinant CL-K1 ( Abnova , H00078989-P01 ) and/or recombinant CL-L1 ( Abnova , H00010584-P01 ) . 10μL of the agarose-protein mix was then spotted onto the wells of plastic tissue culture plates , allowed to polymerise at room temperature for around 10 minutes and cells added . Cell migration and invasion was monitored at 37°C with 5% CO2 for around 48 hours using an Axiovert 135 microscope ( Zeiss ) equipped with a motorized stage that captured 1 image per 15 minutes ( Volocity software v6 . 3 , PerkinElmer ) . Migration and invasion was quantified using ImageJ software by measuring the area within the agarose-protein discs that had been occupied by cells ( Fig 3A ) . Patient mutations c . 25C>T , p . Arg9Ter; c . 226delA , p . Gly77Glufs*66 and c . 528C>G;p . Cys176Trp were introduced into a plasmid encoding wild-type human CL-L1 ( pCMV6-XL5-COLEC10; OriGene , SC303774 ) using QuickChange II Site-Directed Mutagenesis kit ( Agilent ) with hCOLEC10Arg9Ter , hCOLEC10Gly77Glufs*66 and hCOLEC10Cys176Trp primers ( S5 Methods Table ) . hCOLEC10WT , hCOLEC10Arg9Ter , hCOLEC10Gly77Glufs*66 and hCOLEC10Cys176Trp plasmids were complexed with 25kDa branched polyethylenimine ( Sigma ) and transfected into HEK293 and HeLa cells . A negative control with untransfected HEK293 and HeLa cells was used to show CL-L1 expression was not innate cell endogenous expression . Western blot was performed using standard protocols . Briefly , 48 hours post-transfection cell-culture supernatant was collected and clarified by centrifugation at 13 , 000 rpm for 10 minutes and pellet discarded . To obtain cell extract , cells were lysed by incubating on ice with chilled cell extraction buffer ( Invitrogen ) supplemented with cOmplete , mini protease inhibitor cocktail ( Roche ) and 1mM phenylmethylsulfonyl fluoride ( PMSF; Sigma ) for 30 minutes with vortexing every 10 minutes . Cell extract was then clarified by centrifugation at 13 , 000 rpm for 10 minutes and pellet discarded . Proteins in supernatant and cell lysate were separated by SDS-PAGE ( Tris-Acetate 4–15% gels , Invitrogen ) , blotted onto nitrocellulose membranes ( Bio-Rad ) and detected using primary antibodies against CL-L1 ( Generon; CSB-PA896556LA01HU , 2μg/mL ) and GAPDH ( Generon; CSB-PA00025A0Rb , 2μg/mL ) with HRP-conjugated secondary antibodies ( Dako ) . Blots were developed with enhanced chemiluminescence ( Pierce ) . To obtain cell extract for ELISA , cells were lysed by incubating on ice with chilled ELISA cell extraction buffer ( 100mM Tris; pH7 . 4 , 150mM NaCl , 1mM EGTA , 1mM EDTA , 1% Triton X-100 and 0 . 5% sodium deoxycholate ) supplemented with cOmplete , mini protease inhibitor cocktail ( Roche ) and 1mM PMSF ( Sigma ) for 30 minutes with vortexing every 10 minutes . Cell extract was then clarified by centrifugation at 13 , 000 rpm for 10 minutes and pellet discarded . For cell immunofluorescence ATDC5 cells were fixed with cold methanol -20°C , washed with PBS and blocked for 1 hour with 1% BSA . Cells were incubated overnight with the following antibodies and concentrations: CL-L1/100 ( Novus Biologicals H00010584-M01 ) , CL-K1 ( Novus Biologicals H00010584-M01 ) , Laminin ( Abcam , ab11575 ) . Cells were washed with PBS and incubated for 1 hour with Mouse or Rabbit Alexa Fluor 488 and 568 secondary antibodies ( 1/1000 ) ( ThermoFisher ) . E18 . 5 mouse embryos were harvested and fixed in 4% paraformaldehyde overnight at 4°C , dehydrated and embedded in paraffin . 10μm sections were cut . Slides were rehydrated and blocked with 5% BSA with 10% of sheep serum . The samples were incubated with a rabbit in house made CL-L1 primary antibody ( 1/100 ) overnight at 4°C , washed in PBS and developed with a Horseradish peroxidase conjugated secondary antibody and diaminobenzidine staining . 1000 Genomes , http://www . 1000genomes . org Ensembl Genome Browser , http://www . ensembl . org/index . html ExAC Browser , http://exac . broadinstitute . org/ OMIM , http://www . omim . org/ PolyPhen-2 , http://genetics . bwh . harvard . edu/pph2/ SIFT , http://sift . bii . a-star . edu . sg/ All work involving human subject research was approved by the UCL-ICH/Great Ormond Street Hospital Research Ethics Committee ( 08/H0713/82 ) ( REC reference 08/H0713/82 , Protocol number HBD2008v1 ) . All patients and families included in this work have given written consent for the use of their biological samples for research purposes under the HT act 2004 ethics committee . All animal work has been conducted under the UK Home Office regulation , Animals ( Scientific Procedures ) Act 1986 and was approved by the Home Office with Procedure Project License PPL number 70/7892 . | The 3MC syndrome is a unifying term amalgamating four rare recessive genetic disorders with overlapping features namely; Mingarelli , Malpuech , Michels and Carnevale syndromes . It is characterised by facial malformations including , high-arched eyebrows , cleft lip/palate , hypertelorism , developmental delay and hearing loss . We previously reported that lectin complement pathway genes COLEC11 and MASP1/3 were mutated in 3MC syndrome patients . Here we describe a new gene from the same pathway , COLEC10 , mutated in 3MC patients . Our results show that COLEC10 is expressed in craniofacial tissues during development . We demonstrate how CL-L1 , the protein expressed by COLEC10 , can act as a cellular chemoattractant in vitro , controlling cell movement and migration . We overexpressed constructs carrying COLEC10 non-sense mutations found in our patients , CL-L1 failed to be expressed and secreted . Moreover , when we expressed a missense COLEC10 construct , CL-L1 was expressed but failed to be secreted . In sum , we discovered a new gene , COLEC10 , mutated in 3MC syndrome and we propose a pathogenic mechanism for 3MC relating to the failure of CL-L1 function and its craniofacial developmental consequences . | [
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] | 2017 | COLEC10 is mutated in 3MC patients and regulates early craniofacial development |
Significant cell-to-cell heterogeneity is ubiquitously observed in isogenic cell populations . Consequently , parameters of models of intracellular processes , usually fitted to population-averaged data , should rather be fitted to individual cells to obtain a population of models of similar but non-identical individuals . Here , we propose a quantitative modeling framework that attributes specific parameter values to single cells for a standard model of gene expression . We combine high quality single-cell measurements of the response of yeast cells to repeated hyperosmotic shocks and state-of-the-art statistical inference approaches for mixed-effects models to infer multidimensional parameter distributions describing the population , and then derive specific parameters for individual cells . The analysis of single-cell parameters shows that single-cell identity ( e . g . gene expression dynamics , cell size , growth rate , mother-daughter relationships ) is , at least partially , captured by the parameter values of gene expression models ( e . g . rates of transcription , translation and degradation ) . Our approach shows how to use the rich information contained into longitudinal single-cell data to infer parameters that can faithfully represent single-cell identity .
It is well-recognized that cellular heterogeneities exist in a population of isogenic cells [1–3] . Indeed , cellular processes are noisy and generate cell-to-cell differences . Microfluidics and time-lapse fluorescence microscopy combined with cell-tracking algorithms make it possible to follow the behavior of populations of cells at the single-cell level over long time and to apply stimulations homogeneously [4 , 5] . Therefore , cell-cell variability in the expression of a gene of interest can be observed over extended time scales . The origins of the variability of biological processes and phenotypes are multifarious . Indeed , the observed heterogeneity of cell responses to a common stimulus is believed to originate partly from differences in cell phenotypes ( age , cell size , ribosome and transcription factor concentrations , etc… ) , from spatio-temporal variations of the cell environments and from the intrinsic randomness of biochemical reactions . A proper assessment and modeling of such heterogeneity is therefore a challenging task since not only it has several sources but also those sources are inter-dependent and act with different strengths and on different time-scales [6] . Regarding dynamical models of gene expression , the most widely-accepted approach to take into account cell-cell variability so far relies on modeling transcription as a stochastic process [7] . Yet , these approaches only give a partial representation of cellular heterogeneity as they assume that all the measured variability originates only from the noisy expression of the modeled genes . The level of expression of other genes and their products , along with the cell’s phenotype that emerges from it , are considered as fixed in time and equal for all cells . That is , the standard modeling approach considers all gene expression noise to be intrinsic . Yet , it is known from seminal works on noise in gene expression that the overall noise breaks down into intrinsic and extrinsic components [8 , 9] . Although both are always present , intrinsic noise contribution is generally dominant only on short time scales and for unstable or weakly expressed proteins . Therefore , a purely stochastic representation of cellular heterogeneity is not appropriate for a large proportion of genes and biological processes . Witnessing that validating a model encompassing both types of variability against data is still very difficult given current experimental possibilities [10] , we propose to explore a different approach in which variability is represented only as stable differences among cells . This simplifying assumption is a necessary first step towards a congruent representation of the total variability in gene expression , and can be readily applied to other biological processes in which extrinsic variability dominates or when the focus lies on cellular identity . Here we analyzed the temporal evolution of the level of expression of an inducible fluorescent reporter in a population of yeast cells growing in a microfluidic device . By selecting a strong inducible promoter and using a stable reporter , we placed ourselves in experimental conditions where extrinsic variability is dominant over the neglected intrinsic component . In addition we assess directly how the inferred individuality in gene expression can be related to measurable features of cell’s phenotype and physiology and therefore related to typical biological measures of cellular identity . We use a modeling approach in which , for a standard model of gene expression in yeast , each single cell is given specific parameter values while the cell population is described by a multidimensional parameter distribution ( Fig 1 ) . This leads to a challenging inference task compared to a classic situation where all cells are described by the same “mean-cell” model and parameters . Indeed the problem is shifted from obtaining a single value per parameter to obtaining parameter values for each observed cell , as well as a multidimensional distribution representing parameter values in large cell populations . This problem not only involves determining the distribution within a population for each parameter but also their mutual relationships , or more formally , their joint distribution . In order to do so , we used state-of-the-art statistical methods [11 , 12] that allow inferring parameters distribution across the population that are congruent with parameters attributed to each single cell . We motivate the use of such demanding statistical tools by showing why a simpler and more straightforward method is inappropriate for our current objective of representing populations by a distribution of parameters . We propose several validations of the inference results and we analyze the obtained parameter distributions representing cell populations . Then we focus on single cells and analyze the correlation across parameters or between parameters and other single-cell features related to phenotypic and physiological variability . At last , the inheritability of the parameters of gene expression is assessed . Taken together , our results demonstrate that using the proposed framework , biologically-relevant model parameters can be attributed to individual cells and related to single-cell features , while the population of cells is represented in a concise manner . As such , this work is an important step towards identifying the major determinants of extrinsic cell-cell variability , as well as introducing quantitatively the concept of single-cell identity .
Using microfluidics and time-lapse microscopy we acquired longitudinal data of the response of individual yeast cells subjected to repeated hyperosmotic shocks ( see Material and Methods ) [13 , 14] . Cells were bearing a stable fluorescent reporter driven by the STL1 promoter which is strongly activated by hyperosmotic stress [15 , 16] . We extracted fluorescence values for large numbers of single yeast cells ( typically 300 ) over a long period of time ( typically 8–10 hours ) . Markedly-different behaviors were observed between individual cells ( Figs 1 and S1 ) . As extrinsic variability is arguably the dominant component of phenotypic heterogeneity in gene expression in eukaryotic cells [17 , 18] , these differences are expected to depend at least in part on variations in the rates of transcription , translation and degradation/dilution from one cell to another . Parameters of a model of our reporter gene expression should therefore be different from one cell to another to account for extrinsic variability . By using short but pronounced and repeated inductions of gene expression with a stable reporter protein , we limited both the impact of intrinsic noise in our experiments and the deleterious effects of hyperosmotic shocks ( see Experimental Design in S1 Text ) . Mixed-effects ( ME ) models are a class of statistical models introduced to describe the response of different individuals within a population to known stimuli . Here , we used a ME model where the response of individual cells was described in terms of a simple dynamical model of gene expression . Denoting with m and p the cellular level of mRNA and fluorescent protein , respectively , we have {m˙ ( t ) =kmu ( t ) −gmm ( t ) p˙ ( t ) =kpm ( t ) −gpp ( t ) where u ( t ) represents the activity of transcription factors—in our case , the phosphorylation and nuclear import of the kinase Hog1p –and is a function of the osmolarity of the cell environment ( see Material and Methods and S1 Text ) . The production and decay rates are denoted km and gm for the mRNA , and kp and gp for the protein , respectively . To relate fluorescence measurements to actual protein concentrations , we accounted for protein folding time using a delay τ . We also assumed the presence of multiplicative and additive white Gaussian measurement noise whose strength is the same for all cells ( see S1 Text and S1 Table for details ) . Importantly , in the ME framework , it is considered that km , gm , kp , and gp vary within the population . Differences in parameter values may typically originate from differences in the level of key components of the gene expression machinery ( e . g . RNA polymerase and ribosomes ) or in environmental or physiological parameters ( e . g . cell growth rate ) . We assumed that these parameters were log-normally distributed across the population: θ = ( km , gm , kp , gp ) with ln ( θ ) ~N ( μ , Σ ) , where μ and Σ correspond to a vector of means and a covariance matrix , respectively . This assumption ensures the population is represented in a much more concise and general manner than what would be possible by only representing a population by the dynamics of every cell observed in an experiment . Here , we are looking for a multidimensional distribution defined by its center of mass ( i . e . a vector of mean values ) and its spread ( i . e . , a covariance matrix ) across the population . A simple , intuitive manner to tackle this problem is to search for the different parameter values that best describe each individual cell , and then compute the statistics ( mean and covariance ) of the underlying distribution from the set of parameter estimates . We refer to this method as the ‘naive approach’ since it is the natural starting point , bearing limitations that are not apparent until a proper analysis is performed . The proposed alternative is to use state-of-the-art approaches for the identification of ME models , such as Stochastic Approximation Expectation Maximization ( SAEM ) . SAEM is a stochastic approximation version of the well-known Expectation–Maximization algorithm and has been developed for the inference of population models in presence of limited available information [11 , 19] . Notably SAEM is the reference approach in pharmacokinetics/pharmacodynamics studies [12 , 20] . However , it has not yet been applied to time-lapse single-cell data . The SAEM algorithm directly searches for multivariate distributions by alternating ( i ) an estimation of ( an approximation of ) the likelihood of the population parameters and individual observations given the current best estimate of the parameter distribution in the population and ( ii ) an update of the current estimate of the parameter distribution . In a second step , a posteriori estimates of the individual cell parameters are obtained from the inferred parameter distribution and individual data ( maximum a posteriori estimate , MAP ) . This way , the fact that all parameters share ( hidden ) traits of the common population is explicitly taken into account . The naive and SAEM approaches are graphically represented in S2 Fig . Both the naive approach and the SAEM estimation method were applied to an experimental data set comprising more than 300 cells observed during several hours . Despite the significant diversity in the behavior of individual cells ( Fig 2A ) , both the naive approach and the SAEM estimation method were able to find single-cell parameters that fitted well the set of observed single-cell behaviors ( Fig 2B and 2C ) . For the naïve approach , one can observe that the envelope of the fitted trajectories is slightly larger than the data at the early time points ( Fig 2C ) . This simply results from the absence of data to constrain the fits at the early times for cells born during the experiment . Indeed , the average relative absolute difference between single-cell predictions and data are nearly identical in the two approaches ( naïve approach: 8 . 7%; SAEM approach: 8 . 3% ) . We then evaluated the capability of the obtained parameter distributions to actually describe the behavior of the cell population ( mean and spread ) . To do so , the parameter distributions obtained using the naive and the SAEM approaches were randomly sampled , thus creating two different virtual ‘cell populations’ , and the two corresponding sets of behaviors were computed from our model of gene expression . The SAEM-inferred parameter distribution accurately reproduced the observed behavior of the real cell population ( Fig 2D ) , whereas the naive approach failed to do so ( Fig 2E ) . Therefore , although both approaches were able to identify a set of single-cell parameters that reproduce well the behaviors of the set of observed cells , only SAEM was able to infer a parameter distribution at the population level consistent with the observed heterogeneity in gene expression . To investigate the causes of the marked differences between the predictive power of the ME models inferred using either the naive approach or the SAEM algorithm , we compared the corresponding parameter distributions . In both cases , the mean values of the parameters were comparable and within the expected ranges ( see S1 Table for parameter values and S1 Text for literature values ) . However , the distribution obtained with the SAEM algorithm was significantly more compact ( i . e . it had a smaller volume in the parameter space ) and was more structured ( i . e . it had higher cross-correlations on average; S3 Fig ) . This strongly suggested that capturing the structure of the parameter distribution is essential in order to explain the population behavior . Both the individual statistics of each parameter , and their covariance , describing mutual relationships , contain essential information to properly account for the cell-cell variability observed in the dataset . And indeed , when using a parameter distribution with the same individual parameter statistics ( mean and variance ) as the distribution inferred using SAEM but with null cross-correlations ( i . e . using the marginal distributions ) , the model lost its capability to predict the behavior of the population ( compare Fig 2D and 2F ) . Our understanding is that in the naive approach , all cells are fitted individually and are subsequently casted into a multidimensional distribution . In contrast , SAEM allows finding equally good single-cell parameters while favoring a concise multidimensional representation of the overall population . The difference in performance between these two approaches is rooted in the fact that even with a simple model of gene expression the information contained in a single trajectory is too small to constrain the inferred parameter values in a satisfactory way . Using SAEM , we actually allow each single-cell fit to use information about the overall population , which ensures coherence between the representation of the population by distributions and of the single cells by specific parameter values . Having demonstrated that the SAEM-based identification approach captures the behavior of the cell population , from here on we focus only on the results obtained using this method . We then tested the robustness of the inference approach which is an essential property for learning algorithms . Interestingly , the performance of the SAEM inference method degraded gracefully as the number of available single-cell trajectories for identification was decreased to as few as 32 cells ( Fig 3A and S2 Text ) , and also as the experimental time period used for learning was reduced ( Fig 3B and S2 Text ) . Lastly , ME models with SAEM-inferred parameter distributions were still able to give good predictions when tested on a different data set ( Fig 3C , see also S3 Text ) . At this point , we have showed how to efficiently and robustly extract the distributions of parameters of a standard model of gene expression from a collection of longitudinal single-cell data , and a set of parameters for each cell in the population . While we are here mostly interested in the details of the parameter distribution , we can also extract the average value for each parameter of the model . Importantly , they are different from the parameters that are obtained by fitting directly our model of gene expression to the population-averaged behavior . This is illustrated on Fig 4 where the ‘average cell’ trajectory ( whose parameters are the average of single-cell parameters ) is different from the average trajectory ( obtained by directly averaging the single-cell trajectories ) . As mentioned in the introduction , this expected result reminds us that parameters of a model of a biological process estimated from average behaviors , as done in the vast majority of quantitative studies , may poorly represent the actual process . Non-identifiability arises when the information contained in data along with a model structure does not allow for the proper estimation of parameter values: several parameter values ( or more usually combinations of parameter values ) yield equally-good results given the available data . In our framework , very high correlations between parameter values may indicate the existence of non-identifiability relations among parameters . The first application of the SAEM algorithm showed that km and kp were highly correlated , and , indeed , checking single-cell values suggested that the rates of transcription and translation could hardly , if at all , be quantified independently . A detailed identifiability analysis showed that , at the level of individual cells , these two parameters are structurally non-identifiable; only their product can be quantified ( S4 Text ) . However , in population approaches , partial information about the second-order statistics of individual parameters can be inferred from the population statistics even if these parameters are non-identifiable at the single-cell level ( S5 Text ) . Consequently , to address identifiability issues while preserving maximal information , we fixed the mean value for kp when inferring parameter distributions using SAEM , and introduced the protein production rate kmp , defined as the product of km and kp , for the single-cell models . With these changes , shrinkage was then found to be negligible ( S4 Text ) . Having identified single-cell parameter values , one may wonder whether they can be used to retrieve known facts or discover new ones on the physiology of the cell response to hyperosmotic shocks . In our model , hyperosmotic shocks affect all cells identically . However , in the microfluidic device , the intensity of the shock perceived by different cells varied , as evidenced by differences in the reduction of cellular volume following shocks . Therefore , one should find that protein production parameters inferred for the most severely impacted cells are statistically higher than average . We thus estimated the perceived shock intensities as the time-averaged reduction of cellular volume following shocks , and compared for all the cells the inferred parameter values and the perceived shock intensities . We found a strong correlation between protein production rates and shock intensities in agreement with our hypothesis . Moreover an equally-strong correlation was also found with mRNA degradation rates ( Fig 5A ) . This second feature , obtained by our framework without any additional measurements or hypothesis , is consistent with the known global destabilization of mRNAs observed after hyperosmotic shocks [21] . Lastly , the simultaneous increase of protein production rates and mRNA degradation rates strongly correlates with the increase of the perceived shock ( Fig 5B ) indicating that these two processes are jointly regulated in response to hyperosmotic shocks . Note that the direct experimental identification of such co-variations would be very challenging . This shows the interest of extracting and analyzing distributions of model parameters for the identification of joint regulations . In addition to hyperosmotic shocks , several features related to the cell physiology or local environment are also expected to relate to gene expression [22] . Such features notably include cell division rate , cell size , cell age , and local cell density . Since these features can be measured or estimated for each single-cell based on bright-field time-lapse imaging , one can again harness cell-to-cell variability and search for relations between these features and the parameters that describe intracellular processes involved in gene expression . Firstly , we searched for a correlation between the protein decay parameter , gp , and the cell division rate . Indeed , as the fluorescent reporter we used has a long half-life and photobleaching is negligible ( see Initial parameters values S1 Text ) , one should expect that its observed decay comes mostly from dilution due to cellular growth . Therefore , we quantified for each cell its division rate , averaged over the observation period ( S1 Text ) and , as expected , found a significant positive correlation between the measured average single-cell division rate and the protein decay parameter gp ( Fig 6 ) . Stated differently , using exclusively the fluorescence profile of individual cells and the inferred parameter distribution for the cell population as an a priori , the inference approach attributed statistically higher dilution rates to cells that grow faster . Several other highly significant correlations between single-cell parameters and the above-mentioned single-cell measured features were observed ( Fig 6 ) . Note that all measured features were averaged across time to allow the comparison with the time-invariant model parameters ( S1 Text ) . Although it is difficult to attribute in a systematic manner a direct and unambiguous biological interpretation of the observed correlations between coarse-grained model parameters and cell features , one can nevertheless observe ( i ) that cell density appears to have a pronounced influence on the protein production rate , suggesting that—even in microfluidic growth chambers—the environment of the cells should not be assumed to be perfectly homogeneous , and ( ii ) that the correlations of the protein production rates and mRNA degradation rates with every measured feature always have the same sign , corroborating the presence of mechanisms for the joint regulation of these processes in our system . More generally , one wonders how the different measured cell features relate to the overall ( multivariate ) parameter variability . We conducted a principal component analysis ( PCA ) of the set of inferred single-cell parameter values . This yielded a new parameterization of the model ( new parameters being called principal components PC1 , PC2 and PC3 ) that is particularly relevant to investigate variability as , unlike natural parameters , each principal component is uncorrelated to the others . The analysis showed that the first two components PC1 and PC2 represented 87% and 12% , respectively , of the overall variance in single-cell parameter values , and that these principal components correlated very significantly with measured cell features . We then ranked the various features based on their correlation with the variability captured by the inferred ME model . For a given feature , this is defined as the weighted average correlation with the different PCs , with weights equal to the importance ( i . e . , variance ) of every PC . It appeared that local cell density was the most important factor ( average correlation: 0 . 23 ) , followed by cell size ( 0 . 21 ) and the division rate ( 0 . 2 ) . To our knowledge , there is no established direct connection between local cell density and gene expression in yeast . It would be interesting to investigate this connection at the molecular level . Quite surprisingly , from our data , age was not associated with a significant variability in parameter values . Taken together , our results show that , for quantitative studies , features other than colony growth rate should be taken into account . A natural extension of this study would be to investigate how the inclusion of these features in the model , seen as covariates , could improve single-cell predictions . Finally , we investigated inheritance of single-cell parameters . Statistical tests showed that the parameters of mother and daughter cells were significantly closer to each other than the parameters of random cell pairs ( S1 Text and S4 Fig ) . However , this comparison does not exclusively test the effect of lineage . The fact that mother and daughter cells share a similar environment may also explain this result . To study the specific influence of lineage , we compared the parameter values between pairs of cells that either were mother and daughter ( related mother/daughter pairs ) or were a mother and the unrelated daughter of another mother cell ( non-related mother/daughter pairs ) , with all cells growing in the same microfluidic chamber so as to limit environmental bias . As shown in Fig 7 , the parameter values of individual cells were statistically closer to the parameters of their own mother cell than to the parameters of another mother cell . It appears that parameter values are 16% ( resp . 14% , 10% ) closer in genuine mother/daughter pairs for gp ( resp . gm , kmp ) . Although mild in absolute terms , bootstrapping testing showed the presence of a statistically strong inheritance effect ( p-values < 10−15 for all parameters , S1 Text ) . Importantly , we verified using a more restrictive notion of nMD pairs that the observed inheritance effect was not due to the fact that mother and daughter cells have more similar mean densities on average than nMD cells since the former share the same environment . Interestingly , we also found that daughter cells are on average 14% more sensitive than their mothers and that the intensity of the perceived shocks is anti-inherited: the most resistant mothers have the most sensitive daughters , and conversely .
In this work , we proposed an approach for capturing the biological variability observed in single-cell time-lapse microscopy experiments by distributions of parameters . By doing so , we address a fundamental issue encountered in the vast majority of quantitative studies where parameters of deterministic or stochastic models of intracellular processes make sense at the single-cell level but are estimated for a virtual ‘mean cell’ . The analysis was based on the mixed-effects ( ME ) modeling framework and two inference approaches were evaluated . The relevance of the ME framework for modeling biological processes has been recently recognized [23 , 24] . The use of advanced statistical methods , like SAEM , was essential to properly capture the variability of the biological parameters across the population in a simple manner , including most notably the correlation among them . In addition , we showed that the SAEM method scales to real-life problems and provides robust results . With this approach , the information on each and every cell is jointly used to calibrate the model parameter distribution , alleviating the problem of limited observability and noisy observations encountered at the individual-cell level . We then demonstrated the biological relevance of the inferred cell-specific parameters , as they were partly inherited from mother to daughter cells and correlated with independently-measured single-cell features . Our approach is adapted to calibrate models explicitly accounting for extrinsic variability . From a mechanistic viewpoint , two components of biological variability , termed intrinsic and extrinsic noise , have been proposed . For a given cellular process , intrinsic variability is mostly related to fast fluctuations coming from stochasticity in molecular reactions while extrinsic variability includes more stable cell-to-cell differences in intracellular and extracellular environments [8 , 17 , 25] . Thanks to recent methodological developments , such as finite state truncation methods , significant progress have been made in the identification of intrinsic noise models , in particular for the study of gene expression [26] . Such models assume that the different observations arise from different realizations of the same stochastic process and , therefore , are still based on the notion of a virtual mean—although noisy—cell . In comparison , and despite recent methodological developments [27 , 28] , few attempts have been made to infer extrinsic noise models from data , see [4 , 10 , 23 , 29 , 30] and our previous work [31] . We refer the reader to Karlsson et al . [24] for a detailed discussion of these works . This is surprising , given the fact that extrinsic noise has been shown to be the dominating component in many biological systems [17 , 18 , 32] and that application of cell population models has proven extremely useful , notably to explain cell decision processes [3] . Moreover , with the notable exceptions of Zechner et al [10] and Gonzalez et al [31] , no method that exploits single-cell time-lapse data for the identification of cell population models has been able to predict population behaviors . Interestingly , Zechner et al [10] proposed a very general framework capturing intrinsic and extrinsic variability by using a stochastic model based on the chemical master equation with parameter distributions . They investigated whether this modeling framework was able to capture both noise components appropriately , all of the extrinsic variability being aggregated into a unique cell-dependent parameter . Here , we pursued a different objective . We focused on extrinsic noise and investigated whether multidimensional parameter distributions provide an accurate description thereof and can be inferred from the available experimental data , whether the inferred single-cell parameter values are biologically-relevant , and how extrinsic noise is distributed across different cellular processes . Given the identifiability issues encountered already on relatively simple ME models , one might wonder whether more complex models combining the use of a stochastic interpretation of the reactions and of distributions for all ( or most of ) the parameters can be accurately identified based on available experimental data . Another attractive possible extension of the mixed-effect framework is to replace the purely static description of cell-to-cell differences obtained by using different , time-invariant parameter values by a more dynamical representation using reaction parameters that slowly fluctuate in time . This can typically be done by accounting for the stochastic turnover of the proteins underlying the various reactions involved in the processes of interest [33] . The possibility of identifying single-cell models opens new perspectives . Indeed , our results support the approach advocated by Pelkmans and coworkers ( 18 ) in which "studying cell-to-cell variability […] will increase our understanding of how cellular activities are embedded in the physiology of a cell . " Following what we have shown here , one could dissect the variability of the different cellular processes involved in a particular phenotypic response and search for correlations with different cellular processes and with environmental factors . Such rich information on the integrated functioning of cells is otherwise barely accessible . More fundamentally , single-cell modeling provides a quantitative tool to study the notion of cell identity , as it offers a quantitative description of cell-to-cell differences . Lastly , to which extent this increased knowledge can be used to improve our ability to predict and ultimately control single-cell behavior is a question of interest for both the systems and synthetic biology communities [14 , 34–36] .
All experiments were performed using a STL1::yECitrine-HIS5 , Hog1-mCherry-hph yeast strain derived from the S288C background [14] . Cells were cultured overnight in synthetic complete ( SC ) medium at 30°C , in a shaking incubator at 250 rpm , and then the cultures were diluted in SC so as to reach an optical density of ~0 . 2 in 4h . Exponentially-growing cells were injected into a home-made microfluidic device [14] . Liquid medium was flowed using a peristaltic pump ( IPC-N , Ismatec ) placed after the microfluidic device ( flow rate: 120μL/min ) . A computer-controlled three-way valve ( LFA series; The Lee Company ) was used to select between normal medium ( SC ) or the same medium supplemented with 1M sorbitol . The microfluidic chip was made by casting polydimethylsiloxane ( PDMS; Sylgard 184 kit; Dow Corning ) on a master wafer ( made by soft lithography ) , curing it at 65°C overnight , pealing it off , and bonding it to a glass coverslip after plasma activation . The device has 5 chambers of 200x400x3 . 6 μm where cells are imaged . These chambers are connected to larger channels where medium flows such that the environment of the imaging chamber is changed by diffusion only ( see [14] ) . After having loaded cells in the device , we leave them to rest with SC flowing for 30 min before starting the experiment . A switch of the valve state did not lead to an instantaneous change of the cells’ environment inside the microfluidic device: ~2 min were needed for the fluid to pass from the valve to the channels and the imaging chamber . The cells were imaged using an automated inverted microscope ( IX81; Olympus ) equipped with an X-Cite 120PC fluorescent illumination system ( EXFO ) and a QuantEM 512 SC camera ( Roper Scientific ) . The temperature of the microscope chamber , which also contains the media reservoirs , was constantly held at 30°C by a temperature control system ( Life Imaging Services ) . All of these components were driven by the open-source software μManager which was interfaced with Matlab . Images were taken using a 100× oil immersion objective ( PlanApo 1 . 4 NA; Olympus ) . The fluorescence exposure time was 200 ms , with fluorescence illumination intensity set to 50% of maximal power . The fluorescence exposure time was chosen such that the fluorescent illumination did not cause noticeable effects on cellular growth over extended periods of time . Importantly , illumination , exposure time , and camera gain were not changed between experiments , and besides background and auto-fluorescence subtraction ( defined as the minimum intensity in the first frame ) , no data renormalization or processing was done . Imaging was performed at a frequency of one frame every 3 min for bright-field and one frame every 6 min for fluorescence measurements . The duration of the experiments was 10 hours . Single-cell gene expression profiles were obtained in two experiments: one for identification ( DI; 325 single-cell trajectories ) and one for validation ( DV; 166 single-cell trajectories ) . The randomly-generated profiles of hyperosmotic stresses differed in each experiment . Image analysis was performed using a home-made segmentation and tracking tool , CellStar . After observing that newly-detected cells usually corresponded to buds still attached to their mother for a long period of time after detection and might present fluorescence quantification artifacts ( due to their small size and variable focus ) , we discarded the information obtained during the first two hours for new cells . Only cells imaged for more than 5 h were selected for identification and validation . The average size of a cell corresponds to its size measured at each time point in bright-field images and averaged over all time points . Average cell age and density were defined analogously . The density of the environment of a single cell was defined as the area occupied by neighbor cells relative to the area of the neighborhood of the cell . The neighborhood was defined as a disk with a radius corresponding to five times the radius of a typical cell . The relative changes in the size of the cells caused by budding events were used to estimate single-cell division times from bright-field images and compute the average cell specific division rate . After automated segmentation and tracking , lineage was manually extracted from the microscopy images . More details are provided in S1 Text . We assumed that the transcription factor activity , u ( t ) , depends on the osmolarity effectively sensed by the cells inside the microfluidic chambers , uc ( t ) , which itself depends on the valve status , uv ( t ) ( S1 Text ) . To relate fluorescence measurements to actual protein concentrations , we accounted for protein maturation time using a delay τ and assumed the presence of multiplicative and additive measurement noises that are white and Gaussian ( S1 Text ) . A mixed-effects population model is then obtained from single-cell models by assuming that the parameters of the population of cells follow log-normal distributions . More details on the modeling assumptions are provided in S1 Text . Two methods were proposed to infer ME population models: a naive approach and SAEM . The naive approach used the local optimization algorithm fminsearch from Matlab to maximize the ( log- ) likelihood of the parameters tested , given the observed data for the considered cell . The parameter distribution for the ME model is then defined based on the set of single-cell parameters . The SAEM approach aims directly at maximizing the likelihood of the population ( high-level ) parameters describing the distributions of the model parameters , given all the single-cell data . We used the SAEM implementation of Monolix software . Lastly , having inferred a distribution for the model parameters of a population of cells , one could estimate the most likely parameter values for each single cell ( ME single-cell models ) . We used the local optimization tool fminsearch from Matlab to implement a maximum a posteriori approach . For more details on the parameter inference approach see S1 Text . The analysis of the correlations between the perceived shocks or the single-cell measured features and the estimated parameters was performed using the Spearman coefficient of correlation . The significance of the correlations ( p-values ) was assessed using the standard two-tailed test implemented in the Matlab statistics toolbox . To test whether parameters of mother and daughter cells were statistically closer than on average , we constructed pairs of cells that differed solely by whether they were direct relatives ( mother/daughter pairs , MD pairs ) or not ( non-related mother/daughter pairs , nMD pairs ) . The comparison of the mean distance between MD pairs and nMD pairs was performed by bootstrapping ( S1 Text ) . | Because of non-genetic variability , cells in an isogenic population respond differently to a same stimulation . Therefore , the mean behavior of a cell population does not generally correspond to the behavior of the mean cell , and more generally , neglecting cell-to-cell differences biases our quantitative representation and understanding of the functioning of cellular systems . Here we introduce a statistical inference approach allowing for the calibration of ( a population of ) single cell models , differing by their parameter values . It enables to view time-lapse microscopy data as many experiments performed on one cell rather than one experiment performed on many cells . By harnessing existing cell-to-cell differences , one can then learn how environmental cues affect ( non-observed ) intracellular processes . Our approach is generic and enables to exploit in unprecedented manner the high informative content of single-cell longitudinal data . | [
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] | 2016 | What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast |
Screening tests for gambiense sleeping sickness , such as the CATT/T . b . gambiense and a recently developed lateral flow tests , are hitherto based on native variant surface glycoproteins ( VSGs ) , namely LiTat 1 . 3 and LiTat 1 . 5 , purified from highly virulent trypanosome strains grown in rodents . We have expressed SUMO ( small ubiquitin-like modifier ) fusion proteins of the immunogenic N-terminal part of these antigens in the yeast Pichia pastoris . The secreted recombinant proteins were affinity purified with yields up to 10 mg per liter cell culture . The diagnostic potential of each separate antigen and a mixture of both antigens was confirmed in ELISA on sera from 88 HAT patients and 74 endemic non-HAT controls . Replacement of native antigens in the screening tests for sleeping sickness by recombinant proteins will eliminate both the infection risk for the laboratory staff during antigen production and the need for laboratory animals . Upscaling production of recombinant antigens , e . g . in biofermentors , is straightforward thus leading to improved standardisation of antigen production and reduced production costs , which on their turn will increase the availability and affordability of the diagnostic tests needed for the elimination of gambiense HAT .
African trypanosomiases are neglected tropical diseases that perpetuate poverty through their burden on both public health and agriculture [1] . Human African trypanosomiasis ( HAT ) or sleeping sickness occurs in remote sub-Saharan areas and is caused by two human infective subspecies of the protozoan parasite Trypanosoma brucei ( T . b . ) . T . b . gambiense is endemic in West and Central Africa , where it causes a chronic form of sleeping sickness . It is primarily considered as a human infection , but infections of domestic and wild animals might also be observed [2] , [3] . The other human infective subspecies , T . b . rhodesiense , endemic in Eastern and Southern African countries , causes an acute form of sleeping sickness . It is a zoonosis with non-human vertebrates as primary reservoir [4]–[7] . The trypanosomes are transmitted by the bite of an infected tsetse fly ( Glossina spp . ) [8] , [9] . Sleeping sickness can be cured , but early diagnosis is important since treatment of second stage patients is more complicated and the risk of severe side effects increases significantly [8] . In 2001 , efforts to eliminate HAT were intensified . Since then the number of reported cases declined by more than 70% with 7214 new cases reported to the World Health Organisation ( WHO ) in 2012 . Infected patients were only detected in 13 of the 24 historical T . b . gambiense endemic countries , with the vast majority ( 84% ) in the Democratic Republic of the Congo . T . b . rhodesiense infections accounted for only 2% or 110 new cases in 6 countries; 65% of them in Uganda . The WHO envisages the elimination of gambiense HAT by 2030 through active and passive case detection combined with vector control [10] . The HAT control programs in T . b . gambiense endemic areas aim at parasite elimination from the human reservoir by means of mass screening , diagnosis and treatment of affected individuals . Accurate diagnosis of sleeping sickness not only prevents incorrect or delayed medical intervention possibly resulting in death of the patient , but also limits disease transmission in the community through a decrease of the human reservoir [11]–[14] . The diagnosis of gambiense sleeping sickness consists of three interrelated steps: screening , parasitological confirmation and staging [12] , [15] . Currently the Card Agglutination Test for Trypanosomiasis ( CATT ) , an antibody detection test , is used for mass population screening . Even though antibody detection techniques only provide indirect evidence for the presence of trypanosome infections , they are a valuable tool for mass screening because of the limited sensitivity of the parasitological tests [12] , [16] . These parasitological tests are performed on lymph or blood and consist of microscopical parasite detection . The most sensitive diagnostic field test available is the mini-anion exchange centrifugation technique ( mAECT ) ; even more so when the trypanosomes are concentrated in the buffy coat [17] , [18] . Staging of sleeping sickness is performed through microscopical examination of the cerebrospinal fluid for elevated white blood cells ( more than 5 per µl ) or for the presence of trypanosomes . For diseases with often low parasitaemias , such as gambiense sleeping sickness , screening for the presence of specific antibodies elicited upon contact with the parasite , offers a valuable detection tool . The better screening tests for gambiense sleeping sickness are all based on variant surface glycoproteins ( VSGs ) . One type of VSG covers the complete surface of the trypanosome , including the flagellum , by forming a dense layer of dimers . This VSG coat is highly immunogenic . The parasite can however avoid complete elimination by the host humoral immune system by regularly replacing the VSG coat by another one of a different antigenic type , a mechanism called antigenic variation [19] , [20] . The CATT screening test for T . b . gambiense uses the LiTat 1 . 3 VSG as antigen [21] . This VSG is expressed early in most gambiense infections; therefore specific anti-LiTat 1 . 3 VSG antibodies serve as a potent diagnostic marker . To increase the sensitivity of the antibody detection test other predominant VSGs , LiTat 1 . 5 and LiTat 1 . 6 , can be added . A rapid latex agglutination test , LATEX/T . b . gambiense , combining these three VSGs has been developed [22] and the ELISA/T . b . gambiense with the same antigen combination has a proven high sensitivity and specificity on serum , plasma , CSF and even saliva [23] , [24] . However , stability and/or logistical requirement issues prevented replacement of the CATT by these tests . Recently , rapid lateral flow diagnostic tests for gambiense HAT have been developed which use a combination of native LiTat 1 . 3 and LiTat 1 . 5 VSGs as antigens [25]–[27] . Compared to the CATT , these tests are as sensitive and specific and fully comply with the ASSURED criteria defined for rapid diagnostic tests [28] . However , the native antigens for these tests are still produced through massive infections of laboratory rodents with highly human-infective bloodstream form trypanosomes expressing these variant antigenic types ( VATs ) . We therefore aim to replace these native antigens by recombinant antigens in order to eliminate the infection risk for staff and the need for laboratory animals for antigen production . We opted for secreted expression of the expressed VSG fragments to avoid the difficult and tedious purification of intracellular , recombinant proteins . Since eukaryotic post-translational modifications , such as the glycosylation pattern of a protein , can be important for the correct folding of the protein and hence its diagnostic value , we expressed the antigens recombinantly in the yeast Pichia pastoris . We have previously used the same expression host to successfully express the T . evansi VSG RoTat 1 . 2 [29] . The expressed and secreted recombinant proteins were affinity purified and tested for their diagnostic potential with a panel of sera from HAT patients and non-HAT controls .
Sera from HAT patients and endemic non-HAT controls were collected within different diagnostic studies [24] , [30] . All individuals gave their written informed consent for the use of their plasma specimen in HAT research before providing blood . Permission for these studies was obtained from the national ethical committee of the Democratic Republic of the Congo ( DR Congo ) and from the Institute of Tropical Medicine Antwerp ( ITMA ) ethical committee , reference number 03 07 1 413 and 04 44 1 472 . All specimens were anonymised . For recombinant expression of the trypanosome proteins , the Pichia GlycoSwitch M5 strain was used to assure homogeneous , trypanosome-like Man5GlcNAc2 N-glycosylation of the secreted proteins [31] . The recombinants were cloned in the pP-αhSUMO3 vector of the SUMOpro-3 Gene Fusion Technology kit ( LifeSensors ) and electroporated in the Pichia GlycoSwitch M5 strain . This vector contains the strong , methanol-inducible AOX1 promoter followed by an alpha-mating factor signal sequence for secreted expression of the recombinants [32] and a N-terminal His tag for downstream affinity purification . The affinity tag is linked to the human SUMO3 ( small ubiquitin-like modifier ) which precedes the cloned protein-of-interest in order to enhance the expression and to promote the solubility and correct folding of its fusion partner . The cloned constructs in the pP-αhSUMOpro3 vector result in SUMO fusion proteins . The rLiTat 1 . 3 consists of a N-terminal His tag , followed by the SUMO fusion protein , the 349 N-t amino acids of LiTat 1 . 3 VSG and a C-t Strep tag II . The rLiTat 1 . 5 has a similar setup , with 394 N-t amino acids of the LiTat 1 . 5 VSG . The trypanosomes expressing the LiTat 1 . 5 VSG were obtained from the collection of trypanosome stabilates maintained at the Institute of Tropical Medicine in Antwerp . Cryostabilates were injected in OF-1 mice . At the first peak parasitaemia , the mice were euthanised and bled by cardiac puncture . The trypanosomes were purified from the blood through ion exchange chromatography on DEAE cellulose according to Lanham & Godfrey [33] . The purified trypanosomes were centrifuged ( 15 min , 1500 g ) and washed twice with 14 ml of phosphate buffered saline-glucose , pH 8 . 0 , and then pelleted by centrifugation . The pellet was either used immediately or stored at −80°C for later use . Total RNA was extracted from the pellets with the RNeasy Midi Kit ( Qiagen ) . Finally , the RNA was reverse transcribed with an oligo-dT primer to cDNA following the Omniscript RT Kit protocol ( Qiagen ) . The variable N-terminal parts of LiTat 1 . 3 and of LiTat 1 . 5 VSG were cloned in the expression host since they contain the specific immunogenic epitopes of the VSGs which are exposed to the host immune system . The N-terminal part of the LiTat 1 . 3 sequence ( GenBank accession no . KJ499460 ) was PCR-amplified from T . b . gambiense LiTat 1 . 3 genomic DNA ( gDNA ) with a primer set starting at the first residue of the mature polypeptide ( amino acid 24 ) and ending at amino acid 372 of a total of 479 amino acids . A BsaI site was incorporated in the forward primer ( LiTat 1 . 3_SUMO-FP ) and a Strep tag II coding sequence ( IBA ) , a stop codon and a BsaI/XbaI site were added to the reverse primer ( LiTat 1 . 3_SUMO-RP ) . The N-terminal domain of the LiTat 1 . 5 VSG sequence was amplified from T . b . gambiense LiTat 1 . 5 cDNA ( GenBank accession no . HQ662603 ) . These primers were developed according to the instructions of the cloning strategy ( In-Fusion HD Cloning Kit , Clontech Laboratories ) . The complete LiTat 1 . 5 VSG sequence covers 502 amino acids , but the amplified sequence starts at the first residue of the mature polypeptide ( amino acid 33 ) and ends at amino acid 426 [34] . All primers used are shown in table 1 . The PCR fragments were cloned in the pP-αhSUMO3 vector , previously digested with BsmBI . The resulting constructs were transformed into TOP10 E . coli cells ( Invitrogen ) and plated on LBLS-z ( low salt Lysogeny Broth medium with 25 µg/ml zeocin ( InvivoGen ) ) agar plates . Positive clones were selected by colony PCR with α-factor and 3′AOX1 primers and grown overnight in 5 ml LBLS-z medium in a shaker incubator at 37°C and 250 rpm ( Innova 44R , New Brunswick Scientific ) . The plasmid DNA was purified ( QIAprep Spin Miniprep Kit , Qiagen ) and the nucleotide sequences of the truncated genes in the transfer plasmid were confirmed by sequencing . For the LiTat 1 . 3 construct a secreted rLiTat 1 . 3H-SUMO-24-372-Strep ( rLiTat 1 . 3 ) protein of 457 amino acids with a calculated mass of 49 kDa is expected , while the LiTat 1 . 5 construct codes for rLiTat 1 . 5H-SUMO-33-426-Strep ( rLiTat 1 . 5 ) consisting of 502 amino acids with a theoretical mass of 55 kDa ( ExPASy ProtParam tool ) . The constructs were linearised with PmeI and purified ( QIAquick , QIAGEN ) . The concentration was measured with the NanoDrop ND-1000 Spectrophotometer ( ISOGEN Life Science ) and 1 to 3 µg linearised plasmid DNA in a maximum volume of 10 µl were electroporated ( 25 µF , 2000 V; Gene Pulser XCell , Bio-Rad ) into 80 µl electrocompetent Pichia GlycoSwitch M5 cells . The transfected clones were selected on YPDSz ( yeast extract peptone dextrose sorbitol medium with zeocin ) agar plates with zeocin concentrations ranging from 100 to 2000 µg/ml to select for putative multicopy transfectants . Positive transfectants were selected by inoculation of individual colonies in 10 ml Buffered Glycerol-complex Medium ( BMGY ) in 50 ml falcon tubes . After 24 h growth ( 29°C , 250 rpm; Innova 44R , New Brunswick Scientific ) , the cells were collected by centrifugation for 5 min at 3220 g . The supernatant was discarded and the cells were resuspended in 10 ml Buffered Methanol-complex medium ( BMMY ) . The secreted expression of the recombinants was induced for 24 h at 250 rpm and , to minimise protein degradation , at 22°C . The supernatant was collected by centrifugation of the cell culture for 15 min at 3220 g . The proteins in 1 ml supernatant were precipitated with trichloroacetic acid ( TCA ) and resuspended in 25 µl of 1 M Tris-base and 25 µl of SDS-PAGE reducing loading buffer ( 0 . 125 M Tris-HCl ( pH 6 . 8 ) , 4% SDS , 20% sucrose , 0 . 04% bromophenol blue , 0 . 2 M dithiothreitol ) . The secreted proteins were separated over a 12% polyacrylamide gel ( 50 min at 200 V; Mini-PROTEAN Tetra Cell Electrophoresis System , Bio-Rad ) and transferred to a nitrocellulose membrane by western blotting ( 30 min at 100 V; Criterion Blotter , Bio-Rad ) . The nitrocellulose membrane was blocked overnight with TBS-Blotto ( 0 . 02 M Tris-HCl ( pH 7 . 5 ) , 0 . 5 M NaCl , 0 . 004% NaN3 , 5% skimmed milk powder ( Fluka ) ) The proteins were visualised by immunostaining either indirectly with mouse anti-His tag antibody ( 1∶500; AbD Serotec ) as primary antibodies followed by alkaline phosphatase conjugated rabbit anti-mouse IgG ( 1∶15000; Sigma ) or directly with Strep-Tactin AP conjugate ( 1∶4000; IBA ) , followed by the addition of the substrates nitro blue tetrazolium ( NBT ) and 5-bromo-4-chloro-3-indolyl phosphate ( BCIP ) . The selected protein secreting colonies were inoculated in 500 ml BMGY with addition of 0 . 2 g/l chloramphenicol . The cultures were grown in Fernbach shake flasks for 24 to 66 hours at 29°C and 200 rpm to create sufficient biomass . Afterwards , the cells were collected by centrifugation ( 5 min at 4417 g ) and resuspended in an equal volume of BMMY_2%CA ( BMMY supplemented with 2% casamino acids ( BD ) ) containing 1% methanol and with addition of 1% J673A antifoam ( Struktol ) [35] . After measurement of the optical density at 600 nm the culture was further diluted with BMMY_2%CA to an OD600 nm of 5 . 0 and 500 ml of this dilution was transferred to a Fernbach flask . Protein expression was induced for 2 days at 22°C and 200 rpm with addition of 0 . 5% methanol every 8 h . The supernatant was collected by centrifugation for 30 min at 17670 g ( without brake ) . The rLiTat 1 . 3 and 1 . 5 constructs were purified over a nickel charged nitrilotriacetic acid agarose resin ( Ni-beads; HisPur Ni-NTA resin; Pierce ) . The collected culture supernatant was added to the Ni-beads ( 50 ml per 1 ml beads in a 50 ml falcon ) and mixed for 1 h on a roller mixer at room temperature . After 1 h the beads were collected by 5 min centrifugation at 700 g . A sample of the supernatant ( flow-through ) was taken to evaluate the binding efficiency . The beads were resuspended in 1 ml of wash buffer ( 20 mM sodium phosphate , 300 mM sodium chloride ( PBS ) with 25 mM imidazole , pH 7 . 4 ) and decanted into a 20 ml Econo-column ( Bio-Rad ) in a cold room at 4°C . All further purification steps were performed in this cold room . After packing of the column , the unbound proteins were washed away with wash buffer until the baseline absorption value at 280 nm ( i . e . the absorption value of the wash buffer ) , measured with an UV spectrophotometer ( Spectra/Chrom Model 280 UV Detector , Spectrum Chromatography ) , was reached . The bound proteins were eluted with elution buffer ( PBS with 250 mM imidazole , pH 7 . 4 ) . The purified proteins were desalted over a HiPrep 26/10 Desalting column ( GE Healthcare ) with PBS at 4°C according to the manufacturer's instructions . The concentration was measured by the BCA Protein Assay Kit ( Thermo Scientific ) . Microplates ( Maxisorp , Nunc ) were coated overnight at 4°C with 100 µl/well of purified rLiTat 1 . 3 or rLiTat 1 . 5 at 4 µg/ml or a combination of both recombinants at 4+4 µg/ml and with 100 µl/well of native LiTat 1 . 3 or 1 . 5 at 2 µg/ml or a combination of both native antigens at 2+2 µg/ml . All antigens were diluted in PB ( 10 mM sodium phosphate , pH 6 . 5 ) . Antigen-negative control wells were left empty . Further manipulations were done at ambient temperature . The protocol was based on the procedure according to Lejon et al . [36] , [37] . To correct for aspecific reactions , caused by contaminating yeast proteins that were not eliminated from the protein mixture by the one-step affinity purification , a culture of untransfected Pichia pastoris M5 strain was grown for 47 h in BMMY_2%CA ( after a pre-culture in BMGY ) . This culture was induced with methanol in exactly the same way as the transfected strains ( i . e . addition of 0 . 5% methanol every 8 h ) . Before addition to the microplate the sera were desorbed by dilution in PBS-Blotto ( 0 . 01 M sodium phosphate , 0 . 2 M sodium chloride , 0 . 05% NaN3 , 1% skimmed milk powder , pH 7 . 4 ) supplemented with 10% ( v/v ) untransfected M5 medium for 1 h at room temperature . The serum dilutions were centrifuged for 5 min at 3000 g before pipetting . One hundred and sixty two human sera from 88 T . b . gambiense HAT patients and 74 non-HAT controls , were tested at a 1∶400 dilution . Antibody binding was visualised with goat anti-human IgG ( H+L ) conjugated with horseradish peroxidase ( 1∶40000; Jackson ImmunoResearch ) and the chromogen ABTS ( 2 , 2′-azinobis[3-ethylbenzothiazonline-6-sulfonic acid]-diammonium salt; Roche ) . The optical densities were read at 414 nm ( Multiskan RC Version 6 . 0; Labsystems ) . The optical density ( OD ) measured for each serum was recalculated as percent positivity ( PP ) of the OD of the strong positive control present on each plate . The same strong positive control and a weak positive control were incorporated in each plate to monitor plate to plate variations on a Levey-Jennings chart [38] . On this chart the Westgard rules were applied to accept or reject each individual plate and to repeat the ELISA if necessary [39] . The accuracy of the different antigens for diagnosis was determined in SigmaPlot 12 . 0 by calculation of the area under the receiver operator characteristics ( ROC ) curve ( AUC ) [40] . Confidence intervals ( CI ) were determined according to DeLong [41] . Sensitivities and specificities with 95% binomial Wilson confidence intervals were calculated using SigmaPlot 12 . 0 . The McNemar Chi2 test was used to test differences in sensitivity and specificity of the recombinant and native antigens and their mixtures and differences in AUCs .
After two days of induction , the secreted recombinants were affinity purified from the harvested supernatants and analysed by SDS-PAGE followed by western blot and Coomassie staining . The rLiTat 1 . 3 is detected in western blot with anti-His tag and anti-Strep tag II antibodies ( Fig . 1 , panel A and B ) . The anti-His tag antibody recognises a protein of approximately 60 kDa in the affinity purified fraction and in the 20× concentrated fraction that was not retained by the Ni-NTA resin . The anti-Strep tag antibody recognises the same 60 kDa protein in the supernatant of the Pichia culture after 44 h induction but also visualises a shorter , putative degradation product of approximately 35 kDa in the affinity purified fraction and the concentrated flow-through . Three bands are visible in the purified fraction in the Coomassie stained gel ( Fig . 1 , panel C ) . The most prominent band corresponds with the His- and Strep-tagged recombinant protein of 60 kDa . One of the two smaller bands of 30–35 kDa corresponds with the Strep-tagged degradation product of the recombinant protein . The apparent mass of 60 kDa of the rLiTat 1 . 3 does not match its expected theoretical mass of 49 kDa . However , the difference can be attributed to the SUMO fusion protein , since it is known that while this chaperone has a theoretical mass of less than 11 kDa , it migrates at approximately 20 kDa ( Christian Loch , LifeSensors , personal communication ) . Not all secreted recombinant fractions were retained by the Ni-NTA resin since they are visible in the 20× concentrated flow-through of the Ni-NTA resin . The secreted rLiTat 1 . 5 also reacts with anti-His tag and anti-Strep tag II antibodies ( Fig . 2 , panel A and B ) . Only in the affinity purified fraction , the anti-His tag antibody recognises proteins of approximately 80 kDa ( faint band ) and 35 kDa . The anti-Strep tag antibody reacts already after 25 h induction with a 80 kDa protein in the supernatant of the Pichia culture and in the affinity purified fraction . In the latter fraction several putative degradation products of about 20 , 25 , 35 , 40 and 70 kDa also react with this anti-Strep tag antibody . The apparent mass of the secreted recombinant is again higher than expected ( 80 kDa vs . 55 kDa ) . Moreover , this difference cannot be explained by the presence of the SUMO fusion partner alone . Nevertheless , we have previously observed that other recombinantly expressed trypanosome glycoproteins tend to migrate slower , probably due to interference of the oligosaccharides with the binding of SDS on the protein backbone [29] , [42] . Most secreted recombinant fractions were retained by the Ni-NTA resin since they are not or hardly visible in the 20× concentrated flow-through of the Ni-NTA resin ( Fig . 2 , panel C ) . Affinity purification of 100 ml yeast culture after two days of induction yielded typically 1 mg of recombinant proteins . The diagnostic potential of rLiTat 1 . 3 and rLiTat 1 . 5 in comparison with their corresponding native antigens was assessed in ELISA with sera from 88 T . b . gambiense HAT patients and 74 non-HAT controls ( Fig . 3 and Table 2 ) . Both the recombinant and the native antigens reacted more strongly with the patient sera ( 10 to 34 times higher median PP values ) than with the control sera . However , for each recombinant the median PP obtained with the patient sera was lower than for the corresponding native antigen with the same sera . A similar pattern was observed with mixtures of the two recombinant and of the two native antigens . The relationship between diagnostic sensitivity and specificity of the antigens is represented as ROC curves in Figure 4 . The area under the curve ( AUC ) is 0 . 97 ( 95% CI 0 . 948–0 . 999 ) for native LiTat 1 . 3 and 0 . 95 ( 95% CI 0 . 921–0 . 980 ) for rLiTat 1 . 3 and 0 . 98 ( 95% CI 0 . 952–0 . 999 ) for native LiTat 1 . 5 and 0 . 96 ( 95% CI 0 . 928–0 . 984 ) for rLiTat 1 . 5 . With the mixture of the two native antigens an AUC of 0 . 97 ( 95% CI 0 . 944–1 . 000 ) is obtained while with the mixture of the two recombinant antigens , the AUC of 0 . 96 is slightly lower ( 95% CI 0 . 938–0 . 990 ) . Pairwise comparison of the AUC for all antigens and their mixtures showed no significant differences ( p>0 . 05 ) . For the whole range of cut-offs the Youden index was calculated ( Youden index = sensitivity+specificity−1 ) [43] . For the cut-off PP value with maximal Youden index , the diagnostic sensitivity and specificity for each antigen and the combinations are represented in Table 3 . With the individual recombinant antigens , the sensitivities are significantly lower than with the corresponding native antigens ( p = 0 . 0027 for rLiTat 1 . 5 and p = 0 . 0196 for rLiTat 1 . 3 ) . When both antigens are mixed , the sensitivity increases for the recombinant antigens but not for the native antigens and the difference becomes not significant ( p = 0 . 1025 ) . The specificity of recombinant LiTat 1 . 3 antigen is significantly lower than for its corresponding native antigen ( p = 0 . 0455 ) while the specificities of recombinant LiTat 1 . 5 and the mixture of rLiTat 1 . 3 and rLiTat 1 . 5 are not significantly different from the specificities of the native LiTat 1 . 5 and the mixture of nLiTat 1 . 3 and nLiTat 1 . 5 ( respectively p = 0 . 5637 and p = 0 . 3171 ) .
The variable N-terminal domains of two predominant T . b . gambiense VSGs , LiTat 1 . 3 and LiTat 1 . 5 , were successfully expressed as SUMO fusion proteins by the yeast Pichia pastoris and secreted in the culture supernatant . The SUMO part prevents misfolding of its fusion partner and chaperones it through the endoplasmic reticulum [44] . In order to mimic the native antigen structure as much as possible , our recombinants were expressed in the GlycoSwitch M5 strain , ensuring a trypanosome-like Man5GlcNAc2 N-glycosylation profile . The mature rLiTat 1 . 3 consists of 457 amino acids with two putative N-glycosylation sites on the expressed variable N-terminal domain of LiTat 1 . 3 VSG , namely Asn145 and Asn271 or amino acids 45 and 171 of the VSG . The mature rLiTat 1 . 5 consists of 502 amino acids with no putative N-glycosylation sites ( NetNGlyc; ExPASy ) . However , in the absence of putative N-glycosylation sites in the rLiTat 1 . 5 fusion protein , O-glycosylation , which also occurs in Pichia , might account for its slower migration on SDS-PAGE [45] . As shown in the western blots and in the Coomassie stained gels secretion of the recombinants is accompanied by a certain level of degradation . During the optimisation phase of the expression protocol , we attempted to control degradation of the expressed protein by addition of several protease inhibitor preparations , however without success ( data not shown ) . Neither did the addition of antifoam to the culture medium inhibit protein degradation although this resulted in a higher production . The combination of supplementation of the culture medium with antifoam and limiting the induction phase to maximum two days at 22°C provided the best means to control degradation of the expressed proteins with yields of up to 10 mg affinity purified recombinant protein per litre cell culture . Another strategy that still could be attempted to avoid protein degradation , is the identification of the protease-sensitive sites through Edman degradation of the smaller fragments , followed by site-directed mutagenesis to change the identified protease recognition sequences . Alternatively , the recombinant antigens could be expressed in Pichia strains that are deficient in vacuolar proteases , such as SMD1163 , SMD1165 or SMD1168 , in order to reduce protein degradation . However , these strains typically exhibit slower growth rates , lower transformation efficiencies and lower viabilities . Moreover , they are not available in the GlycoSwitch format [46] . Purification of the secreted recombinants was limited to a one step affinity purification on Ni-NTA resin . Additional purification via the C-terminal Strep tag II is possible but the diagnostic specificity of the recombinant proteins does not suggest that further purification is necessary . Finally , the SUMO fragment could also be removed from the fusion proteins with SUMO protease 2 but neither the sensitivity nor the specificity of the recombinants seem to be impaired by the presence of the SUMO protein . The rLiTat 1 . 3 and rLiTat 1 . 5 antigens proved their diagnostic potential when compared to the corresponding native antigens in ELISA on sera from 88 HAT patients and 74 non-HAT controls . The slightly lower reactivity of the individual recombinants might be due to the fact they represent only the N-terminal fragments of the native VSGs , thus carrying less epitopes than their native counterparts . Furthermore , incomplete or incorrect dimerisation of the recombinants may impede the formation of certain diagnostically important conformational epitopes . These antigens are therefore ready to be incorporated in new rapid diagnostic tests . As mentioned in the introduction , new immunochromatographic antibody detection tests have been developed using the native LiTat 1 . 3 and LiTat 1 . 5 as antigens . This study proves that the rLiTat 1 . 3 and rLiTat 1 . 5 , especially in combination , could replace the native antigens in a similar test format . Replacing the native antigens will eliminate infecting and sacrificing laboratory rodents and the inherent manipulation-related infection risk for the laboratory staff that is linked to the production of native antigens . Upscaling production of recombinant antigens , e . g . in biofermentors , is straightforward thus leading to improved standardisation of antigen production and reduced production costs , which on their turn will increase the availability and affordability of the diagnostic tests needed for the elimination of gambiense HAT . | Population screening for the chronic form of sleeping sickness or gambiense human African trypanosomiasis ( HAT ) is still based on an antibody detection test against the native variant surface glycoprotein ( VSG ) LiTat 1 . 3 . This protein is produced through massive infections of lab animals with highly virulent parasites . We aim to replace this native antigen with recombinant VSGs , both LiTat 1 . 3 and LiTat 1 . 5 , expressed in the yeast Pichia pastoris . The diagnostic potential of these recombinants was confirmed in ELISA with sera from HAT patients and negative controls . Replacement of the native LiTat 1 . 3 VSG with these recombinants would prevent the infection and sacrifice of lab animals and the inherent infection risk linked to the production of the screening test . Upscaling production of recombinant antigens , e . g . in biofermentors , is straightforward thus leading to improved standardisation of antigen production and reduced production costs , which on their turn will increase the availability and affordability of the diagnostic tests needed for the elimination of gambiense HAT . | [
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] | 2014 | Recombinant Antigens Expressed in Pichia pastoris for the Diagnosis of Sleeping Sickness Caused by Trypanosoma brucei gambiense |
Previous studies have stressed the genetic divergence and high pathogenicity of strains of T . gondii from French Guiana . Although strains from coastal , human adapted environments ( so called anthropized ) resemble those found in other regions of the Caribbean , strains collected from inland jungle environment are genetically quite diverse . To better understand the composition of these distinct strain types , we undertook a more in depth analysis of T . gondii strains from French Guiana including profiling of chromosome 1a ( Chr1a ) , which is often shared as a single monomorphic haplotype among lineages that are otherwise genetically distinct . Comparison of intron sequences from selectively neutral genes indicated that anthropized strains were most closely related to clonal type III strains from North America , although wider RFLP analysis revealed that they are natural hybrids . In contrast , strains isolated from the jungle were genetically very diverse . Remarkably , nearly all anthropized strains contained the monomorphic version of Chr1a while wild stains were extremely divergent . The presence of the monomorphic Chr1a strongly correlated with greater transmission in domestic cats , although there were several exceptions , indicating that other factors also contribute . Anthropized strains also varied in their virulence in laboratory mice , and this pattern could not be explained by the simple combination of previously identified virulence factors , indicating that other genetic determinants influence pathogenicity . Our studies underscore the marked genetic separation of anthropized and wild strains of T . gondii in French Guiana and provide additional evidence that the presence of Chr1a is associated with successful expansion of widely different lineages within diverse geographic areas . The predominance of Chr1a among strains in the anthropized environment suggests that it may confer an advantage for transmission in this environment , and thus potentially contribute to the spread of pathogenecity determinants .
Toxoplasma gondii is a globally distributed protozoan parasite with a broad host range , and which often incidentally infects humans [1] . Studies of the population genetics have emphasized the predominance of three clonal lineages that share overlapping geographic distributions and hosts in North America and Europe [2] . In addition , a fourth clonal lineage , related to type II has recently been described in North America , where it tends to be found in wild rather than domestic animals [3] . In contrast , strains in South America are more genetically diverse and comprise distinct lineages that do not demonstrate marked clonality [2] . Recent studies , comparing more than 900 isolates from around the world , grouped strains into 15 separate haplogroups ( HG ) that cluster within 6 major clades [4] . Current coverage of isolates from Africa , Asia , and South East Asia is sparse , so this pattern is likely to change with continued sampling . Infection with T . gondii normally causes only mild symptoms in healthy hosts [5] , yet there is evidence that some strains , particularly in South America , are more virulent [6] . For example , in regions of southern Brazil , toxoplasmosis is associated with severe recurrent ocular disease [7] and strains from this region have been shown to comprise unique genotypes that are also highly virulent in mice [8] , [9] . Previous studies have emphasized the genetic diversity of T . gondii strains collected in the jungles of French Guiana versus those collected in anthropized environments [10] . Infection of humans with strains from wild or jungle environments has been associated with severe toxoplasmosis in French Guiana [11] , [12] [13] , [14] . Although the factors that shape the population structure of T . gondii remain poorly understood , previous studies have highlighted the common inheritance of a monomorphic version of chromosome 1a ( Chr1a ) among clonal strains in North America and Europe [15] . It was originally proposed that association of monomorphic Chr1a with the dominant lineages in North America and Europe might explain their clonality , in particular if it were responsible for asexual transmission [15] , [16] . However , subsequent studies found the identical version of Chr1a in several different lineages in South America , despite the fact that they are not clonal and differ substantially from each other across most other regions of their genomes [17] . The presence of monomorphic Chr1a among the clonal lineages might simply reflect their common recent ancestry [16] , which resulted from a few genetic crosses in the wild [18] . However , such a process would not explain the presence of this same conserved chromosomal variant in otherwise disparate genomes of different halpogroups in South America . Rather , the presence of monomorphic Chr1a among lineages that differ substantially in their overall genomic content suggests it is preferentially retained despite recombination in other regions of the genome [17] . The reasons for the successful spread of Chr1a among such diverse strains from different geographic regions and within different genetic backgrounds remain unknown . Here we examined the distribution of Chr1a in wild and anthropized T . gondii strains collected from French Guiana . We found the same monomorphic version of Chr1a was almost universally found in strains from anthropized environments , while this chromosome was extremely diverse in wild isolates . We were interested in factors that might lead to successful expansion of strains harboring Chr1a and considered both the potential of such isolates to cause acute and chronic infection in rodents and to be transmitted by cats . Several of the isolates from the anthropized environment were shown to have enhanced virulence in mice , despite not containing previously identified virulence factors , pointing to the existence of unique pathogenicity determinants in natural isolates of T . gondii . As well , strains harboring the monomorphic Chr1a showed a greater propensity for transmission in domestic cats . Together these factors may lead to greater success of strains in natural hosts within anthropized environments , which may in turn affect the spread of pathogenecity determinants important in humans .
All animal experiments were conducted according to the U . S . A . Public Health Service Policy on Humane Care and Use of Laboratory Animals ( Animal Welfare Assurance numbers A-3381-01 or A4400-01 ) . Animals were maintained in an Association for Assessment and Accreditation of Laboratory Animal Care International-approved facilities . All protocols were approved by the Institutional Care Committee at the School of Medicine , Washington University in St . Louis ( approval number 20130130 ) or the Animal Parasitic Disease Laboratory ( protocol numbers 12-016 and 12-013 ) , USDA , Beltsville , MD . Eighteen strains of T . gondii from French Guiana and one from Surinam were collected and characterized with microsatellite markers , described previously [10] , [13] , [19] , [20] . These strains are designated with a code according to the Toxoplasma Biological Resource Center ( BRC ) ( http://www . toxocrb . com ) nomenclature for T . gondii isolates ( Table S1 ) . Thirty eight representative strains for the major HGs of T . gondii were chosen from a previously described set of strains [4] ( Table S1 ) . Strains were grown in human foreskin fibroblast ( HFF ) cells cultured in DMEM ( Invitrogen ) containing 10% FBS , 2 mM glutamine , 20 mM HEPES pH 7 . 5 and 10 µg/ml gentamicin , and harvested after host cell lysis by passing through 3 . 0 micron polycarbonate filters ( Fisher Scientific , UK ) [4] . Harvested parasites were resuspended in phosphate buffered saline ( PBS ) at a concentration of approximately 106 cell/ml and digested with 10 µg/ml proteinase K ( Sigma , St . Louis , MO , USA ) at 55°C for 2 hr . Proteinase K was inactivated by incubating the lysate in 95°C for 15 min [4] . T . gondii strains were genotyped using 15 microsatellite markers distributed on 10 of 14 chromosomes , as described previously [21] . Briefly , for each primer pair , the forward primer was 5′-end labeled with fluorescein to allow sizing of PCR products that were separated by electrophoresis in an automatic sequencer . PCR was carried out in a 25 µL reaction mixture consisting of 12 . 5 µL of 2X QIAGEN Multiplex PCR Master Mix ( Qiagen , France ) , 5 pmol of each primer , and 5 µL of template DNA . Cycling conditions were 15 min at 95°C; 30 s at 94°C , 3 min at 61°C , and 30 s at 72°C for 35 cycles followed by 30 min at 60°C . PCR products were diluted 1∶10 with deionized formamide . One microliter of each diluted PCR product was mixed with 0 . 5 µL of a dye-labeled size standard ROX 500 ( Applied Biosystems , location ) and 23 . 5 µL of de-ionized formamide ( Applied Biosystems ) . The mixture was denatured at 95°C for 5 min and then electrophoresed using an ABI PRISM 3130xl automatic sequencer ( Applied Biosystems ) . The sizes of the alleles in bp were estimated using GeneMapper analysis software v4 . 0 ( Applied Biosystems ) . Neighbor-Joining trees were reconstructed from the genetic distances among individual isolates using Populations 1 . 2 . 32 ( http://bioinformatics . org/populations/ ) . Trees were reconstructed using the Cavalli- Sforza and Edwards chord-distance estimator [22] . Distance analyses were repeated for 1 , 000 bootstrap replicates in which loci were sampled with replacement . Unrooted trees were generated using MEGA 6 . 05 ( http://www . megasoftware . net/history . php ) software . Genotyping of T . gondii strains was conducted using nine PCR-RFLP markers and sequencing of four introns from three different genes ( UPRT , EF , and HP ) constituting 1 , 775 bp . These markers have been used previously to characterize other T . gondii lineages [4] , and representative strains from each HG were included here for comparison ( Table S1 ) . Genetic profiling of ChrIa was performed by sequencing 12 scattered blocks comprising 800 to 900 bp each for a total length 8 , 055 bp , as described previously [17] , [23] . Parasite lysates were used as template DNA for the PCR amplification of RFLP markers , introns , and ChrIa regions using primers described previously [4] . Amplified PCR products were used for restriction enzyme digestion and gel electrophoresis or for sequencing using BigDye cycle sequencing ( Applied Biosystems , Foster City , CA ) performed by GENEWIZ , Inc . ( South Plainfield , NJ ) . Clustal W/X [24] was used to align raw sequences with default settings . Aligned sequences were saved as nexus files and directly incorporated into SplitsTree v4 . 4 [25] to generate unrooted phylogenetic networks using a neighbor-net method and 1 , 000 bootstrap replicates . Virulence was determined by intraperitoneal ( i . p . ) infection of eight week old female CD-1 outbred mice ( Charles River Laboratories , Wilmington , MA ) using 5 mice for doses of 10 , 100 , or 1000 tachyzoites ( n = 15 mice per strain ) , as described previously [26] . In parallel , plaque assays were conducted to estimate the number of viable parasites , as described previously [26] . Animals were monitored for 30 days and surviving animals were tested serologically by enzyme-linked immunosorbent assay ( ELISA ) . In brief , tachyzoites of the ME49 strain were sonicated and used to coat high-binding capacity ELISA plates ( Corning , location ) with a suspension containing 1×106 parasites/ml in PBS at 4°C for overnight . Plates were washed using PBS containing Tween-20 ( 0 . 05% ) . Sera were diluted 1∶500 in 1 . 0% bovine serum albumin ( BSA ) in PBS and incubated with coated plates for one hour at room temperature . After incubation , plates were washed three times using PBS with Tween-20 ( 0 . 05% ) and incubated for one hour at room temperature with horseradish peroxidase-conjugated goat anti-mouse IgG ( Amersham Pharmacia/GE Healthcare , USA ) diluted 1∶10 , 000 in PBS . Following washing , peroxidase reactivity was detected with 100 µl of equal mix of substrate-A and B ( ECL , PerkinElmer , Waltham , MA , USA ) for 20 min in the dark . The reaction was stopped using 50 µl of 2 M H2SO4 and the plates were scanned for absorbance at 490 nm . Samples were performed in triplicate wells and repeated twice . Mean values for each sample were compared to a set of 6 non-infected animals ( negative controls ) to establish significance levels for positivity ( 95% ) , as described previously [27] . Cumulative mortality was defined as = ( the number of animals that succumbed/total number of animals that were infected ) ×100 . For strains used to study transmission dynamics , we specifically chose strains with a low passage history , all within 5 passages of primary isolation . To develop tissue cysts for transmission studies , CD1 mice were infected by i . p . injection of tachyzoites of T . gondii strains grown in culture . For highly virulent strains , CD1 mice were treated with sulfadiazine ( 0 . 4 to 0 . 6 mg/ml ) supplemented drinking water for 10 days ( 5 to 15 days post-infection ) . At 6 to 8 weeks for post-infection , mice were euthanized and brains were removed and homogenized in sterile PBS . The number of tissue cysts was determined by fluorescent staining with fluorescein isothiocyanate-conjugated Dolichos Biflorus lectin ( DBL ) and microscopic counting . In brief , an aliquot of the homogenized brain was fixed in 5% formalin and permeabilized with 0 . 25% Triton X-100 . Samples were washed three times by centrifugation ( 400 g for 10 min ) at room temperature in PBS , followed by blocking in 10% FBS for 20 min and staining with DBL ( Sigma , St , Louis , MO ) . Samples were examined and cysts counted using a wide-field epifluorescence microscope . Groups of 5 CD1 mice were used for oral challenge with ∼20 cysts each , as described previously [8] . At 30 days post-infection , sera were collected from surviving mice , and tested by ELISA , as described above . Oral transmission was defined as the number of animals that were infected ( i . e . those that became sick and died plus those that were serologically positive ) , over the total number of animals challenged ×100 , as defined previously [8] . To monitor transmission in the definitive host , Toxoplasma-free laboratory raised cats were fed orally with brain homogenates containing tissue cysts of T . gondii ( 100–800 cysts per animal ) and feces were examined for shedding of oocysts between days 4–21 , as described previously [28] . Cats used in these experiments were aged 2–4 mos and of either sex and were part of a colony maintained at the USDA , as described previously [28] . For practical reasons , the experiments were staggered to assure similar age of the cats , and similar duration of chronic T . gondii infection in mice prior to feeding ( 1–2 mos after establishment of infection ) . Serological conversion of the inoculated cats was determined by the modified agglutination test as described previously [29] . For select cats , infections were tested by bioassay in Swiss Webster mice . Samples of heart , liver , lung , and spleen ( total 50 g ) were homogenized in saline , digested in 0 . 5% trypsin or pepsin , and inoculated s . c . into groups of 5 mice . Six weeks later mice were bled , tested serologically and their brains were examined for tissue cysts , as described above . For cats that were positive for shedding , the total number of oocysts shed was determined by pooling positive stool samples , making dilutions , and counting oocysts by microscopic examination . To monitor viability , oocysts were washed in HBSS and treated with 10% ( vol/vol ) Chrolox ( 8 . 25% NaOCl ) for 30 min at 4°C . After further washing , oocysts were vortexed in a suspension of 0 . 3–0 . 5 g of 0 . 5 mm glass beads in 1 ml of PBS to mechanically disrupt the oocyst wall . Oocysts were further treated with 5% sodium taurodeoxycholate ( Sigma-Aldrich ) in HBSS for 10 min at 37C . Following washing , oocysts were plated on HFF monolayers and the number of plaques formed determined at 10 days post-infection . Comparison of the frequency of oocyst shedding in cats was analyzed using Fisher's exact test as computed using Prism ( GraphPad ) . To determine the minimum sample sizes needed to detect a significant difference at P<0 . 05 , we used Pearson's Chi-squared test with continuity correction and assumed the same effect size as is evident in the current data ( . 59 ) . Estimates were calculated with 1 degree of freedom under a chi-square test using R as described ( http://www . statmethods . net/stats/power . html ) . Power analysis was computed using pwr: Basic functions for power analysis . R package version 1 . 1 . 1 . ( http://CRAN . R-project . org/package=pwr ) .
French Guiana consists of two main geographical environments: a wild environment characterized by the dense , near-inaccessible , and unpopulated Neotropical rainforest that covers 96% of the country , and an anthropized environment that lies along a thin coastal strip where the majority of the human population lives in towns , villages or at the edge of forests that have been cleared for farming ( Figure 1 ) . Seven strains from the rainforest environment ( TgH18001 , TgH18002 , TgH18003 , TgH18007 , TgH18008 , TgH00002 , and TgH00009 ) were isolated from blood samples of patients with severe toxoplasmosis between 1991 and 2004 [13] , [19] , [20] . All these patients were otherwise healthy adults who were infected naturally in the rainforest . The locations of these wild strains are available only for three strains ( TgH18003 , TgH18007 , and TgH18008 ) , which are shown precisely , while the remaining wild strains are grouped in a single central location ( Figure 1 ) . Twelve strains were collected from animals in the anthropized environment of French Guiana in 2009 [10]: seven were from stray dogs ( TgA18002 , TgA18003 , TgA18006 , TgA18012 , TgA18017 , TgA18020 and TgA18030 ) , three were from chickens ( TgA18016 , TgA18027 , and TgA18028 ) , one was from a stray cat ( TgA18034 ) , and one was from a greater grison ( Galictis vittata ) ( TgA18005 ) . The 12 strains from the anthropized environment were collected within a 30-km radius in the Cayenne area , the main city of French Guiana ( Figure 1 ) . Previous studies using microsatellite ( MS ) markers have emphasized that strains from the anthropized regions of French Guiana show lower allelic diversity than those form the wild , and that these two groups clustering separately using phylogenetic analysis [10] . To provide a broader comparison of these two groups of isolates from French Guiana , we compared them to representative strains from each of the major 15 HGs , recently defined by a combination of different genetic markers [4] . These reference strains were chosen to represent not only different HG , but also different geographic regions , and hosts ( see Table S1 ) . We analyzed the MS patterns for 57 strains , including 19 from French Guiana ( 7 wild , 12 anthropized ) and clustered them using Neighbor-joining . As shown in Figure 2 , the wild ( green ) and anthropized ( red ) isolates group to distinct branches of the unrooted tree together with various HGs . The anthropized strains lie on a common branch with HG3 and HG9 , and yet they are slightly distinct from each of these groups ( Figure 2 ) . In contrast , the wild strains group with a variety of divergent HGs , including HG5 and HG10 that have previously been found in French Guiana and Brazil [4] . There were two exceptions to this grouping , wherein anthropized strains clustered with the wild strains ( i . e . TgA18005 , Tg18006 ) ( Figure 2 ) . Consistent with previous reports [10] , anthropized strains show a high degree of similarity to each other , while the wild strains were found on separate long branches ( Figure 2 ) . The dissimilarity of wild strains of French Guiana isolates might influence their grouping in the MS analysis due to a long-branch effect . Therefore , we also examined the genetic composition of French Guiana strains using a set of introns from housekeeping genes that have been used previously to analyze the population structure of T . gondii [4] , [23] . This intron-based analysis provides an independent estimate of divergence based on single nucleotide polymorphisms ( SNP ) in selectively neutral loci . Analysis of genetic diversity based on intron polymorphisms using a neighbor-network revealed that anthropized strains were closely grouped with HG3 , while wild strains grouped with HG5 or HG10 . These groupings were supported by shared regions of the networks , and did not simply reflect long-branch attractions . As noted above for the microsatellite analysis , two strains from the anthropized environment ( i . e . TgA18005 , TgA18006 ) grouped with the wild strains in HG 5 , and one strain ( i . e . TgA18030 ) grouped with HG8 ( Figure 3 ) . Based on the relative branch lengths , anthropized strains also appeared much more tightly clustered , while wild strains were more divergent , consistent with the MS analysis above and reported previously [10] . The similarity of anthropized strains from French Guiana to HG3 suggested that these strains might share other traits common to this group; for example type III strains belong to HG3 and also harbor monomorphic Chr1a [15] , [23] . To determine the composition of Chr1a in FG strains , we sequenced regions that were scattered across the chromosome and which have been used previously to characterize its genetic makeup [15] , [23] . Analysis of SNPs within these regions by Neighbor-network analysis revealed that the majority of anthropized strains contained a monomorphic version of Chr1a , similar to HG1 , 2 , 3 ( Figure 4 ) . The exceptions to this pattern were strains TgA18006 and TgA18005 , which as discussed below are wild-like strains collected from an anthropized environment . This monomorphic Chr1a is also found in HG4 , 7 , 8 , 9 , as reported previously [15] , [23] . In contrast , the wild strains from French Guiana contained highly divergent versions of Chr1a , similar to other HG5 and HG10 strains ( Figure 4 ) . The similarity of anthropized strains to HG3 and the presence of monomorphic Chr1a suggested they might represent a pocket of clonal type III strains in South American , despite the fact that this lineage is generally confined to North America and to a lesser extent Europe [4] . Hence , we analyzed a set of restriction fragment polymorphism ( RFLP ) markers that has previously been used in typing T . gondii strains [4] . Based on this analysis , the anthropized strains did not match type III strains ( or HG3 ) but rather were mixtures of alleles from types I , II , and III ( Table 1 ) . This suggests that they may have arisen by natural recombination among the clonal lineages , or at least have a similar parental origin to the clonal types that dominate in North America and Europe . This mixed ancestry appears to have a predominant type III signature , consistent with the grouping by microsatellite , intron , and RFLP markers . The clonal lineages type I , II and III ( HGs 1 , 2 , and 3 ) differ substantially in their acute virulence in mice , with type I strains being acutely virulent , type II strains exhibiting intermediate virulence , and type III being avirulent in laboratory mice [2] . Previously genetic studies have shown that these phenotypic differences are largely due to the combination of alleles at a few polymorphic loci that encode rhoptry kinases or pseudokinases [30] . We examined the ability of a subset of the anthropized and wild strains from French Guiana to infect outbred laboratory mice and cause acute infection . The wild strains from French Guiana exhibited high levels of acute virulence ( Table 2 ) , consistent with previous reports that they may cause more severe disease [19] . In contrast , challenge with anthropized strains resulted in a wide range of mortality from 0–100% for different strains ( Table 2 ) . The high degree of lethality seen in some anthropized strains ( i . e . TgA18020 ) is not consistent with type III strains , which normally do not result in any mortality at even much higher doses [26] . The mixed genotypes of these anthropized strains ( Table 1 ) , suggests that their more virulent phenotypes may be a consequence of their recombinant genotypes . To evaluate if the outcome of mouse infections was related to previously characterized virulence traits , we analyzed polymorphism in ROP18 , ROP5 , ROP16 and GRA15 . All of the anthropized stains contained a type III allele at ROP18 , which has previously been associated with the avirulence of type III strains [26] , [31] . The anthropized strains also contained the shared alleles characteristic of type I and III strains at ROP5 , ROP16 and GRA15 ( Table 2 ) . Hence , the range of virulence phenotypes of anthropized strains cannot be explained by the known virulence determinants of T . gondii . The wild strains from French Guiana contained divergent alleles at both ROP5 and ROP18 ( Table 2 ) , indicating they contain additional allelic diversity not previously seen in the clonal lineages . We also tested the ability of strains from French Guiana to cause chronic infection in mice and to generate tissue cysts that were orally infectious to mice and to cats . For strains that exhibited high virulence , mice were treated with sulfadiazine to prevent death . All of the strains produced normal appearing tissue cysts in the brains of chronically infected animals and these were orally infectious to naïve mice ( Table 2 ) . One potential model to explain the preponderance of Chr1a in the environment would be if it favors infection of cats or leads to greater fecundity ( i . e . higher oocyst production ) in cats . To test this possibility , the brains from chronically infected mice were fed to each of two cats per parasite strain . Feeding of tissue cysts from all four anthropized strains harboring the monomorphic Chr1a resulted in oocyst shedding by cats , although for two strains only one of the two cats challenged were positive ( in total 6 of 8 cats challenged shed oocysts ) ( Table 2 ) . The two animals that did not shed oocysts also did not become infected as shown by bioassay ( Table 2 ) . In contrast , only one of three divergent strains , harboring divergent forms of Chr1a , resulted in shedding of oocysts when fed to cats , and this only occurred in one of two cats challenged , while the remaining animal did not become infected as shown by its low serum titer ( Table 2 ) . For two of the divergent strains ( TgA18006 , TgH18008 ) , one of two cats fed tissue cysts for each strain likely became infected as shown by a positive antibody titer , yet none of the four cats shed oocysts ( Table 2 ) . Overall , 5 of 6 cats fed tissue cysts from these divergent strains failed to shed oocysts ( Table 2 ) . Among cats that shed , the number of oocysts and viability were similar ( Table 2 and data not shown ) . The low level of infectivity of strains harboring a divergent Chr1a is unexpected , as cats are normally quite permissive for infection when fed tissue cysts [32] . Therefore , we tested the null hypothesis that presence of the monomorphic Chr1a leads to greater transmission in cats using Fisher's exact test , which nearly achieved statistical significance ( P = 0 . 0513 , 1-tailed test ) , suggesting the observed outcome is unlikely to be due to chance . These findings support the hypothesis that anthropized strains containing a monomoprhic versio of Chr1a have a high capacity for productive infections in cats , while this trait is more restricted in wild strains harboring divergent forms of Chr1a .
Our findings reveal that T . gondii strains in French Guiana comprise two very different populations that are stably maintained despite their close geographic proximity . Highly divergent strains are typical of the jungle environment , while less diverse strains predominate in anthropized environments , with few exceptions . Consistent with previous reports , these anthropized strains are related to clonal type III , and are similar to other strains seen in the Caribbean [10] . However , they are not simply type III strains seen in North America , but instead have genotypes consisting of mixtures of alleles . These strains exhibited a range of acute virulence in the mouse , which cannot be explained by previous virulence determinants . Anthropized strains also share a monomorphic version of Chr1a , while wild strains are highly divergent , and this feature correlated with efficient oocyst shedding in cats , suggesting this chromosome may be important in transmission . The majority of strains isolated in the anthropized environment were similar to HG3 and HG9 , two related lineages that predominate in North America and South America , respectively [4] . Anthropized French Guiana strains have previously been analyzed using microsatellite makers and were shown to be similar to type III-related genotypes that are common in the Caribbean [10] . Our studies using a wider range of markers demonstrate that they are not simply variants of type III , but rather contain alleles from different genetic types at different loci . This pattern suggests that they may be derived from genetic crosses in the wild between the members of the clonal lineages , or between a unique parental strain and a type III strain . Further analysis of their genetic composition may reveal their ancestry and relationship to existing HG . Although these anthropized strains differ from clonal type III strains , they have a highly conserved version of Chr1a that has previously been seen in both clonal North American strains and in non-clonal , genetically divergent South American strains [17] , [23] . The mixed ancestry of French Guiana anthropized strains suggests they may have acquired Chr1a by interbreeding , perhaps from a type III-like parental donor . There were several exceptions to the pattern that anthropized strains in French Guiana resemble HG3 and HG9 . Notably , sample TgA18030 is more typical of HG4 and HG8 , which are common in other regions of South America such as Brazil [4] . Additionally , two strains collected in the anthropized environment group much more closely with the wild strains ( i . e TgA18006 , TgA18005 ) , both in the diversity of their genome as a whole and in having a divergent Chr1a . For this reason , they are denoted as wild ( W ) strains here . These strains were isolated from a dog ( TgA18006 ) and a greater grison ( TgA18005 ) , in different geographic regions ( Figure 1 ) . The grison is a mustelid that has an omnivorous diet and typically inhabits aquatic regions of tropical savannahs , but is also known to enter areas that have active farming . The animal sampled here was in an anthropized environment , but may well have acquired its infection from the nearby jungle . Likewise , the close proximity of the jungle environment to developed areas of French Guiana makes it possible that the dog carrying TgA18006 was infected in the wild environment , despite being sampled in an anthropized region . These two examples also demonstrate that mixing of genotypes is likely to occur along this interface , something that has been suggested previously [10] . As such , attributes found in strains from the wild or anthropized environments , including virulence determinants , may spread from one population to another . Isolates from wild regions of French Guiana were acutely virulent in the outbred mouse model , similar to strains from the related HG5 and HG10 [8] . The wild strains studied here contain divergent alleles at ROP5 an ROP18 , suggesting there may be important functional differences conveyed by these new alleles . The anthropized strains exhibited a wide range of virulence in the mouse model consistent with their mixed genotypes and distinct from what is expected from type III strains [26] . The intermediate to high virulence phenotype of some anthropized strains is similar to TgPgUs15 ( also known as P89 ) , a HG9 strain that is highly virulent in the mouse model despite having an allele of ROP18 that is associated with avirulence [8] , [17] . A similar pattern was seen here among anthropized strains that have a type III allele at ROP18 and yet were highly virulent in naïve , outbred mice . ROP18 encodes a serine threonine kinase that targets innate immunity and protects parasites within interferon-activated macrophages [33] . An upstream region in the genome of type III strains is associated with under-expression and avirulence [34] . Although the anthropized strains studied here contain type I alleles at ROP5 , a cluster of pseudokinases that controls ROP18 [35] , [36] , this is unlikely to explain their high level of virulence given the presence of a type III allele at ROP18 . Collectively , these findings suggest that there are additional virulence traits in some anthropized strains such as those sampled here that cannot be easily explained by factors that have been identified in types I , II and III [30] . Further genetic studies will be necessary to define the genes , or combination of genes , that contribute to virulence in both the anthropized and wild strains from French Guiana , and other regions of South America . Our studies reveal that the monomorphic Chr1a is more widespread than previously thought , being found in strains of mixed ancestry in anthropized regions of French Guiana . In each of the environments where the monomorphic Chr1a is prevalent , it is associated with anthropized environments . Clonal isolates ( i . e . HG 1 , 2 , and 3 ) containing the monomorphic Chr1a in North America and Europe have been sampled from human infections , companion , or domestic animals in close proximity with urban or agricultural environments . In South America , monomorphic Chr1a is found in HG 4 , 8 , and 9 strains , most of which have been isolated from feral cats ( Felis catus ) and dogs and chickens in anthropized environments . A highly similar version of Chr1a also exists in China , where it is found in a predominant clonal lineage isolated from farming communities [4] , [17] . In other regions , chimeric versions of Chr1a have been found in HG6 and HG14 strains , which are found in Africa and South America [4] , [17] . As well chimeric versions of Chr1 have been seen in HG12 , a newly described clonal lineage in North America [3] , [4] , [17] . Overall , the number of variants of Chr1a is small , and most lineages have inherited all or a large part of this chromosome from a single source . The exceptions are the highly divergent strains from lineages 5 , 10 , and 15 [4] , [17] , including the wild strains sampled here from French Guiana . The wide spread distribution of the monomorphic Chr1a among genetically divergent lineages suggests several possible models for its unusual prevalence . It is possible that it was introduced into new environments by the spread of humans , together with domestication of a small number of animal species . Recent human mass migrations include the Neolithic agricultural revolution ca 10 , 000 years ago and widespread global colonization over the past 500 years . Travel between regions may have led to introduction of pest animals ( i . e . mice and rats ) , domestic cats , or livestock , possibly carrying particular genotypes of T . gondii in the process . It has previously been noted that livestock serve as hosts for a number of parasites that show low genetic diversity [37] , and this combined with human migrations , may serve to disseminate conserved strains of T . gondii across widely different geographic regions . However , this process has not been sufficient to erode the marked genetic divergence that is seen between North and South American strains of T . gondii . It also does not adequately explain the existence of the identical version of Chr1 in lineages that otherwise differ substantially in their genetic makeup and ancestry [17] . Rather this pattern suggests that once introduced into a lineage , Chr1 is advantageous . In North America , Chr1a is retained in inbreeding populations that show marked clonality . In contrast , in South America , it is retained in outbreeding populations that show high levels of diversity in their genome as a whole . One potential trait that might influence the spread of strains bearing Chr1a would be if it conferred differences in fecundity or fertility in different felids , the only known definitive host [1] . Previous studies have shown that T . gondii strains often lose the ability to complete the life cycle within cats with extensive passages [38] , hence we were careful to examine only low-passage isolates here . To test the possibility that Chr1a is an adaptation for transmission in domestic cats , we challenged cats with tissue cysts from either anthropized or wild strains from French Guiana and examined oocyst shedding . Anthropized strains that harbored the monomorphic Chr1a showed a high capacity for oocyst shedding , where 6 of 8 animals fed tissue cysts led to shedding of oocysts . This pattern is consistent with previously studied clonal lineages , which all share the monomorpic Chr1a [32] , [39] . In contrast , only one of six cats fed T . gondii strains harboring divergent Chr1a shed oocysts , indicating they were less capable of causing productive infections in domestic cats . Although Chr1a is strongly associated with transmission in the domestic cat , not all monomorphic strains led to oocyst shedding , nor did all divergent ones fail to do so . Such variation suggests that although Chr1a may influence this trait , that other genes are likely also involved . None of the strains studied here are isogenic and so it is expected that complex phenotypes might not track completely with any one chromosome or gene . Additionally , although we have controlled for passage in the laboratory , these the strains likely differ in passage history in the wild . They were isolated from different hosts and it is possible that such variable histories might influence subsequent transmission in the domestic cat . Although there was a strong association between the monomorphic Chr1a and cat transmission , it slightly exceeded the cutoff needed to achieve statistical significance by the normally accepted value of P<0 . 05 . Such limitation might be overcome by larger sample sizes , but this is offset by ethical concerns about the use of more animals . To provide a power estimate , we performed Pearson's Chi-squared test with continuity , which indicated that ∼30 animals would be needed to have 90% power to reliably detect an effect of this size at P<0 . 05 . Thus confirmation of the trend detected here would require almost doubling the number of animals used in the present study . Moreover , this would only test the association between the monomorphic Chr1a and cat transmission among this relatively small set of strains from French Guiana , but not establish if this pattern were true on a wider basis . At present , the only other example of a wild strain that showed low capacity to cause oocyst shedding when tissue cysts were fed to cats is an unrelated isolate from an Alaskan black bear ( Ursus americanus ) [40] . RFLP typing of this isolate indicates it has a mixed lineage and is not a member of one of the predominant clonal lineages in North America [40]; suggesting that it is a member of a divergent lineage , although the genotype of Chr1a in this strain is unknown . Our findings support the hypothesis that variation in Chr1a is associated with enhanced cat transmission , and justify more widespread testing of this pattern among other strains of T . gondii . Domestic house cats are found in French Guiana among anthropized environments , although specific data on their densities , home range , etc . , are not available . In contrast , jungle environments are unlikely to be populated by domestic cats but instead have wild felids including ocelot ( Leopardus pardalis ) , oncilla ( Leopardus tigrinus ) , Margay ( Leopardus wiedii ) , cougar ( Puma concolor ) , jaguarundi ( Puma yagouaroundi ) and jaguar ( Panthera onca ) [41] . Although T . gondii has been isolated from the tissues of a jaguar in French Guiana [20] , the role of wild felids in natural transmission in the jungle is unknown . Hence , differences in transmission among felid species may be one factor that maintains the separation of wild and anthropized strains in French Guiana . Wild and anthropized strains also showed similar abilities to cause chronic infection in laboratory mice , and they were equally capable of producing tissue cysts that were orally infectious to naive mice . However , strains of laboratory mice are extremely closely related [42] , and they do not capture the genetic diversity of wild house mice from which they were derived . Hence , Chr1a may be adapted for transmission in natural rodent species such as house mice ( Mus musculus ) , or species of rats ( i . e . Ratus norvegicus or Rattus rattus ) that commonly inhabit anthropized environments in many different geographic regions . In contrast , wild strains of T . gondii are likely transmitted by distinct species of rodents that populate jungle environments [43] , [44] . Future studies designed to sample a range of intermediate hosts , as well as the species of felids involved in transmission , will be needed to address the hypothesis that Chr1a is an adaptation for transmission among distinct hosts in the wild . French Guiana is ideally suited for such sampling as it contains highly divergent and genetically homogenous lineages that exist in close proximity in a region of high transmission . Moreover , Chr1a is strongly associated with both genotype and environment along a well-defined boundary between anthropized and wild environments . Such studies on the ecology of natural transmission have the potential to inform us about the spread of T . gondii strains , and the virulence traits they carry , among natural hosts that lead to zoonotic infections . | Toxoplasma gondii is a widespread parasite of animals that is easily transmitted to humans . Previous studies have shown that human infections in jungle areas of French Guiana are often quite severe , unlike most human infections that are characterized by mild symptoms in healthy adults . Here we characterized the genetic makeup of strains from French Guiana and confirm that while genetically homogeneous strains exist in anthropized environments , highly divergent and pathogenic isolates are found in jungle environments . The geographic separation of strain types is also mirrored in conserved genomic regions , including a monomorphic version of chromosome 1a ( Chr1A ) , which has previously been associated with the spread of different lineages around the world . Strains harboring the monomorphic Chr1a showed greater potential for transmission in domestic cats , which may contribute to their prevalence in anthropized environments . Our findings also revealed large differences in acute virulence of French Guiana isolates in the laboratory mouse , and these differ from known genetic mechanisms that have been defined previously . Hence , the ability of some strain types to expand in the environment as a consequence of enhanced transmission may also lead to the spread of virulence determinants . | [
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] | 2014 | Geographic Separation of Domestic and Wild Strains of Toxoplasma gondii in French Guiana Correlates with a Monomorphic Version of Chromosome1a |
Caspases are cysteine proteases that can drive apoptosis in metazoans and have critical functions in the elimination of cells during development , the maintenance of tissue homeostasis , and responses to cellular damage . Although a growing body of research suggests that programmed cell death can occur in the absence of caspases , mammalian studies of caspase-independent apoptosis are confounded by the existence of at least seven caspase homologs that can function redundantly to promote cell death . Caspase-independent programmed cell death is also thought to occur in the invertebrate nematode Caenorhabditis elegans . The C . elegans genome contains four caspase genes ( ced-3 , csp-1 , csp-2 , and csp-3 ) , of which only ced-3 has been demonstrated to promote apoptosis . Here , we show that CSP-1 is a pro-apoptotic caspase that promotes programmed cell death in a subset of cells fated to die during C . elegans embryogenesis . csp-1 is expressed robustly in late pachytene nuclei of the germline and is required maternally for its role in embryonic programmed cell deaths . Unlike CED-3 , CSP-1 is not regulated by the APAF-1 homolog CED-4 or the BCL-2 homolog CED-9 , revealing that csp-1 functions independently of the canonical genetic pathway for apoptosis . Previously we demonstrated that embryos lacking all four caspases can eliminate cells through an extrusion mechanism and that these cells are apoptotic . Extruded cells differ from cells that normally undergo programmed cell death not only by being extruded but also by not being engulfed by neighboring cells . In this study , we identify in csp-3; csp-1; csp-2 ced-3 quadruple mutants apoptotic cell corpses that fully resemble wild-type cell corpses: these caspase-deficient cell corpses are morphologically apoptotic , are not extruded , and are internalized by engulfing cells . We conclude that both caspase-dependent and caspase-independent pathways promote apoptotic programmed cell death and the phagocytosis of cell corpses in parallel to the canonical apoptosis pathway involving CED-3 activation .
The elimination of unnecessary or dangerous cells is fundamental to development , tissue homeostasis and disease mitigation in multicellular organisms . The primary mechanism of cell elimination is apoptosis , a form of cell suicide that was originally defined by evolutionarily conserved morphological characteristics that include chromatin condensation , shrinkage of the cytoplasmic volume and membrane blebbing [1] and by biochemical features like phosphatidylserine exposure and DNA fragmentation [2] , [3] . Apoptosis serves as a highly controlled mechanism for the removal and degradation of damaged or unnecessary cells , and blocking apoptosis can lead to catastrophic forms of cell death , such as necrosis , which can cause dangerous inflammatory responses [4] . The discovery of the CED-3 caspase as a cell-autonomous executioner of programmed cell death in the nematode Caenorhabditis elegans led to the paradigm that the caspase family of cysteine proteases drives apoptosis through the cleavage of substrate proteins at specific peptide sequences [5] , [6] . Indeed , caspases have evolutionarily conserved roles in apoptosis throughout metazoa [7] . Despite the compelling causal link between caspases and apoptosis , a growing body of evidence indicates that apoptosis can occur in the absence of caspases [4] . For example , mouse cells lacking Apaf-1 , an activator of the apical caspase Caspase-9 , which in turn activates effector caspases , can undergo apoptosis in response to pro-apoptotic stimuli [8] . In the presence of caspase inhibitors , TNF ( tumor necrosis factor ) can induce a form of cell death termed necroptosis , which exhibits characteristics of both necrosis and apoptosis [4] , [9] . The mitochondrial flavoprotein AIF ( apoptosis-inducing factor ) is thought to promote apoptotic cell death in mammals even in the presence of caspase inhibitors [10] . Furthermore , cell death with aspects of apoptotic morphology occurs in non-metazoans , including unicellular eukaryotes and prokaryotes , that lack clear caspase homologs [11] , [12] . Thus , it is possible that apoptosis , as defined morphologically and biochemically , can occur in the absence of caspases . A standard approach to assaying the caspase-dependence of apoptotic stimuli in tissue cell culture is through the pharmacological inhibition of caspases . However , it is difficult to prove that caspase activity is completely blocked in such experiments , and it is possible for caspase inhibitors to trigger non-apoptotic forms of cell death [13] . Studies of caspase-independent apoptosis in metazoans are also complicated by the existence of multiple caspases with potentially redundant functions in promoting cell death . The human genome , for example , encodes at least 10 caspase homologs , seven of which ( caspases-2 , -3 , -6 , -7 , -8 , -9 and -10 ) have demonstrated roles in apoptosis [14] . The genome of Drosophila melanogaster encodes seven caspase homologs ( dcp-1 , dronc , drice , dredd , decay , damm and strica ) [7] , several of which are essential for organismal viability . The C . elegans genome encodes three caspase homologs ( csp-1 , csp-2 and csp-3 ) in addition to ced-3 [15] . Therefore , the use of mutant animals or cell lines deleted for one or two caspases might not eliminate all caspases expressed within a specific cell . Furthermore , since caspases have different substrate specificities [16] , the use of a chemical substrate-competitive caspase inhibitor might not completely block all caspase activity . Ideally , experiments that test whether apoptosis can occur in the absence of caspases should be performed using mutant animals or cells that are genetically deleted of all caspase homologs . In this regard , C . elegans is an excellent animal for studies of caspase-independent programmed cell death , because: ( 1 ) there are several examples of ced-3-independent programmed cell death in C . elegans [17]–[19]; ( 2 ) mutants of ced-3 , csp-1 , csp-2 and csp-3 are viable [18]–[23]; and , ( 3 ) it is relatively easy to generate multiply mutant C . elegans strains . The ced-3 caspase gene is required for most programmed cell deaths that occur during C . elegans development [5] , [20] . However , a small number of cells die in animals carrying null mutations of ced-3 . The male-specific linker cell , which facilitates the connection of the vas deferens to the cloaca and then dies , undergoes a non-apoptotic cell death that bears morphological features ( e . g . , nuclear membrane crenellation ) not seen with other C . elegans programmed cell deaths and that occurs in ced-3 mutants as well as in animals doubly mutant for ced-3 and csp-1 , csp-2 or csp-3 [18] , [20] , [24] . We recently showed that a subset of cells fated to die in the C . elegans embryo are eliminated from ced-3 mutants via a caspase-independent shedding mechanism [19] . Interestingly , the shed cells appear apoptotic , exhibiting chromatin condensation , TUNEL-reactive DNA degradation and phosphatidylserine exposure despite the absence of all four caspases . Unlike other apoptotic programmed cell deaths of C . elegans , the shed cells do not undergo phagocytosis by engulfing cells; instead , they are extruded from the developing embryo . By contrast , a small number of apoptotic cell corpses are visible in the heads of ced-3 larvae [17] . Like other programmed cell deaths of C . elegans , these ced-3-independent cell corpses have a refractile appearance when viewed with Nomarski optics and are not extruded from the animal , suggesting that a ced-3-independent cell-killing activity contributes to these typical programmed cell deaths . The other caspase homologs , csp-1 , csp-2 and csp-3 , are obvious candidates for driving this ced-3-independent cell-killing activity . However , it has recently been reported that csp-2 and csp-3 inhibit apoptosis in the germline and soma , respectively [22] , [23] . To date , the C . elegans caspase homolog csp-1 has no known function in vivo , including in apoptosis . An isoform of CSP-1 can cleave and possibly activate the CED-3 pro-protein in vitro [15] . We tested whether csp-1 can promote or inhibit programmed cell death and whether it is regulated by the canonical programmed cell death pathway that activates ced-3 . We found that csp-1 encodes a pro-apoptotic caspase activity that promotes programmed cell death independently of the CED-3 caspase , CED-4 ( the Apaf-1 homolog that activates CED-3 ) , and CED-9 ( a Bcl-2 family protein that negatively regulates CED-3 activation via inhibition of CED-4 ) . Furthermore , we tested whether csp-1 , csp-2 and csp-3 contribute to the ced-3-independent cell-killing activity that generates cell corpses in the heads of ced-3 mutant larvae and found that these apoptotic cell deaths can occur in the complete absence of caspases . Thus , during C . elegans development programmed cell death followed by cell-corpse engulfment is achieved through three redundant pathways: ( 1 ) a ced-3-dependent pathway; ( 2 ) a csp-1-dependent pathway , which is not regulated by the canonical apoptosis pathway that controls ced-3; and , ( 3 ) a caspase-independent pathway .
The C . elegans genes csp-1 , csp-2 and csp-3 are paralogs of the pro-apoptotic ced-3 caspase gene [15] , which is required for most programmed cell deaths in the worm [5] , [20] . Given the conserved role of caspases in apoptosis , we tested csp-1 , csp-2 and csp-3 for roles – both pro- and anti-apoptotic – in programmed cell death . We used mutations of csp-1 ( n4967 and n5133 ) and csp-2 ( n4871 ) that completely remove the genomic sequences encoding their respective predicted caspase active sites ( SACRG in the CSP-1 protein , and VCCRG in the CSP-2 protein ) and therefore eliminate any potential caspase activity encoded by these genes ( ref . [19]; Figure 1A ) . csp-3 lacks a caspase active site ( ref . [15] , [22]; Figure 1A ) ; we used the csp-3 deletion mutation n4872 , which is likely a null allele [19] . Recently , it was reported that mutations in csp-2 and csp-3 cause ectopic cell deaths in the germline and soma , respectively , and hence that csp-2 and csp-3 inhibit apoptosis [22] , [23] . We therefore tested whether csp-1 mutants have ectopic cell deaths indicative of a loss of anti-apoptotic function . Using Nomarski optics and a Pmec-3::gfp transgene that expresses GFP in the six touch neurons ( AVM , two ALM , PVM and two PLM neurons ) in addition to the FLP and PVD neurons , we examined csp-1 mutants for missing cells that normally survive . We observed that csp-1 ( n4967 ) mutants contained a full complement of touch neurons and pharyngeal cells ( Table S1 ) . We also noted that csp-1 ( n4967 ) failed to cause ectopic cell deaths in sensitized animals carrying the loss-of-function mutation n2812 in the anti-apoptotic gene ced-9 , a homolog of human BCL2 ( Table S1; data not shown ) . These results indicate that csp-1 does not have an obvious anti-apoptotic function in the soma . Consistent with a previous report that csp-2 does not affect somatic cells [23] , a mutation in csp-2 did not cause ectopic cell deaths in the somatic cells we examined ( Table S1 ) . However , we failed to observe the ectopic cell deaths in csp-3 mutants previously reported [22] . Ectopic somatic cell deaths have also been noted in animals with loss-of-function mutations in ced-9 [25] or tat-1 [26] , [27] , which encodes an aminophospholipid translocase required for the asymmetric distribution of phosphatidylserine on the inner leaflet of the plasma membrane . As expected , we found that ced-9 mutant larvae were missing pharyngeal cells and many touch neurons: more than 80% of PLM neurons were not present in ced-9 ( n2812 ) larvae ( Table S1 ) . However , we failed to detect the previously reported ectopic cell-death defect of tat-1 mutants ( ref . [26] , [27]; Table S1 ) ; we used the same deletion alleles for csp-3 and tat-1 and assayed the same cells that had been characterized in the previous studies . To determine whether the C . elegans caspase homologs csp-1 , csp-2 or csp-3 promote programmed cell death in the soma , we examined animals carrying csp deletion mutations for extra cells that failed to undergo programmed cell death in the anterior pharynx . As many as 16 extra cells can be counted in the anterior pharynges of mutants with strong defects in programmed cell death , e . g . , ced-3 ( n3692 ) ( ref . [28]; Table 1 ) . Single mutations in csp-1 , csp-2 or csp-3 failed to cause detectable defects in programmed cell death ( Table 1; data not shown ) . However , we observed that mutations in csp-1 ( but not csp-2 or csp-3 ) caused the survival of pharyngeal cells in sensitized strains carrying a weak mutation in the caspase gene ced-3 ( Table 1 ) . The partial loss-of-function ced-3 mutations n2427 and n2436 cause slight and intermediate defects in apoptosis , respectively ( ref [17]; Table 1; data not shown ) . The n4967 and n5133 mutations , both of which delete the putative active site of CSP-1 ( Figure 1A ) , enhanced the cell-death defects of ced-3 ( n2427 ) and ced-3 ( n2436 ) mutants , increasing the number of extra cells in their anterior pharynges on average by 1 . 4 and 2 . 4 cells , respectively ( Table 1 ) . These results are consistent with our RNAi experiments in which csp-1B dsRNA ( which likely inactivated all csp-1 transcripts ) was injected into the gonads of rrf-3 ( pk1426 ) ; ced-3 ( n2436 ) animals and caused an enhanced cell-death defect in their progeny ( Figure 1C ) ; we used the rrf-3 mutation to increase sensitivity to RNAi [29] . The cell-death defect conferred by the csp-1 ( n4967 ) mutation was rescued by extrachromosomal arrays carrying a 9 kb genomic csp-1 fragment that included the entire csp-1 coding region , 1 . 5 kb of genomic sequence 5′ of the csp-1A translational start codon and 3 . 5 kb of genomic sequence 3′ of the csp-1A/B translational stop codon ( Figure 1B; Table S2 ) . These results demonstrate that csp-1 encodes a detectable cell-killing activity that contributes to programmed cell death in C . elegans . Mutation of csp-2 and/or csp-3 neither enhanced nor suppressed the cell-death defects of strains mutant for csp-1 and/or ced-3 ( Table 1; Table S3 ) , suggesting that csp-1 and ced-3 are the only C . elegans caspase genes with functions in somatic programmed cell deaths . The development of the anterior part of the C . elegans pharynx involves 16 programmed cell deaths , all of which appear to be sensitive to ced-3 [17] , [28] , [30] . To test whether specific pharyngeal programmed cell deaths required csp-1 , we used GFP reporters to visualize the survival of cells fated to die , specifically the sister cells of the M4 and NSM neurons . csp-1 was partially required in ced-3 ( n2427 ) or ced-3 ( n2436 ) sensitized strains for the death of the M4 sister cell ( Table S4 ) ; by contrast , mutation of csp-1 did not affect the cell deaths of the sister cells of the NSM neurons ( data not shown ) . Likewise , csp-1 did not appear to function in the postembryonic programmed cell deaths of the ventral cord or postdeirid sensilla ( Table S4 ) . We conclude that csp-1 promotes cell death in a subset of cells fated to die during C . elegans development . The csp-1 locus produces three known mRNA isoforms [15] , all of which include the sequence that encodes the presumptive caspase active site ( Figure 1A ) . The csp-1A transcript contains a long prodomain not present in the other transcripts , and it uses an alternative start site that is 3 kb 5′ to the start site of the csp-1B and csp-1C isoforms . To determine which isoforms are required for the cell-killing activity of csp-1 , we peformed experiments in which the csp-1 rescuing transgene was mutated to express: ( 1 ) the A isoform only , ( 2 ) the B and C isoforms only , or ( 3 ) a truncated version of csp-1A including only the prodomain ( PD ) . Extrachromosomal arrays engineered to express only csp-1-PD or the csp-1A isoform failed to rescue the cell-death defect of csp-1 ( n4967 ) mutants ( Figure 1B; Table S2 ) . By contrast , a csp-1 transgene lacking the csp-1A translation start codon and predicted to express only the csp-1B and csp-1C transcripts rescued the csp-1 ( n4967 ) defect in programmed cell death ( Figure 1B; Table S2 ) . Consistent with these results , transgenes expressing the csp-1B cDNA , but not the csp-1A cDNA , under the control of the mec-7 promoter efficiently killed touch neurons ( Figure 2A–2B; Table 2; data not shown ) ; we also expresed the csp-1C cDNA under the control of the mec-7 promoter and failed to observe killing of the touch neurons ( data not shown ) . Ectopic expression of csp-1B from the ser-2d and flp-15 promoters killed the OLL and I2 neurons , respectively ( ref . [31]; N . Bhatla and H . R . Horvitz , unpublished results ) . However , we noted that tm917 , a csp-1 allele that deletes coding regions of only the csp-1A transcript , enhanced significantly ( albeit weakly ) the cell-death defects of ced-3 ( n2427 ) and ced-3 ( n2436 ) mutants , increasing the number of extra cells in their anterior pharynges by 0 . 9 and 1 . 2 cells , respectively ( Table 1 ) . dsRNA targetting the csp-1A prodomain ( csp-1-PD ) caused a similar slight enhancement of the cell-death defect of ced-3 ( n2436 ) mutants ( Figure 1C ) , suggesting that , in addition to the more robust cell-killing activity of the csp-1B transcript , csp-1A might have a weak cell-killing function . The proteolytic activity of caspases requires an active-site cysteine . Previously , it was shown that the CSP-1B protein can proteolytically process CED-3 in vitro and that this enzymatic activity required the active-site ( SACRG ) cysteine of CSP-1B , C138 [15] . We tested in vivo whether C138 was necessary by assaying the touch neuron-killing activity of mutant Pmec-7::csp-1B transgenes in which C138 was changed to a serine . We observed that the ectopic cell deaths were entirely dependent on the caspase active site ( Table 2 ) . Thus , csp-1B promotes cell death via caspase activity . The cell deaths induced by a Pmec-7::csp-1B transgene resulted in cell corpses with apoptotic characteristics ( Figure 2C–2D ) . When observed with Nomarski optics , the csp-1B-induced cell deaths exhibited a refractile button-like appearance ( Figure 2C ) similar to that of developmental programmed cell deaths . Transmission electron micrographs of the cell corpses showed some contraction of the cytoplasmic volume and considerable condensation of the nuclear chromatin ( Figure 2D ) , which are general characteristics of apoptotic cells , including those generated by ced-3 cell-killing transgenes ( ref . [32]; data not shown ) . We conclude that csp-1B encodes a functional caspase that promotes programmed cell deaths with apoptotic morphology . CED-3 , like most caspases , is expressed as an inactive zymogen with an inhibitory N-terminal prodomain . Trans-auto-proteolysis of the CED-3 pro-protein at two aspartate residues removes the pro-domain and yields two subunits that form the active caspase [33] . CED-3 auto-activation is dependent on its prodomain and is facilitated by the association of two CED-3 pro-proteins within an octameric complex formed with the Apaf-1 homolog CED-4 [34]–[36] . Under normal cellular conditions , CED-4 is sequestered by CED-9 at mitochondria through a direct protein-protein interaction [37]–[39] . In response to upstream pro-apoptotic signals and the consequent expression of the BH3-domain-only protein EGL-1 , which binds to and inhibits CED-9 [40] , CED-4 is released from CED-9 and translocates to the nuclear periphery [37] , [41] , where it facilitates CED-3 activation [38] . Thus , the activation of CED-3 is controlled by an apoptosis pathway involving a BH3-domain-only protein , a member of the Bcl-2 family of apoptosis regulators , and a homolog of the apoptosome complex protein Apaf-1 . The basic elements of this apoptosis pathway are evolutionarily conserved in mammals and are responsible for the activation of caspases in response to cell-intrinsic apoptotic stimuli [7] . Consistent with the role of ced-9 in negatively regulating ced-3 activation , it was previously shown that null mutations of ced-9 enhance the touch neuron-killing activities of Pmec-7::ced-3 transgenes [32] . ( These experiments were performed using a ced-3 ( null ) background to suppress the ced-3-dependent inviability of ced-9 ( null ) animals . ) Furthermore , this enhancement is dependent on ced-4 [32] , indicating that the absence of CED-9 activates endogenous CED-4 within the touch neurons and that CED-4 activation elevates CED-3 activity . Unlike the CED-3 zymogen , CSP-1B lacks a long prodomain , suggesting that it might be activated via an alternative mechanism ( i . e . , independently of CED-4 and CED-9 ) . To determine whether these canonical apoptosis regulators control CSP-1B activation , we introduced the ced-9 ( n2812 ) mutation into ced-3 ( n3692 ) strains carrying Pmec-7::csp-1B transgenes and assessed the effect of this ced-9 null mutation on PLM survival . In contrast to its effects on Pmec-7::ced-3–mediated PLM killing , ced-9 ( n2812 ) failed to enhance PLM killing in Pmec-7::csp-1B strains with a ced-3 ( n3692 ) mutant background ( Figure 3A ) . Instead , ced-9 ( n2812 ) partially suppressed csp-1B-mediated PLM death ( Figure 3A ) . CED-9 has a poorly understood pro-apoptotic activity in addition to its anti-apoptotic role in CED-4 inhibition [42] , and it is possible that this ced-9 pro-apoptotic activity contributed to the deaths of cells expressing ectopic CSP-1B . Nevertheless , our results indicate that csp-1B-mediated cell killing , unlike ced-3-mediated cell killing , is not negatively regulated by ced-9 and suggest that CSP-1B is activated independently of CED-9 . We also observed that the expression of a Pmec-7::csp-1A transgene in ced-3 ( null ) mutant strains failed to cause PLM cell death , even in a ced-9 ( null ) background ( Figure S1 ) . These results suggest that the CSP-1A isoform ( which contains a long prodomain similar to that of CED-3 ) does not promote programmed cell death , even in the absence of the anti-apoptotic protein CED-9 . A role for csp-1A in cell death cannot be excluded entirely , as it is possible that endogenous CSP-1A requires a co-factor not present in the touch neurons to mediate cell killing . Since CSP-1B can proteolytically cleave CED-3 in vitro [15] , we tested whether the csp-1B cell-killing activing requires the endogenous ced-3 and ced-4 genes . The ced-3 ( n3692 ) and ced-4 ( n1162 ) mutations weakly suppressed csp-1B-mediated PLM death ( Figure 3B ) , and it is possible that the endogenous csp-1 can in part promote programmed cell death through ced-3 . Nonetheless , most csp-1B cell-killing activity was independent of ced-4 and ced-3 ( Figure 3B ) . Loss of endogenous csp-1 failed to suppress PLM death in strains carrying Pmec-7::ced-3 or Pmec-7::ced-4 transgenes ( Figure 3C–3D ) . Together , our results are consistent with a model in which csp-1B promotes programmed cell death at least mostly independently of and in parallel to the canonical apoptosis pathway ( Figure 3E ) . To determine which C . elegans cells express csp-1 , we directly visualized endogenous csp-1 transcripts via fluorescence in situ hybridization ( FISH ) experiments using Cy5- and ALEXA-labelled probes complementary to the csp-1B transcript ( i . e . , targeted to all csp-1 transcripts ) or to the csp-1A prodomain ( specific to the csp-1A trancript ) . To our surprise , csp-1 mRNA was not detectable in the somatic cells of wild-type or egl-1 ( n1084 n3082 ) mutant embryos , larvae or adult hermaphrodites ( data not shown ) . By contrast , csp-1 transcripts were present in the germlines of L4-stage larval and adult hermaphrodites ( Figure 4A–4B ) . This expression was restricted to the late pachytene stage of meiosis I in both L4 larval gonads ( in pachytene nuclei adjacent to differentiating sperm ) and adult gonads ( in pachytene nuclei adjacent to the bend of the gonad arm ) ( Figure 4A–4B ) . Both csp-1A and csp-1B/C transcripts were expressed in the adult pachytene germ cells , as indicated by the presence of FISH foci recognized by the csp-1A prodomain probes and foci recognized primarily by the csp-1B probes and only weakly by the csp-1A probes ( Figure 4C ) . Stochastic and ionizing radiation ( IR ) -induced germline cell deaths occur during the late pachytene stage of oocyte development in adult gonads [43] , [44] . However , csp-1 ( unlike ced-3 ) was not required for either stochastic or IR-induced germline apoptosis , even in ced-3 ( n2436 ) strains sensitized for defects in germ-cell death ( Figure 4D ) . In these experiments , apoptotic germ cells were identified using a transgene that expresses a functional GFP::CED-1 fusion protein that envelopes dying cells engulfed by the gonadal sheath [45] , [46] . We also failed to detect differences in either stochastic or IR-induced germline cell death between csp-1 mutants and wild-type animals in experiments in which apoptotic germ cells were quantified by acridine orange staining or by direct observation of their refractile morphology using Nomarski optics ( data not shown ) . We also noted that the level of csp-1 transcript expression in the germline ( as determined by FISH ) was not affected by either ionizing radiation or by mutation of egl-1 or ced-3 ( data not shown ) . Since we detected csp-1 expression in the adult germline but not in somatic cells of the embryo , we tested whether maternally supplied csp-1 transcript was necessary for the zygotic function of csp-1 in programmed cell death . Indeed , in sensitized genetic backgrounds ( ced-3 ( n2427 ) and ced-3 ( n2436 ) ) , csp-1 ( + ) progeny of csp-1 ( n4967 ) hermaphrodites ( M−Z+ animals ) had more undead pharyngeal cells than the csp-1 ( + ) progeny of csp-1 ( + ) hermaphrodites ( M+Z+ animals ) or the csp-1 ( n4967 ) progeny of csp-1 ( + ) hermaphrodites ( M+Z− animals ) ( Table 3 ) . Thus , csp-1 expressed in the maternal germline is necessary for the csp-1 pro-apoptotic activity in embryonic programmed cell deaths . Given that we could not detect csp-1 expression in either embryos or larvae , it is therefore not surprising that the postembryonic programmed cell deaths of the ventral cord and postdeirid sensilla were unaffected by mutation of csp-1 ( Table S4 ) . Most programmed cell deaths in C . elegans require ced-3 [20] . However , some cells die in mutants completely lacking ced-3 . We previously reported that a subset of cells fated to die can be eliminated from ced-3 mutant embryos via a cell-shedding mechansm [19] . In that study , we noted that cell shedding from ced-3 mutants occurs independently of csp-1 , csp-2 and csp-3: quadruple mutants lacking all four caspases also generate shed cells , indicating that cell elimination by this mechanism is completely caspase-independent [19] . Like most programmed cell deaths , the cells generated by caspase-independent extrusion are apoptotic in appearance . However , unlike caspase-dependent cell corpses , shed cells do not undergo phagocytosis by engulfing cells . The death of the male linker cell , which also occurs independently of ced-3 , is non-apoptotic and requires the heterochronic protein LIN-29 , its binding partner MAB-10 [47] , and the polyglutamine repeat protein PQN-41 ( ref . [18] , [24]; Table S5 ) . Previously it was shown that this cell death occurs in double-mutant males in which ced-3 and an additional csp gene ( csp-1 , csp-2 or csp-3 ) were inactivated [18] . We have now examined males lacking all four caspases and observed that the linker cell died in 100% of csp-3; csp-1; csp-2 ced-3 mutants ( Table S5 ) . The csp-3; csp-1; csp-2 ced-3 quadruple mutants were viable and fertile . Thus , both zygotic and maternal caspase contributions were eliminated . Our results therefore confirm that this cell death is indeed completely caspase-independent . In addition , cell corpses are visible in the heads of larvae carrying null alleles of ced-3 ( ref . [17]; Table 4 ) . All programmed cell deaths in the developing heads of wild-type animals occur embryonically and are engulfed and degraded prior to hatching ( ref . [30] , [48]; Table 4 ) . To detect ced-3-independent programmed cell deaths in larval heads , we used mutations ( e . g . , ced-1 ( e1735 ) , ced-6 ( n2095 ) or ced-7 ( n1996 ) ) that cause defects in cell-corpse engulfment and result in the persistence of many embryonic cell corpses into larval stages ( ref . [49] , [50]; Table 4 ) . Like most wild-type cell corpses , the ced-3-independent cell corpses were refractile in appearance as observed with Nomarski optics and were not extruded from the animal ( data not shown ) . We also observed that larvae mutant for ced-4 or egl-1 contained similar cell corpses , demonstrating that their generation does not require the canonical pro-apoptotic pathway that mediates most programmed cell deaths ( Table 4 ) . We tested whether the small number of cell corpses visible in ced-3 larval heads are generated by the other C . elegans caspase genes and found that all double , triple and quadruple caspase mutants that we examined contained a small number of refractile corpses ( Table 4 ) . For example , 39% of csp-3; csp-1; ced-6; csp-2 ced-3 mutant animals contained at least one refractile cell corpse ( Table 4 ) , indicating that these programmed cell deaths occur in animals lacking all C . elegans caspases . We observed caspase-independent cell corpses in different regions of the larval head , including positions internal and external to the pharynx , which suggests that multiple cell lineages – at low frequencies – generated caspase-independent cell corpses . Surprisingly , we discovered that engulfment-competent ced-3 and csp-3; csp-1; csp-2 ced-3 mutants also contained refractile cell corpses ( Table 4 ) . The number of cell corpses per ced-3 or csp-1; csp-2 ced-3 larva increased until 12 to 24 hours post hatching ( see below; data not shown ) , indicating that at least some of the cell deaths occurred after embryogenesis . Given that all programmed cell deaths in the head normally occur embryonically and that cell corpses are never observed in the heads of wild-type larvae , we concluded that timing of cell deaths in these ced-3 mutants was delayed . Thus , caspase-independent cell corpses can undergo an inefficient programmed cell death with slow kinetics in the absence of CED-3 activity , indicating that these cells likely die via CED-3-mediated apoptosis in wild-type animals . Despite the strong causal link between caspase activation and apoptosis , recent studies have demonstrated that many morphological and biochemical changes associated with apoptosis can occur in the absence of caspases [4] , [19] , [21] . For example , in C . elegans the shed cells of csp-3; csp-1; csp-2 ced-3 quadruple mutants exhibit phosphatidylserine exposure , expression of the pro-apoptotic BH3-only gene egl-1 , and chromatin condensation [19] . To determine whether these apoptotic attributes are evident in caspase-independent programmed cell deaths that do not involve extrusion of the dying cell from the embryo , we characterized the cell corpses visible in caspase-deleted larvae ( Figure 5 and Figure 6 ) . In most of these experiments , we used strains with the wild-type csp-3 allele , because ( 1 ) csp-3 lacks a caspase active-site [15]; ( 2 ) although previous studies reported that csp-3 has an anti-apoptotic function in somatic cells [22] , we were unable to replicate those findings ( Table S1 ) ; and , ( 3 ) the presence or absence of a csp-3 mutation had no effect on the frequency or appearance of caspase-independent corpses ( Table 4; Figure 6B; data not shown ) . Like ced-3-mediated programmed cell deaths in wild-type animals , the caspase-independent corpses expressed egl-1 , the upstream activator of the canonical apoptosis pathway ( Figure 5A ) . Also , these cell corpses displayed phosphatidylserine on their cell surfaces , as indicated by the phosphatidylserine-binding reporter MFG-e8::Venus ( Figure 5B ) , and exhibited many of the morphological hallmarks of apoptosis , including contraction of cytoplasmic volume and , in some but not all cases , condensation of nuclear chromatin ( Figure 5C ) . Additionally , we noted that the caspase-independent cell corpses frequently stained with acridine orange ( Figure 6A ) , suggesting that these corpses are engulfed , internalized and degraded via endosomal pathways , as are canonical programmed cell deaths [30] , [48] , [49] , [51] . Indeed , we found that the caspase-independent corpses were recognized by CED-1 ( Figure 6B ) , a receptor expressed on engulfing cells required for the efficient phagocytosis of cell corpses [46] , [49] , [50] . The recognition of caspase-independent cell corpses by CED-1 appeared to be functionally important , as ced-1; csp-1; csp-2 ced-3 larvae contained more corpses than csp-1; csp-2 ced-3 larvae ( Figure 6C ) . Given that ced-1 and other genes that function in cell-corpse engulfment promote programmed cell death [52] , [53] , it is unlikely that the ced-1 ( e1735 ) loss-of-function mutation caused additional cell deaths in the caspase-deleted mutants . Instead , the extra cell corpses in ced-1 mutant larvae likely reflected an engulfment defect , consistent with the comparatively rapid degradation and disappearance of most caspase-independent corpses in ced-1 ( + ) larvae within the 36-hour period after hatching ( Figure 6C ) . We conclude that caspases are not required for programmed cell deaths to be recognized by the engulfment machinery , internalized and degraded . In short , many aspects of apoptosis , including phagocytosis – the ultimate fate of apoptotic cells – can occur without caspases . We conclude that a parallel , caspase-independent pathway contributes to programmed cell death in C . elegans and can execute most cellular changes associated with apoptosis .
The caspases CED-3 and CSP-1B appear to be regulated differently . The auto-activation of CED-3 is facilitated by the Apaf-1 homolog CED-4 in a protein-protein interaction that requires the CED-3 prodomain [34]–[36] . In the absence of a pro-apoptotic signal , CED-9 sequesters CED-4 [37] , thereby preventing its association with the inactive CED-3 proprotein . The CSP-1B proprotein lacks a long prodomain , suggesting that it is not activated through an association with the CED-4 octamer in cells undergoing apoptosis . Consistent with this expectation , we observed that the cell-killing activity of csp-1B transgenes , unlike that of ced-3 transgenes , was not negatively regulated by ced-9 ( Figure 3 ) . Furthermore , based on our genetic experiments ( Figure 3 ) and the in vitro studies of Shaham [15] , it does not appear that CSP-1B is activated by CED-3 . We therefore propose that CSP-1B is regulated by a mechanism different from the canonical programmed cell death pathway that activates CED-3 and that CSP-1B likely promotes cell killing in parallel to CED-3 ( Figure 3E ) . There are no known or candidate regulators of csp-1 . It is possible that csp-1 is controlled entirely at the transcriptional level and that csp-1 contributes a minor , sub-lethal pro-apoptotic activity to all cells within the C . elegans embryo . Indeed , only using sensitized backgrounds with partial defects in programmed cell death did we detect the pro-apoptotic function of csp-1 . Nevertheless , we expect that it will be possible to identify regulators and effectors of csp-1 through genetic screens for mutants that modify the cell-killing activity of csp-1B transgenes . Given the minor contribution of csp-1 to programmed cell death and the lack of a detectable role of csp-2 or csp-3 in apoptosis ( Table 1; Table S1; data not shown ) , it is tempting to speculate that the csp genes have non-apoptotic functions in C . elegans . In C . elegans , ced-3 functions in axon regeneration following laser axotomy [54] . In mammalian and Drosophila neurons , caspases have functions in dendritic pruning , axon guidance and the synaptic changes underlying long-term depression [14] . Caspase function is also required for the maturation of Drosophila sperm [55] . Interestingly , we observed robust expression of csp-1 in the germlines of L4 and adult hermaphrodites , specifically in the late pachytene nuclei ( Figure 4 ) . We also observed temporally and spatially restricted csp-2 and csp-3 mRNA expression in the late pachytene nuclei of the L4 larval germline ( data not shown ) , suggesting that the csp genes might have functions in germ cell development . However , mutant hermaphrodites and males carrying all tested combinations of csp-1 , csp-2 and csp-3 , including the triple csp mutant were viable , fertile and failed to exhibit obvious brood-size defects that would suggest abnormalities in sperm or oocyte differentiation ( data not shown ) . Genetically encoded cell-killing activities provide an efficient and convenient method for determining cellular function through cell ablation . Killer genes such as ced-3 have been used under the control of various promoters to ablate specific cells [32] , [45] , [56] , [57] . However , the potent cell-killing activity of ced-3 transgenes can cause organismic inviability , particularly if the promoter expression is not exclusive to a small number of cells ( see below ) . csp-1B overexpression using the mec-7 and flp-15 promoters efficiently killed the touch and I2 neurons , respectively ( Figure 2; Table 2; N . Bhatla and H . R . Horvitz , personal communication ) . The mec-7 and flp-15 promoters are relatively strong , as they also robustly induced gfp expression in these cells , such that the neural processes were visible with a dissecting microscope equipped with fluorescence optics . By contrast , the odr-1 promoter did not produce detectable GFP expression in the neurites of the AWB , AWC and I1 neurons , and csp-1B under the control of the odr-1 promoter failed to kill these cells even when injected at plasmid concentrations as high as 100 ng/µl ( N . Bhatla and H . R . Horvitz , unpublished results ) . Thus , high levels of csp-1B expression might be required to kill most cells , making the use of csp-1B as a cell-ablation tool appropriate in situations in which the promoter sequence strongly drives expression in targeted cells and/or weakly promotes expression in additional cells not intended to be targets . For example , the Pmec-7::csp-1B constructs , which were injected at a concentration of 15 ng/µl , produced csp-1B expression outside of the touch neurons that was detectable by fluorescence in situ hybridization . However , this level of csp-1B expression was sub-lethal and did not induce cell death or other cellular defects outside of the touch neurons ( data not shown ) . By contrast , Pmec-7::ced-3 constructs were toxic to the animals when injected at concentrations above 1 ng/µl , suggesting that cells are very sensitive to ectopic ced-3 and that using ced-3 as a cell ablation tool is potentially problematic when promoter expression is not restricted to a small number of targeted cells . Although the csp-1 gene contributes a cell-killing activity to normal programmed cell deaths ( Table 1 ) , csp-1 and the other csp genes are not responsible for the ced-3-independent programmed cell deaths present in the heads of ced-3 larvae ( Table 4 ) . These deaths , like those of the male linker cell ( ref . [18]; Table S5 ) and the embryonic shed cells [19] , are caspase-independent – a surprising result in light of our observations that these cell corpses are morphologically apoptotic ( Figure 5 ) and are engulfed ( albeit with slower kinetics ) like normal programmed cell deaths ( Figure 6 ) . Thus , the complete apoptotic program including cell-corpse internalization can occur in the absence of caspases in C . elegans , suggesting that the cellular changes accompanying apoptosis do not require proteolysis by the caspase family of proteases . Moreover , it is clear that apoptotic programmed cell deaths are achieved through the integration of independent cell-killing activities from CED-3 , CSP-1B and an unknown caspase-independent source . Given the minor cell-killing effects of the CSP-1B and the caspase-independent pathways , why might cell-killing activities in addition to that of CED-3 have evolved ? It is possible that different cells , even within the set of C . elegans cells fated to die , are differentially sensitive to pro-apoptotic signals and that additional caspase and caspase-independent pathways ensure efficient and complete cell death under diverse environmental and developmental conditions . Interestingly , the postembryonic programmed cell deaths of the ventral cord are more sensitive to weak ced-3 mutations than are the embryonic programmed cell deaths in the presumptive anterior pharynx: ced-3 mutations that have weak effects in the anterior pharnyx typically have stronger effects in the ventral cord ( ref . [17]; data not shown ) . We observed a complementary function for csp-1 , which promotes apoptosis in the anterior pharynx ( Table 1 ) but not in the ventral cord ( Table S4 ) . In summary , multiple pro-apoptotic caspases function in programmed cell death in C . elegans , Drosophila and vertebrates . Furthermore , as we and others have shown , there are additional caspase-independent contributions to programmed cell deaths in C . elegans . We identified C . elegans caspase-independent cell deaths that are essentially identical to wild-type programmed cell deaths based on their apoptotic appearance and their recognition and internalization by engulfing cells . We expect that caspase-independent pro-apoptotic activities are present in other metazoans and that their identification will be of major importance to our understanding of cell death in the contexts of development and disease .
All C . elegans strains were cultured as described previously [58] and maintained at 20°C . We used Bristol N2 as the wild-type strain , and the mutations used in our experiments are listed below: LG I . unc-75 ( e950 ) , ced-1 ( e1735 ) , csp-3 ( n4872 , tm2260 , tm2286 ) , nIs177[Pceh-28::gfp] [59] LG II . csp-1 ( n4967 , n5133 , tm917 ) , mab-10 ( n5117 ) , lin-29 ( n836 ) LG III . ced-4 ( n1162 , n3158 ) , ced-6 ( n2095 ) , ced-7 ( n1996 ) , ced-9 ( n1653 , n2812 ) , tat-1 ( tm1034 ) , nIs308[Pmec-7::csp-1B , Pmec-3::gfp] , nIs400[Pced-1::ced-1ΔC::gfp] [19] LG IV . csp-2 ( n4871 ) , ced-5 ( n1812 ) , dpy-20 ( e1282 ) , unc-30 ( e191 ) , ced-3 ( n2427 , n2436 , n2452 , n3692 ) , nIs309[Pmec-7::csp-1B , Pmec-3::gfp] LG V . egl-1 ( n1084 n3082 ) , bcIs39[Plim-7::ced-1::gfp] [45] , nIs342[Pegl-1::4×NLS::gfp] [59] , qIs56[Plag-2::gfp] LG X . ced-8 ( n1891 ) , bzIs8[Pmec-4::gfp] [22] , nIs106[Plin-11::gfp] [52] Unknown linkage . nIs290[Pmec-3::gfp]; nIs307[Pmec-7::csp-1B , Pmec-3::gfp] , nIs368-370[Pmec-7::csp-1B ( C138S ) , Pmec-3::gfp] , nIs398[Pdyn-1::mfg-e8::Venus] [19] , [60] Extrachromosomal arrays . nEx1646[Pdyn-1::mfg-e8::Venus] [19] , [60] , nEx1465-71[csp-1 ( + ) ( pDD027 ) ] , nEx1604-9[csp-1B/C only ( pDD030 ) ] , nEx1614-16[csp-1A only ( pDD029 ) ] , nEx1617-19[csp-1-PD ( pDD028 ) ] The Pmec-7::ced-3 , Pmec-7::ced-4 [32] , Pdyn-1::mfg-e8::Venus [60] , Plim-7::ced-1::gfp [45] , Pced-1::ced-1ΔC::gfp [46] , Plin-11::gfp [52] , Pegl-1::gfp and Pceh-28::gfp [59] plasmids were described previously . The csp-1 rescuing plasmid ( pDD027 ) was constructed using PCR to amplify a 9 kb fragment of the csp-1 genomic locus with the primers 5′-gtaacgccagggttttcccagtcacgacggtgatccttcggagcttcag and 5′- acgaggatatccgcattgag . The resulting amplicon was ligated via the TOPO-TA subcloning protocol into the pCR2 . 1 vector ( Invitrogen ) . pDD028 ( csp-1-PD ) , pDD029 ( csp-1A only ) , and pDD030 ( csp-1B/C only ) were constructed using site-directed PCR mutagenesis . Two early stop codons in the csp-1B/C isoforms were generated in pDD028 using the primer 5′-ccgagaacggacgcctagtaatcgaaccataaac and its reverse complement . The csp-1B/C start codon was mutated to an alanine codon in pDD029 using the primer 5′-gactctcagagtcgagcgccgagaacggacgcc and its reverse-complement . Two early stop codons in the csp-1A isoform were generated in pDD030 using the primer 5′cctgaaaacgatagaagataattgataatcacaattcgacgatgatttgg and its reverse complement . The Pmec-7::csp-1A plasmid ( pDD003 ) was constructed using PCR to amplify the csp-1A cDNA from pDD006 using the primers 5′-gcggctagcatggtcctgaaaacgatagaag and 5′-gcgccatggttacatcgaccttgaaaagtgcc , which incorporate the restriction sites NheI and NcoI , respectively , into the resulting amplicon . The csp-1A amplicon was digested with NheI and NcoI and then ligated into the vector pPD52 . 102 . The Pmec-7::csp-1B plasmid ( pDD002 ) was constructed by using PCR to amplify the csp-1B cDNA from pDD001 using the primers 5′-gcggctagcatgccgagaacggacgccaag and 5′-gcgccatggttacatcgaccttgaaaagtgcc , which incorporate the restriction sites NheI and NcoI , respectively . The csp-1B amplicon was digested with NheI and NcoI and then ligated into the vector pPD52 . 102 , which encodes the mec-7 promoter . The Pmec-7::csp-1B ( C138S ) plasmid ( pDD005 ) was constructed from pDD002 using PCR with the primers 5′-tggatgaactatacaaatagctgcgctccagcgcgttcgt and its reverse complement . The RNAi plasmid pL4440::csp-1-PD ( pDD060 ) was constructed using PCR to amplify the prodomain encoding fragment of the csp-1A cDNA with the primers 5′-gcgagatctatggtcctgaaaacgatagaag and 5′-cgcctcgagatggcgggtttcagctgggtc , which incorporate the restriction sites BglII and XhoI , respectively . The resulting csp-1-PD amplicon was digested with BglII and XhoI and then ligated into pL4440 . The RNAi plasmid pL4440::csp-1B ( pDD061 ) was constructed using PCR to amplify the csp-1B cDNA with the primers 5′-gcgagatctatgccgagaacggacgccaag and 5′-cgcctcgagttacatcgaccttgaaaagtgcc , which incorporate the restriction sites BglII and XhoI , respectively . The resulting csp-1B amplicon was digested with BglII and XhoI and then ligated into pL4440 . The in vitro transcription , purification , preparation and microinjection of csp-1-PD ( pDD060 ) and csp-1B ( pDD061 ) dsRNA were performed as described previously [61] . The fixation of embryos and larval and adult animals , the conjugation of Cy5 or ALEXA594 fluorescent probes to in situ oligo probes , and the hybridization of oligos to fixed samples were performed as described previously [62] . All images were acquired using an inverted Nikon TE-2000 compound microscope equipped for fluorescence microscopy ( Prior Scientific ) . Images were acquired with a PIXIS camera ( Princeton Instruments ) controlled by MetaMorph software ( Molecular Devices ) and modified for publication with ImageJ software ( NIH ) . The “total csp-1” set of probes included 32 distinct 20-nucleotide sequences complementary to csp-1B ( Biosearch Technologies , Inc ) . This set of oligos was conjugated to the fluorophore Cy5 ( GE Healthcare ) and hybridized to all three csp-1 mRNA isoforms ( csp-1A , csp-1B and csp-1C ) . The “csp-1A” set of probes included 32 distinct 20-nucleotide sequences complementary to the region of csp-1A that encodes the prodomain . This set of oligos was conjugated to the fluorophore ALEXA594 ( Invitrogen ) and hybridized specifically to the csp-1A mRNA isoform . Probe sequences are listed in Table S6 . The numbers of undead cells that failed to undergo programmed cell death in the anterior pharynges and postdeirid sensilla of L3 larvae were determined by direct observation using Nomarski optics as described previously [28] . Persistent cell corpses in larval heads also were quantified by direct observation using Nomarski optics; for this assay , larvae were staged by the time of hatching . For other cell-death assays , the ventral cord cells of young adults , the M4 neuron and its undead sister cell of L3 larvae , the touch neurons of L4 larvae , and the germ cell corpses of adult hermaphrodite gonads were identified using previously described GFP reporter transgenes [45] , [52] , [59] . For experiments involving ionizing radiation , L4 larvae were exposed to gamma irradiation from a Co-60 source . All strains were analyzed using a Zeiss Axioskop II compound microscope equipped for fluorescence microscopy . Images were acquired with an ORCA camera ( Hammamatsu ) controlled by OpenLab software ( Perkin Elmer ) and modified for publication using ImageJ ( NIH ) . L1-stage larvae were fixed , stained and sectioned for transmission electron microscopy as described previously [43] . Stained sections were imaged with a JEM-1200EX II microscope ( JEOL ) using an AMT XR41 CCD camera . | Caspases are cysteine proteases that in many cases drive apoptosis , an evolutionarily conserved and highly stereotyped form of cellular suicide with functions in animal development and tissue maintenance . The dysregulation of apoptosis can contribute to diseases as diverse as cancer , autoimmunity , and neurodegeneration . Caspases are often thought to be required for , or even to define , apoptosis . Although there is evidence that apoptosis can occur in the absence of caspase activity , caspase-independence can be difficult to prove , as most animals have multiple caspases . The nematode Caenorhabditis elegans has four caspases , CED-3 , CSP-1 , CSP-2 , and CSP-3 . CED-3 has a well-established role in apoptosis , but less is known about the functions of the CSP caspases . In this study , we show that CSP-1 promotes apoptosis in the developing C . elegans embryo and that CSP-1 is regulated differently than its homolog CED-3 . Furthermore , we show that apoptosis and the engulfment of dying cells can occur in mutants lacking all four caspases , proving that neither apoptosis nor cell-corpse engulfment require caspase function and that caspase-independent activities can contribute to apoptosis of some cells during animal development . | [
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] | [
"developmental",
"biology",
"animal",
"genetics",
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"genetics",
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] | 2013 | Both the Caspase CSP-1 and a Caspase-Independent Pathway Promote Programmed Cell Death in Parallel to the Canonical Pathway for Apoptosis in Caenorhabditis elegans |
Scabies is endemic in many Aboriginal and Torres Strait Islander communities , with 69% of infants infected in the first year of life . We report the outcomes against scabies of two oral ivermectin mass drug administrations ( MDAs ) delivered 12 months apart in a remote Australian Aboriginal community . Utilizing a before and after study design , we measured scabies prevalence through population census with sequential MDAs at baseline and month 12 . Surveys at months 6 and 18 determined disease acquisition and treatment failures . Scabies infestations were diagnosed clinically with additional laboratory investigations for crusted scabies . Non-pregnant participants weighing ≥15 kg were administered a single 200 μg/kg ivermectin dose , repeated after 2–3 weeks if scabies was diagnosed , others followed a standard alternative algorithm . We saw >1000 participants at each population census . Scabies prevalence fell from 4% at baseline to 1% at month 6 . Prevalence rose to 9% at month 12 amongst the baseline cohort in association with an identified exposure to a presumptive crusted scabies case with a higher prevalence of 14% amongst new entries to the cohort . At month 18 , scabies prevalence fell to 2% . Scabies acquisitions six months after each MDA were 1% and 2% whilst treatment failures were 6% and 5% respectively . Scabies prevalence reduced in the six months after each MDA with a low risk of acquisition ( 1–2% ) . However , in a setting where living conditions are conducive to high scabies transmissibility , exposure to presumptive crusted scabies and population mobility , a sustained reduction in prevalence was not achieved . Australian New Zealand Clinical Trial Register ( ACTRN—12609000654257 ) .
Scabies mites infect up to 300 million people worldwide , most of whom are children living in poverty and overcrowded conditions . [1–3] In remote Australian Aboriginal communities , scabies has been near universal during the first year of life ( 69% ) . [4] Secondary infections with highly pathogenic bacterial pathogens Streptococcus pyogenes and Staphylococcus aureus contribute to high rates of pyoderma in these communities . [5–8] Acute post-streptococcal glomerulonephritis ( APSGN ) and streptococcal and staphylococcal sepsis , [9] , [10] are recognised complications of pyoderma , whereas rheumatic fever , rheumatic heart disease and chronic renal failure are postulated sequelae that all occur in Australian Aboriginal people at the highest rates in the world . [11 , 12] In contrast , scabies is infrequently seen in non-Indigenous Australians . [2 , 8 , 13] Individuals with scabies classically present with profuse pruritus involving only 5–15 mites per person , whereas an individual with crusted scabies , a rare condition , can have thousands of mites . [14 , 15] Well documented to occur in immune compromised hosts , most Aboriginal people identified with crusted scabies have no definable immune defect . [16] People with crusted scabies are highly infectious and have been identified as core transmitters in scabies epidemic cycles and institutional outbreaks . [3 , 16 , 17] Prior to 1996 and the introduction of ivermectin in Northern Territory ( NT ) Australia , there was a 5-year mortality rate of up to 50% for people with crusted scabies . [16] Mass drug administration ( MDA ) programs using topical acaricides to decrease scabies prevalence have had varying degrees of success in Australia . [5 , 8 , 13] Due to high endemicity , high transmissibility of infestations , low treatment uptake and limited regional coverage , the presence of crusted scabies in communities and mobility of regional populations , a sustained reduction in prevalence has not been achieved to date in remote Aboriginal communities . [1 , 8 , 18] Having an established collaboration through the East Arnhem Healthy Skin Program [1 , 19 , 20] which demonstrated poor uptake of topical acaricides in household contacts , [1] we were invited by one community in eastern Arnhem Land to develop a proposal for an oral-ivermectin MDA targeting both scabies and strongyloidiasis . Strongyloidiasis is an infection with the intestinal nematode parasite , Strongyloides stercoralis , for which ivermectin is the first-line treatment . [21] Here we report the outcomes against scabies of the MDA program designed in collaboration with the participating community .
The project was registered with the Australian New Zealand Clinical Trial Register ( ACTRN—12609000654257 ) [23] and received ethical approval from Human Research Ethics Committee of the Northern Territory Department of Health and Menzies School of Health Research ( EC00153—project 09/34 ) . Study recruitment was conducted by Aboriginal Community Workers ( ACWs ) who had completed a nationally accredited training program ( Certificate II in Child Health Research 70131NT ) . The ACWs visited each house to discuss the project with family members and establish a household occupancy list . Ascertainment of written informed consent was obtained using a pictorial flipchart that incorporated a culturally-appropriate process to explain the project . [28] Parents or registered caregivers provided written consent for children aged <18 years and additional written assent was obtained from children aged 12-<18 years .
Scabies prevalence among the baseline cohort was 4% and remained relatively stable during the initial assessment period ( 2% , 6% , 3% and 5% per month from April-July 2010 respectively when 91% of the baseline cohort were seen ) . At the month 6 survey , prevalence was 1% but increased to 9% at month 12 ( 5% absolute increase from baseline to month 12 for the baseline cohort ) ( Fig 1 ) . At month 18 , prevalence fell to 2% . The median age of participants with scabies was 11 years ( IQR 6–38 years ) with more females at baseline diagnosed with scabies than males ( Table 2 ) . Of the 42 participants diagnosed with scabies , 8/35 ( 23% ) had infected scabies . Prevalence among the baseline cohort had increased from 4% to 9% at month 12 , whereas prevalence among new entries to the cohort ( those seen for the first time at month 12 ) was 14% ( Fig 1 ) . In addition to the new cohort entries , the increased prevalence at month 12 was influenced by a cluster of cases epidemiologically linked to a participant diagnosed with presumptive crusted scabies . Prevalence within the baseline cohort of those who were known contacts rose from 7% ( 7/96 ) at baseline to 18% ( 17/96 ) at month 12 , whereas prevalence amongst others who were not known contacts within the baseline cohort rose from 4% ( 23/598 ) at baseline to 8% ( 46/604 ) at month 12 . Of the 113 participants diagnosed with scabies , 34/105 ( 32% ) had infected scabies . The presumptive crusted scabies case was identified in May 2011 , a school age participant who had been receiving topical acaricide treatment from the school nurse every two weeks for the previous two months . With support from nine public health personnel who joined the study team , we identified 13 priority houses for follow-up , three of which were houses where the presumptive crusted scabies participant had been living over the previous four weeks , and 10 other households that had school contacts with scabies . There were 184 people identified as residing in these 13 houses of whom 141 ( 77% ) were seen; a median of 13 ( IQR 10–18 ) participants per house ( Fig 2 ) . Of the 141 participants seen , 91 ( 65% ) were from the baseline cohort of whom 16 ( 18% ) had scabies at month 12 . Of the 50 new participants seen for the first time in the priority houses , eight ( 16% ) had scabies . Scabies prevalence within these 13 households collectively was 17% ( n = 24 ) . Almost all ( 98% ) of those seen received ivermectin or 5% permethrin at the first visit . On follow-up , 77/184 residents ( 42% ) from the 13 priority houses were seen again at visit 2 , median 37 days ( IQR 23–42 ) after visit 1 , with an acquisition rate of 4% . Of the 24 participants observed with scabies lesions at visit 1 , 18 ( 75% ) were re-treated at visit 2 ( 12 had lesions present when reviewed ) . Follow-up of these priority houses was completed within two months . The increase in scabies prevalence at month 12 was most evident among children <15 years of age and was highest amongst new entries to the cohort ( Fig 3 ) . Pyoderma prevalence where the sores were described as purulent or crusted also increased amongst these age groups at month 12 ( Fig 4 ) . Scabies treatment failures and acquisition were low throughout the study period ( S1 and S2 Tables ) . The treatment failure rate was 6% ( 2/35 ) at month 6 and 5% ( 5/91 ) at month 18 . The acquisition rate was 1% ( 4/352 ) at month 6 and 2% ( 6/276 ) at month 18 . The median time between participant visits from baseline to month 6 was six months ( IQR 5–7 months ) and from month 12 to 18 , eight months ( IQR 7–10 ) .
In our study , MDA incorporating ivermectin had a demonstrable but relatively short-term impact on scabies prevalence . In the six-months following each MDA , both the low overall prevalence ( 1–3% ) and the low acquisition rates ( 1–2% ) suggest that transmission was substantially reduced . However , the rapid rise in prevalence at month 12 highlights that an MDA program , where utilised , needs to be incorporated with a multi-faceted control program and ongoing surveillance in the community . MDAs have been used to control and eliminate diseases for more than 25 years . [29] Ivermectin is one of the most commonly used drugs worldwide in the treatment of strongyloidiasis , lymphatic filariasis , and onchocerciasis . [30] It is increasingly being used to treat other parasitic infections including scabies , [31] pediculosis capitis [32] and malaria . [33] In 2014 , Merck Sharp and Dhome updated the indications for ivermectin use to include treatment of crusted scabies and classical scabies if topical treatment is ineffective . [24 , 34] . Scabies is a neglected tropical disease , [35] ubiquitous in Australian Aboriginal and Torres Strait Islander communities , despite repeated MDAs with topical acaricides . [8] , [3] Infestations are highly transmissible , [3] and as this study shows , prevalence escalates in the presence of high exposure ( prevalence amongst known contacts of the presumptive crusted scabies case rose from 7% at baseline to 18% at month 12 ) and a high proportion of mobility ( 36% new entries to the cohort at month 12 , of whom 14% had scabies ) . Others have shown the impact of exposure to crusted scabies [36] and overcrowded living conditions [1] on scabies prevalence . Outbreaks [37] and high scabies prevalence [38] have previously been linked with epidemics of APSGN , the sequelae of a post streptococcal infection that is common in developing countries and Indigenous populations . [10] The participation rate among residents within the community was noteworthy ( 80–95% ) encompassing an informed consent process implemented with and by the community . Under the guidance of elders and key community stakeholders , the development of a pictorial flipchart that incorporated a culturally-appropriate process to explain the project was fundamental in obtaining informed consent . [28] The flipchart incorporated a local story well known in the community which we had gained specific approval to use and translate into local language . That some members declined participation is testament to the culturally appropriate processes enabled within this study . Moreover , the process of ongoing engagement and the culturally acceptable arrangements regarding pregnancy testing , screening and steady ( as opposed to rapid ) roll-out of the program were integral to the reach achieved over the course of the study . A previous attempt to implement an ivermectin MDA for scabies control in Queensland Aboriginal communities in the early 1990s did not proceed due to administrative concerns about medication safety and informed consent . [39] Instead the team conducted a MDA with ivermectin in the Solomon Islands and showed ivermectin to be safe and effective with low scabies prevalence persisting for at least 32 months . [31] The longer-term duration of benefit however , is unclear as there was no ongoing active surveillance . In Fiji , no significant difference was found between MDAs with either ivermectin or benzyl benzoate after 24–28 days . [40] To date , the use of ivermectin to treat scabies has not been associated with any serious adverse effects nor were any observed in our study . However , it is recommended that ivermectin not be administered to pregnant women or children who are younger than five years of age or in those who weigh less than 15 kg . This recommendation is due to theoretical concerns regarding potential neurotoxicity and a lack of safety data . Although there have been no reports of foetal problems when ivermectin has been administered in pregnancy to thousands of women , caution is still recommended . [41] While the safety of ivermectin at the extremes of age remains to be conclusively established , there is increasing evidence suggesting that the use of ivermectin in children <5 years is safe . [26] The high proportion of new entries to the cohort at the month 12 census ( 36% ) coincided with a large funeral that was attended by visitors from other communities who were camping in tents in the house yards of relatives . At this time , many local residents were also displaced from their homes into tents or other people’s homes as their houses were being refurbished or demolished and rebuilt , as part of a government initiative to address housing shortages in Aboriginal communities . [42] This change in population dynamics is considered highly mobile by Australian mainstream standards , but does not reflect the stability reflected by the customary attachment of Aboriginal people to their home community and the regional area . [43] The increased scabies prevalence at month 12 was notable in the 0–14 year age group and in particular for those new participants to the cohort . Young children are particularly susceptible to scabies infestations [2 , 4 , 44] and , as shown in this study , are more likely than adults to show a change in prevalence . For population surveillance of scabies it has previously been recommended that this is best achieved by monitoring the prevalence in young children , [4] a recommendation that is further supported by this study . At baseline there was concern about inter-observer variation in the diagnosis of scabies as more females ( n = 33 ) than males ( n = 9 ) had been diagnosed with scabies . These concerns were dispelled after reviewing the names of the researchers screening the children ( for whom the majority of scabies were diagnosed ) and found that the female researchers , who at that time had more experience in diagnosing scabies than the male researchers , had been conducting most of the skin checks for male and female children . Thereafter we conducted regular reviews of screening processes in the field and from photographs taken , to improve consistency in diagnosis and reduce inter observer variation . It was also apparent to our community-based research team , that the relationship built over the course of the team’s work meant that by month 12 it was relatively commonplace for households to seek out the research team to assist in making their homes scabies free , and to send family members who had not been present on the day the family were seen to the research office for screening and treatment . We acknowledge that this may have introduced a screening bias in the latter part of the study but the increased scabies prevalence at month 12 amongst those who had been seen at baseline indicates that the increase in prevalence was not an artefact of care-seeking behaviour . The rise in scabies prevalence at month 12 coincided with: a cluster of cases epidemiologically-linked to an individual with presumptive crusted scabies , a high prevalence amongst new entries to the cohort ( an indicator of the impact of high population mobility ) , and an increased prevalence amongst members of the baseline cohort who did not have a known exposure to the suspected crusted scabies case ( 4% to 8% ) . This demonstrated how readily scabies prevalence can increase . Control measures were able to be implemented promptly as the research team had commenced the second house to house population census and MDA and were able to coordinate the response with the local PHC services and community . Of note , was an outbreak of APSGN [45] occurring at the same time in another large NT community that public health personnel were responding to . Scabies prevalence in this community for children aged 1–17 years was 3% ( n = 8 ) and 40 . 5% ( n = 219 ) for purulent or crusted sores . Personal communication from the public health unit revealed there had been three cases of ARF and no cases of APSGN reported in the region in the four months following the outbreak . Our study provides evidence that ivermectin based MDAs can have a role in reducing scabies prevalence but also highlights that maintaining a reduction requires ongoing surveillance , [4] diagnosis and chronic case management of individuals with crusted scabies , [18 , 34] and ongoing engagement with community members that has a particular focus on households and close contacts . [1] Due to the customary movements of Aboriginal people , regional approaches to decrease re-introduction of scabies from neighbouring communities needs to be considered . | Scabies is endemic in many Australian Aboriginal and Torres Strait Islander communities , with 69% of infants infected in the first year of life . Previous mass drug administration ( MDA ) programs using topical acaricides to decrease scabies prevalence have had varying degrees of success in Australia . We were invited by one community in eastern Arnhem Land to develop and deliver an oral-ivermectin MDA . Utilizing a before and after study design , we measured scabies prevalence through population census with sequential MDAs at baseline and month 12 . Scabies prevalence fell from 4% at baseline to 1% at month 6 , rising to 9% at month 12 in association with an identified exposure to a presumptive crusted scabies case . For new entries to the cohort at month 12 scabies prevalence was higher at 14% . We were able to demonstrate a reduction in scabies prevalence in the six months after each MDA with a low risk of acquisition ( 1–2% ) ; however , a sustained reduction was not achieved . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Impact of an Ivermectin Mass Drug Administration on Scabies Prevalence in a Remote Australian Aboriginal Community |
Studies of mice with Y chromosome long arm deficiencies suggest that the male-specific region ( MSYq ) encodes information required for sperm differentiation and postmeiotic sex chromatin repression ( PSCR ) . Several genes have been identified on MSYq , but because they are present in more than 40 copies each , their functions cannot be investigated using traditional gene targeting . Here , we generate transgenic mice producing small interfering RNAs that specifically target the transcripts of the MSYq-encoded multicopy gene Sly ( Sycp3-like Y-linked ) . Microarray analyses performed on these Sly-deficient males and on MSYq-deficient males show a remarkable up-regulation of sex chromosome genes in spermatids . SLY protein colocalizes with the X and Y chromatin in spermatids of normal males , and Sly deficiency leads to defective repressive marks on the sex chromatin , such as reduced levels of the heterochromatin protein CBX1 and of histone H3 methylated at lysine 9 . Sly-deficient mice , just like MSYq-deficient mice , have severe impairment of sperm differentiation and are near sterile . We propose that their spermiogenesis phenotype is a consequence of the change in spermatid gene expression following Sly deficiency . To our knowledge , this is the first successful targeted disruption of the function of a multicopy gene ( or of any Y gene ) . It shows that SLY has a predominant role in PSCR , either via direct interaction with the spermatid sex chromatin or via interaction with sex chromatin protein partners . Sly deficiency is the major underlying cause of the spectrum of anomalies identified 17 y ago in MSYq-deficient males . Our results also suggest that the expansion of sex-linked spermatid-expressed genes in mouse is a consequence of the enhancement of PSCR that accompanies Sly amplification .
During spermatogenesis , germ cells progress through three phases to become functional sperm: proliferation , meiosis , and spermiogenesis . In the latter phase , haploid germ cells ( spermatids ) undergo dramatic remodeling and DNA compaction as they differentiate into spermatozoa . The X and Y chromosomes are transcriptionally silenced during meiosis by a process termed meiotic sex chromosome inactivation ( MSCI ) , and postmeiotically , the spermatid X and Y chromosomes remain largely repressed [1] . Nevertheless , there is substantial X and Y gene expression in spermatids , and based on their analysis of X gene expression in spermatids , Mueller and colleagues have argued that gene amplification plays a key role in compensating for postmeiotic sex chromatin repression ( PSCR ) [2] . Although the chromatin modifications associated with MSCI and PSCR are not the same [1] , [3] , PSCR is thought to be a downstream consequence of MSCI [4] , [5] . In 2005 , we reported the surprising finding that deletions of the long arm of the mouse Y ( MSYq ) lead to the up-regulation of several spermatid-expressed X and Y chromosomal genes [6]; this suggests that one ( or more ) of the multicopy genes known to be located on MSYq is involved in PSCR . Aside from this , MSYq deficiencies cause sperm head malformations , with severity correlating with the extent of the deficiency and ultimately leading to infertility [7]–[11] . Intriguingly , males with an approximately two-thirds deletion of MSYq ( 2/3MSYq− ) are fertile but produce offspring with a sex ratio distortion in favor of females; this has been considered a manifestation of a postmeiotic intragenomic conflict between the sex chromosomes that led to the amplification of sex ratio distorter and suppressor genes [12]–[14] . Our favored candidate for the MSYq factor needed for normal sperm differentiation and a balanced sex ratio has been Sly , one of the four multicopy genes identified on the mouse Y long arm [6] , [15] , [16] . Sly encodes a protein that is very highly expressed in round spermatids , and among the proteins with which it interacts are the acrosomal protein DKKL1 and the chromatin modifier and transcriptional coactivator KAT5 ( aka TIP60 ) [17] . More than 70 copies of Sly that retain an open reading frame , and 30 copies annotated as “noncoding” are predicted to be present on MSYq ( Entrez Gene database from the National Center for Biotechnology Information [NCBI]; http://www . ncbi . nlm . nih . gov/sites/entrez ? db=gene ) as a result of the amplification of a >500-kb repeat unit encompassing at least two copies of Sly [16] ( J . Alfoldi and D . C . Page , personal communication ) . Sly expression is consequently reduced in proportion to the extent of MSYq deficiency [15] , [17] . Interestingly , the X chromosome carries multiple copies ( ∼25 ) of Slx ( Sycp3-like , X-linked ) [2] , a gene related to Sly that encodes a cytoplasmic spermatid-specific protein of unknown function [18] . Slx is one of the X-linked genes found to be up-regulated in MSYq-deficient males , and Slx and Sly have been suggested to be key players in the postmeiotic X-Y genomic conflict [6] . Because Sly is present in multiple copies , traditional gene targeting is not an option for investigating its function . In the present study , by using an in vivo RNA interference approach , we have produced male mice with a dramatic reduction in Sly expression . The analysis of these mice has enabled us to demonstrate that SLY is a key regulator of sex chromosome gene expression during sperm differentiation .
The major challenge in the study of MSYq gene functions is the highly repetitive nature of MSYq , which contains coamplified multicopy genes organized in clusters over several megabases [16] ( J . Alfoldi and D . C . Page , personal communication ) . This precludes the use of conventional gene targeting , so we used a transgenic approach to deliver short hairpin RNAs ( shRNA ) [19] designed to generate Sly-specific small interfering RNAs ( siRNAs ) ( Figure 1 ) . Sly short hairpin target sequences were selected to be shared by most Sly copies , and to be specific to Sly; particular care was taken to avoid sequences that might target related genes such as Slx . The selected Sly short hairpin sequences ( shSLY ) were cloned under the control of the U6 promoter [20] . We chose U6 , a ubiquitous polymerase III promoter , in order to achieve sufficient expression of shSLY RNA to produce a substantial knock-down of the very abundant Sly transcripts . As Sly expression is restricted to the testes , no other organs were expected to be affected by its knock-down , and indeed , we did not see any phenotypic changes other than testis-related changes ( see below ) . The efficiency and specificity of shSLY constructs were tested in cell culture by cotransfection experiments ( Figure S1 ) . Two shSLY constructs ( sh136 and sh367—see Figure 1 ) were used to produce transgenic mice . High expression of shSLY RNA was associated with a dramatic decrease in Sly expression at the transcript and at the protein levels ( Figure 2A–2C ) . Transgenic mice for sh136 and sh367 constructs ( hereafter , sh136 and sh367 mice ) showed an ∼70% reduction of Sly transcripts ( Figure 2B ) , with both known splice variants being affected ( unpublished data ) . SLY protein level was even more dramatically reduced , with no protein being detected by Western blotting ( Figure 2C and Figure S2 ) even after long exposure . This discrepancy suggests that the persisting Sly transcripts are not translated , or that they encode a variant SLY protein ( s ) not detected by our antibody . The sh367 transgene was also introduced into 2/3MSYq− males and Sly transcript levels fell further , to 10% of those of normal males ( Figure 2B ) . Four checks were made for “off-target” effects of the RNA interference . First , the levels of two testis-expressed microRNAs , mir-t3 and mir-t25 [21] , were measured and found to be unchanged in testes of shSLY mice ( Figure S3A ) , indicating that the transgenically delivered siRNAs did not affect the expression of naturally expressed small RNAs . Second , since some shRNAs or siRNAs induce an interferon response [22]–[24] , the expression level of 2′ , 5′-oligoadenylate synthetase 1 ( Oas1 ) was measured as a marker of an interferon response [22] , [24] , [25]; this was also unchanged in shSLY mice ( Figure S3B ) . Third , microarray analyses performed on sh367 mice did not detect any significant changes in the expression of known target genes of the interferon pathway ( see below ) . Finally , microarray analyses were performed on juvenile testes ( 17 d postpartum ) to check for potential off-target gene activation before the onset of Sly expression . There were no statistically significant differences in gene expression between juvenile sh367 mice and controls ( unpublished data ) . In view of the substantial and specific knock-down of Sly expression in the shSLY mice , we proceeded to analyze their phenotypes in order to assess the extent to which Sly depletion mimicked the phenotypic consequences of MSYq deficiencies . Mice with MSYq deficiencies have an increased incidence of sperm head abnormalities , correlated with the extent of the deletion [7]–[11] . In mice lacking 9/10ths of MSYq ( 9/10MSYq− ) or with no MSYq ( MSYq− ) , 100% of the sperm are abnormal , and this is thought to be the cause of their sterility [10] , [11] . Analysis of epididymal sperm from Sly-deficient mice ( i . e . , shSLY mice from either line ) revealed that over 92% of the sperm had head abnormalities , and defects were similar to those observed in mice with MSYq deficiencies ( Figure 3A and 3B ) . The proportion of abnormal sperm heads ( grouped into three categories: slightly flattened , grossly flattened , and other gross abnormalities ) was very significantly increased ( p<0 . 0001 ) in shSLY mice compared to controls ( transgene-negative siblings ) . In terms of severity , the sperm head abnormalities of shSLY mice from both sh136 and sh367 lines were very similar and fell between those of 2/3MSYq− and 9/10MSYq− mice ( Figure 3A ) . The presence of the sh367 transgene in the context of 2/3MSYq− led to a more severely abnormal sperm phenotype than that seen in 2/3MSYq− males or in shSLY males with a normal YRIII chromosome ( Figure 3A and 3B , Figure S4 ) . Another spermiogenic defect described for 9/10MSYq− males [11] and seen in MSYq− males ( unpublished data ) is a delay in sperm shedding . This is also the case for shSLY mice ( unpublished data ) . Together these results show that the key spermiogenic defects observed in MSYq− mice are also seen in shSLY mice , thus demonstrating that Sly deficiency is the underlying cause . Previous studies have shown that 9/10MSYq− and MSYq− males are sterile [10] , [11] , whereas males carrying less extensive deletions of MSYq ( such as 2/3MSYq− and B10 . BR-Ydel ) are fertile , but their sperm have markedly reduced in vitro fertilizing ability [12] , [26]–[28] . We therefore checked for impaired fertility in shSLY males , initially focusing our study on sh367 males since the phenotypes of both lines were similar . As is the case for 9/10MSYq− and 2/3 MSYq− males , testis weights for the shSLY males did not differ from controls; sperm numbers were reduced but within the fertile range ( Table S1 ) . However , when mated for a period of 7 mo , sh367 mice had markedly fewer offspring and litters when compared to transgene-negative siblings ( Table 1 ) . The in vitro fertilizing ability of epididymal sperm samples was also dramatically reduced relative to controls with only one of 662 eggs developing to the two-cell stage ( Table 1 ) , and the quality of sperm motility appeared to be impaired as shown by the increase in the proportion of non-progressively motile sperm ( Table S1 ) . A reduced quality of motility has recently been reported for B10 . BR-Ydel males [29] . Overall , the fertility defects were intermediate in severity between those of 2/3MSYq− and 9/10MSYq− mice , as was the case for the sperm head abnormalities . Surprisingly , two sh136 and sh367 transgenic males that were obtained early in the backcrosses were exceptionally fertile , whereas Sly-deficient males obtained in subsequent generations are all markedly subfertile or sterile ( see Table S2 ) . Because of the poor fertility of Sly-deficient males , offspring sex ratio data are very slow to accumulate . After pooling data for all matings involving sh136 and sh367 males and for nontransgenic males arising in the same breeding program , 55 . 5% ( 79 of 142 ) of the offspring of the transgenics are female and 47 . 8% ( 176 of 368 ) of the offspring from nontransgenics are female . The sex ratio distortion in favor of females ( 7 . 7% ) approaches significance at the 0 . 05 level ( p-value = 0 . 0569 ) . However , a major caveat is the fact that a large proportion of the data come from just two males ( Table S2 ) . It may be necessary to produce an shSLY line with an intermediate knock-down to establish whether or not Sly deficiency contributes to the sex ratio distortion observed in 2/3MSYq− mice . In our initial gene expression study of MSYq-deficient mice , 18 sex chromosome genes ( of which 16 were X- and two Y-linked ) were found up-regulated [6] . We decided to investigate the consequences of Sly deficiency on gene expression in our new mouse model , and to reexamine gene expression in MSYq-deficient males , using a more exhaustive array . Microarray analyses were performed on adult testes of sh367 mice , sibling controls , 2/3MSYq− , 9/10MSYq− , and MSYq− mice ( see Figure S5 ) . We found 230 differentially expressed genes that were grouped into five categories based on their expression ratios across all genotypes ( Figure 4A ) . The largest category ( 127 genes ) comprises genes that are up-regulated in sh367 mice and in mice with MSYq deficiencies ( category 2 ) ; 67 . 7% ( 86/127 ) of these up-regulated genes are X-linked , increasing to 81% ( 68/84 ) of those up-regulated at least 1 . 5-fold ( Figure S5 ) . Many of these X-linked genes are present in multiple copies , such as Slx , Cypt , Asb , Ssxb , and Rhox; of these , Slx , Cypt , Ssxb , Rhox3 , and Rhox11 are specifically expressed in postmeiotic cells ( Table 2 ) . Several of the up-regulated single-copy X-linked genes are also known to be involved in the differentiation of postmeiotic germ cells ( i . e . , spermatids ) ( Table 2 ) . Indeed , Actrt1 and Spaca5 are components , respectively , of the perinuclear theca and the acrosome , two highly differentiated structures of the sperm head [30] , [31] . X-linked genes encoding variants of histone H2A ( LOC100045423 , a copy of H2al1 , and LOC100046339 , which is closely related to H2A . Bbd ) were also derepressed in shSLY and MSYq-deficient mice ( cf . Table 2 ) . The array also shows up-regulation in sh367 mice of 27 Y-linked gene loci , almost exclusively representing the multicopy genes Ssty1 and Ssty2 ( category 4 ) . Ssty1 and Ssty2 are spermatid-specific MSYq-encoded genes [32] and are consequently reduced in mice with MSYq deletions ( Figure S5 ) . Overall , more than 65% of the genes up-regulated in shSLY mice , and >80% of the ones that are highly up-regulated ( >1 . 5-fold increase ) , are located on the sex chromosomes ( Figure 4A ) . No X or Y genes ( except one pseudogene provisionally mapped to the Y ) were found to be down-regulated . The microarray results for a number of genes were confirmed by real-time PCR ( Figure 4B ) . Spermatid-specific X- and Y-linked multicopy genes , such as Slx , Slx-like , H2al1 , Ssty1 , and Ssty2 are all markedly derepressed in shSLY mice , and this is also the case for the spermatid-specific single-copy genes Actrt1 and 1700008I05Rik ( an X-linked homolog of the t-complex gene Tcp11 ) . Of two other MSYq-encoded spermatid-expressed genes that were not on the array , Asty was not significantly up-regulated , and Orly was not as dramatically up-regulated as Ssty1 and Ssty2 . Zfy2 , another gene predominantly expressed in spermatids [33] , was found markedly up-regulated by real-time PCR , even though not picked up in our array analysis . Zfy2 is encoded by the Y chromosome short arm , and its up-regulation shows that the derepression of the Y is not restricted to its long arm . Levels of expression of autosomal genes , including the spermatid-specific Acrv1 and Protamine1 genes , are not significantly changed , in agreement with the sex-linked gene bias identified in the microarray ( Figure 4B ) . Similar results were obtained for sh136 transgenic mice ( unpublished data ) . A further microarray analysis was performed on purified round spermatids from sh367 transgenic mice , sibling controls , and 2/3MSYq− mice . The vast majority of the X and Y genes found up-regulated before were also found significantly up-regulated in the new comparison ( 109 of 113 , Figure S6 ) . In addition , this new dataset identified a greater number of up-regulated X-linked genes ( 126 vs . 68 genes , >1 . 5-fold increase; see Figure S6 ) . The identification of a greater number of genes in the new dataset is probably due to the increase in sensitivity when the analysis is restricted to the cell type in which up-regulation occurs . Additional Y-encoded up-regulated transcripts were also identified , such as Orly , Rbm31y , and H2al2y ( a Y-encoded histone H2A spermatid specific variant ) . This demonstrates that the derepression of sex chromosome genes occurs in spermatids and also provides a control for differences in the cellular composition between Sly-deficient , MSYq-deficient , and control testes . The up-regulation of sex-chromosome spermatid genes observed at the transcript level is also detected at the protein level , as shown for SLX and SSTY1 proteins ( Figure 5A and 5B and Figure S2 ) . SLX immunostaining of testis sections confirmed that the derepression is restricted to spermatids ( Figure S7 ) . All these data point to a global derepression of postmeiotic sex chromatin ( PMSC ) following Sly deficiency; the X and Y genes that are up-regulated are those already expressed in spermatids . Thus , it is clear that SLY has a key role in PMSC repression . Several studies have demonstrated that the PMSC of X and Y spermatids is enriched in histone modifications known to be associated with transcriptional repression , such as hypermethylation of lysine 9 of histone H3 ( H3K9 ) [3]–[5] , [34] , [35] . The heterochromatin proteins CBX1 and CBX3 ( aka HP1β and HP1γ ) also accumulate on PMSC [4] , [5] , [35] . As shown in other contexts , the heterochromatin proteins are recruited via binding to methylated H3K9 [36] , [37] and mediate gene repression [38] , [39] . In view of our microarray results implying global PMSC derepression in Sly-deficient spermatids , we decided to examine these repressive chromatin marks in our mouse model . The analysis of shSLY mice revealed a significant ( p<0 . 005 ) decrease of trimethylated H3K9 ( H3K9me3 ) staining on PMSC as compared with the chromocenter ( 85% of spermatids with less staining vs . 46 . 5% in control mice ) . Similarly , CBX1 accumulation on PMSC was significantly ( p<0 . 05 ) reduced in mutant mice relative to the chromocenter ( 82% of spermatids with less staining vs . 57% in control mice ) ( Figure 6 and Figure S8 ) . A recent study shows a comparable reduction of PMSC-associated H3K9me3 and CBX1 staining in MSYq-deficient males [40] . These observations imply that the repressive effect of SLY on sex chromosome gene expression in spermatids is due to a global effect on PMSC via ubiquitous mediators of heterochromatinization/transcriptional silencing . Despite the fact that SLY has been shown to interact with the histone acetyl transferase KAT5 [17] , so far no obvious change of histone acetylation was detected in Sly-deficient round spermatids ( unpublished data ) . KAT5 is highly expressed in spermatocytes but poorly expressed in spermatids ( [41] and unpublished data ) , and it is possible that the effect of SLY on KAT5 function is too subtle to be observed with our current tools . The SLY protein is related to SYCP3 and XLR , two nuclear proteins thought to associate with chromatin via their conserved COR1 domain ( NCBI Conserved Domains Database; http://www . ncbi . nlm . nih . gov/Structure/cdd/cdd . shtml ) [15] . Based on cytoplasmic/nuclear protein extracts , SLY protein is predominantly located in the cytoplasm of round and early-elongating spermatids , but a significant fraction is nevertheless observed in the nucleus [17] . However , the nuclear localization has not been documented by immunostaining . Using modified immunohistochemistry protocols , we have now been able to observe SLY protein in the nuclei of spermatids from stage II–III until early stage IX , with the intensity of the signal increasing through spermatid development . SLY nuclear staining is then excluded from the nuclei at the onset of spermatid elongation ( from stage IX , see Figure 7 and Figure S9 ) . SLY nuclear localization is consequently specific to round spermatids ( probably excluding stage I round spermatids in which SLY nuclear staining was not detectable above background ) . At higher magnification , SLY seemed to localize to a DAPI-dense subnuclear structure that could be the PMSC ( Figure S9C ) . This was confirmed in spread spermatids in which SLY clearly colocalized with either the X or the Y PMSC in 66% of round spermatids ( Figure 8 and Figure S10 ) . In addition to PMSC colocalization , SLY protein was sometimes observed outside PMSC ( ectopic , Figure 8 ) ; the reason for this non-PMSC localization remains to be determined . The 31 . 5% of spermatid nuclei without an SLY signal may be accounted for by the absence of nuclear SLY in early-stage spermatids . These data strongly suggest that SLY induces gene repression via direct interaction with the PMSC or with PMSC protein partners such as histone-modifying enzymes . In a previous study , we observed that SLY interacts with the acrosomal protein DKKL1 [17]; the severe defects of Sly-deficient sperm could be a consequence of the disturbance of this interaction . However , DKKL1 intracellular localization was unchanged in Sly-deficient testes ( Figure S11 ) , and the global level of DKKL1 protein in purified spermatids was also unaffected ( Figure S2 ) . In addition to the massive up-regulation of sex chromosome genes expressed in spermatids , some autosomal genes were identified as up-regulated in Sly- and MSYq-deficient mice ( see Figure 4A; and category 2 , Figure S5 ) . Intriguingly , these included five members of the multicopy Speer gene family and several autosomal genes encoding variants of histones H3 and H4 ( Table 2 ) . Genes that were down-regulated in shSLY and in MSYq-deficient mice ( category 3 ) were exclusively autosomal ( aside from a provisionally Y-linked pseudogene ) . These down-regulated genes include Chaf1b , a chromatin assembly factor ( Figure S5 ) . The microarray analysis on purified round spermatids confirmed the change of expression for the majority of these autosomal genes ( Figure S6 ) . Other autosomal genes were found to be up-regulated or down-regulated in shSLY mice , but not in MSYq− mice ( category 1a and 1b , Figure S5 ) . This could be due to off-target effects of shSLY transgene expression , but no particular pattern/pathway was apparent . Genes of the interferon pathway , previously reported to be activated by some shRNAs [22]–[24] , were not up-regulated in shSLY mice , supporting the evidence from the microarray analyses performed before the onset of Sly expression that there are no off-target effects . Alternatively , the changes in autosomal gene expression that are specific to shSLY mice could be a consequence of the up-regulation of other MSYq genes such as Ssty1 and Ssty2 transcripts; in MSYq-deficient models , all MSYq genes show reduced expression .
Our previous study suggesting that MSYq encodes information required for the repression of PMSC identified 18 up-regulated sex-linked genes in testes of MSYq-deficient mice: 16 from the X chromosome and two from the Y chromosome short arm [6]; many of these genes were exclusively expressed in spermatids . Here , our more extensive analysis of MSYq-deficient mice together with Sly-deficient mice identified 113 up-regulated sex-linked genes: 86 X-linked and 27 Y-linked . The latter mice served to establish that the up-regulation is due to Sly deficiency and that this up-regulation includes multicopy MSYq genes such as Ssty1 and Ssty2 . A further microarray confirmed the up-regulation in purified spermatids . SLY is consequently the MSYq factor required for PSCR in the mouse , and the generality of the sex-linked gene repression demonstrates that SLY acts to globally repress the PMSC . How does SLY mediate this global repression of the PMSC in X and Y spermatids ? Importantly , we have shown that SLY is nuclear from stage II to IX and localizes to the PMSC of X and Y spermatids . This localization may involve the SLY COR1 domain , which is proposed to mediate association with chromatin ( NCBI Conserved Domains Database ) [15] . The localization to X-bearing spermatids will have been facilitated by the sharing of gene products via intercellular bridges [42] . In the male germline , the X and Y chromosomes are initially transcriptionally inactivated at the beginning of pachytene ( meiotic sex chromosome inactivation [MSCI] ) ; this inactivation is triggered by the phosphorylation of the histone H2AX [1] . During the transition from MSCI to PSCR , there are changes in nucleosomal histones , in epigenetic histone marks , and in the recruitment of heterochromatin proteins; these chromatin features are lost from the PMSC during stage XI [3]–[5] , [34] , [35] , [43] . This loss at stage XI is unsurprising since it is the stage when the replacement of histones with protamines is initiated . Of importance in the current context is the recruitment of the heterochromatin protein CBX1 during diplotene , coincident with the loss of H2AX phosphorylation , that is presumed to be responsible for sex chromosome repression following the shutdown of MSCI [4] , [5] , [43] , [44] . It is thus significant that we have found that SLY subsequently plays a role in maintaining CBX1 enrichment in the PMSC , and thus , in maintaining a substantial degree of transcriptional repression . Hypermethylated H3K9 is known to be a platform for CBX1 recruitment [36] , [37] , and in view of the changes in nucleosomal histones during the MSCI-PSCR transition [3] , maintenance of CBX1 enrichment is likely to require continuing H3K9 methylation as the new histones are introduced . It is therefore noteworthy that SLY is also involved in maintaining H3K9 trimethylation . In our microarray screening , several autosomal and sex-linked genes coding for histones H2 , H3 , and H4 variants ( including recently identified spermatid-specific H2A variants , H2al1 and H2al2y [45] , [46] ) were found up-regulated when Sly expression is reduced . Conversely , Chaf1b , which encodes a chromatin assembly factor , appears down-regulated . All these changes could contribute to the derepression of sex chromatin . The importance of histone H3 ( and particularly of variant H3 . 3 ) in germline function has been recently described in the mouse and Drosophila [3] , [47] . What is the molecular basis for the link between Sly deficiency and the spermiogenic defects in MSYq-deficient mice ? SLY interacts with the acrosomal protein DKKL1 [17] , but our study shows that DKKL1 level and pattern of expression are not noticeably affected by Sly deficiency . However , the reduction/absence of SLY leads to a dramatic up-regulation of many X and Y genes in spermatids . This up-regulation is almost certainly not benign , and we propose this to be the major contributing factor to the spermiogenic defects of Sly- and MSYq-deficient mice . Actrt1 [30] , Spaca5 [31] , and Cypt [33] , which are up-regulated in Sly-deficient spermatids , encode proteins of the perinuclear theca and the acrosome , two specific structures of the sperm head , and thus are candidates for contributing to sperm head defects . Candidates for effects on sperm function include 1700008I05Rik , an X-linked homolog of Tcp11 , which codes for a receptor of a fertilization promoting peptide thought to promote sperm capacitation/function [48]; Rhox3a , Rhox3h , and Rhox11 , related to Rhox5 , which has been implicated in sperm production and motility [49]; and the A-kinase anchoring protein Akap14 , which is predicted to regulate flagellum function , and consequently , sperm motility [50] . Future work on these many candidate genes will be required to determine their involvement in MSYq-deficient spermiogenesis phenotypes . If the sperm abnormalities in MSYq-deficient mice are solely a consequence of Sly deficiency , then there should be a correlation between the extent of Sly reduction and the severity of the sperm defects . The relevant genotypes in order of decreasing transcript levels ( given as percentage of control ) are: 2/3MSYq− ( 40% ) , sh367 ( 30% ) , sh367 2/3MSYq− ( 10% ) , and 9/10MSYq− ( <1% ) [6]; this is the same order as that for increasing severity of sperm head defects . However , the latter three genotypes were indistinguishable with respect to the expression of SLY protein , since none could be detected by Western analysis . From sequence data for the remaining transcripts , it appears that the majority encode variant ( but presumably functional ) SLY proteins that are unlikely to be detected by our antibody , thus providing an explanation for the seeming discrepancy between RNA and protein levels . Nevertheless , it is important to bear in mind that 9/10MSYq− mice differ from shSLY mice in that the former are deficient in other MSYq-encoded transcripts ( i . e . , Ssty1 , Ssty2 , Asty , and Orly ) , which could contribute to the severity of their sperm defects . There is now substantial data documenting that many sex-linked spermatid-expressed genes in the mouse are highly amplified [2] , [33] , [46] , with MSYq genes being especially highly amplified [15] , [16] ( J . Alfoldi and D . C . Page , personal communication ) . There are currently two hypotheses that seek to explain this amplification: first , that it is a response to PSCR enabling sufficient expression of some X and Y genes with critical postmeiotic functions [2] , and second , that it is driven by a genomic conflict involving postmeiotic competition between X- and Y-encoded gene products that affect sex ratio [13] , [14] . Given the variety of genes involved , the PSCR-amplification hypothesis is attractive since it explains why so many different genes have become simultaneously amplified in the mouse: it seems unlikely that they could all affect sex ratio . However , a challenge for the hypothesis is to explain why the same degree of amplification is not seen on the Y chromosome of other species ( J . Alfoldi and D . C . Page , personal communication ) . This can be resolved by our finding that one of the amplified mouse-specific genes , Sly , regulates PSCR ( our present data ) . We therefore propose that the mouse-specific expansion of sex-linked spermatid-expressed gene copy number is a downstream consequence of the enhancement of PSCR that accompanied Sly amplification . So what drove Sly amplification ? The straightforward answer would be that this was necessary in order to maintain Sly function in the face of the enhancement of PSCR , but this creates the paradox that Sly has become amplified in order to escape its own repressive effects . This in turn implies that the enhancement of PSCR must also have been of selective advantage; otherwise , this function of Sly would have been lost . One possibility is that the enhancement of PSCR was a weapon in a postmeiotic genomic conflict , where one or more of the genes on the X chromosome acts to distort the sex ratio in favor of females , whereas Sly acts via PSCR to repress the distorter gene ( s ) and restore a normal sex ratio . The fact that 2/3MSYq− mice have a sex ratio distortion in favor of females is strong evidence that MSYq does encode a factor or factors that are suppressing sex ratio distortion . For shSLY mice , we observed a mild sex ratio skew of borderline significance . It may be possible to create further shSLY lines with a milder phenotype more comparable to that of 2/3MSYq− mice to enable us to obtain more extensive breeding data than that obtained with the severely subfertile mice in the present study . Our proposals concerning the role of Sly in driving sex-linked spermatid-expressed gene amplification are summarized in Figure 9 . In conclusion , SLY has a predominant role in postmeiotic sex chromatin repression , as it is required for the maintenance of the heterochromatin protein CBX1 on PMSC . Sly deficiency recapitulates almost all of the phenotypes observed in mice with MSYq deletions . Thus , Sly encodes the spermiogenesis factor identified 17 y ago on the Y long arm [10] . Future studies of the many genes that we found differentially expressed in shSLY mice will help in understanding the direct cause ( s ) of the multiple spermiogenesis defects observed in Sly- and MSYq-deficient mice . Here , we have used transgenic delivery of siRNAs to disrupt the function of a multicopy Y gene , and the same approach could be used for multicopy genes on other chromosomes , for example , Slx , a gene related to Sly . Furthermore , despite numerous attempts in several laboratories , no one has reported the successful disruption of the function of a single-copy Y gene using traditional gene targeting strategies [51]; transgenic delivery of Y gene–specific siRNAs should be an effective alternative .
To generate the U6shSLY constructs , we used a PCR-based approach similar to that described in Harper et al . , 2005 , using primers designed to generate the shSLY sequences [20] ( cf . Table S3 ) . The PCR products were cloned into the pCR2 . 1 vector and sequenced ( TOPO TA Cloning , Invitrogen ) . The U6shSLY cassettes were then subcloned into pCX-eGFP plasmid [52] . Prior to injection , the plasmids were linearized at ApaL1 and BamH1 sites and on-column purified from agarose gels ( Gel Extract II kit , Macherey Nagel ) . Fertilized eggs from CBA/Ca×C57BL/10 mating were microinjected with the construct , using standard protocols . Transgenic founders carrying the pCX-eGFP-U6-shSLY construct ( shSLY mice ) were identified by the ubiquitous expression of eGFP . Two female founders with “strong” eGFP expression were obtained ( one transgenic for the sh136 construct , the other for the sh367 construct ) and crossed with XYRIII males on a random-bred MF1 albino ( National Institute for Medical Research colony ) background . These females transmitted the transgene and gave rise to two lines of transgenic mice . The lines are maintained by further backcrossing shSLY transgenic females to MF1 mice and generate XYRIII males with ( tsgic ) and without ( neg sib ) the transgene . To produce 2/3MSYq− sh367 transgenic mice , sh367 transgenic females were crossed with XYRIIIqdel males on an MF1 background [12] . The breeding strategy to obtain 9/10MSYq− , MSYq− , and control mice was described previously [15] . Animal procedures were in accordance with the United Kingdom Animal Scientific Procedures Act 1986 and were subject to local ethical review . To obtain cell fractions enriched in spermatids , an adapted protocol from the trypsin method described by Meistrich [53] was used . Testes from a group of four to five adult mice from the same genotype ( i . e . , three groups of sh367 transgenic mice , three groups of sh367-negative siblings , and two groups of 2/3MSYq− ) were used for the study . Testes were dissected and chopped in 20 ml of DMEM ( GIBCO ) and treated with 2 . 5 mg/ml trypsin ( GIBCO ) and 50 µg/ml DNase I ( Sigma ) for 30 min at 31°C with stirring . After adding fetal calf serum ( GIBCO ) ( final concentration of 8% ) , the cells were passed through a 100-µm filter . Cells were then centrifuged at room temperature ( 500 g , 15 min ) , resuspended in DMEM 0 . 5% bovine serum albumin ( Sigma ) with 50 µg/ml DNase I , and cooled on ice . Cells were counted and checked for clumps before proceeding with the elutriation . Cell integrity was checked using Trypan blue . Fractions enriched in different testis cell types were separated with a JE-6B elutriator ( Beckman ) with conditions described before [54] . Collected fractions were washed in PBS , and cell pellets were frozen down at −80°C . Fraction content was assessed based on cell morphology after DAPI staining . Fractions #6 contained >90% round spermatids and were used for RNA and protein analyses . Transgenically delivered shRNAs were detected using the Northern blot protocol optimized for short transcripts described by Shukla et al . [55] . Sh136 reverse primer and sh367 reverse primers were used as probes to detect sh136 and sh367 RNAs . All sequences are available in Table S3 . Western blot analyses were performed as described previously [18] . Briefly , 10 to 15 µg of testis or spermatid fraction protein extracts were run on a 12% SDS/polyacrylamide gel . Following transfer and blocking , membranes were incubated overnight with a primary antibody ( anti-SLX antibody [18] and anti-SLY antibody [17] were used at 1/3 , 000; anti-SSTY1 antibody , i . e . , anti-YMT2b [32] , and anti-DKKL1 [R&D Systems] were used at 1/1 , 000 and anti-β-actin [Sigma] at 1/50 , 000 ) . Incubation with the corresponding secondary antibody , coupled to peroxidase and detection by chemiluminescence , were carried out as described by the manufacturer ( SuperSignal West Pico , Pierce ) . Immunofluorescence experiments were performed on testis material fixed in 4% buffered paraformaldehyde as described previously [18] . Anti-SLX [18] , anti-DKKL1 ( R&D Systems ) , and anti-SLY [17] antibodies were used at 1/100 , and a preimmune rabbit serum was used as a control . For nuclear detection of SLY , an additional step of 15-min permeabilization with 0 . 5% Triton X-100 ( Sigma ) was performed prior to antigen retrieval , and blocking was performed using 5% fetal calf serum ( GIBCO ) . Alexa Fluor 594–conjugated peanut agglutinin lectin ( Invitrogen ) was used to stage the testis tubules [56] . A portion of testis ( approximately 25 mg ) was chopped in 1 ml of RPMI medium ( GIBCO ) and transferred to a round-bottomed tube . Five milliliters of fixative solution ( 2 . 6 mM sucrose , 1 . 86% formaldehyde ) were added to the cells drop by drop , and cells were mixed by inverting the test tube three times . The cell suspension was incubated at room temperature for 5 min before proceeding to centrifugation ( 1 , 200 rpm , 8 min ) . The fixative was then removed and the cells resuspended in six drops of PBS ( GIBCO ) . Two drops of the cell suspension were spread on Superfrost Plus slides ( BHD ) and air dried for 2 min . The cells were permeabilized by adding 0 . 5% Triton X-100 to the slides for 10 min , and washed twice in PBS before incubation in blocking buffer ( PBS , 0 . 15% BSA , 0 . 1% Tween-20 ) for 30 min at room temperature in a humid chamber . Incubation with the primary antibody ( anti-SLY [17] , anti-CBX1 , or anti-H3K9me3 [Upstate] diluted 1/100 ) was carried out for 2 h in a humid chamber at 37°C . Three washes of 2 min in PBS were performed before proceeding with secondary antibody detection as described previously [17] . As a control for specificity , SLY antibody was preabsorbed with 8 µg of SLY immunogenic peptide or with 8 µg of a noncompeting peptide ( SLX peptide [18] ) . Controls are described in Figure S10 . For quantification of CBX1 or H3K9me3 signals , the signal intensity over the PMSC and over the chromocenter was measured using the DeltaVision SoftWoRx software , and PMSC/chromocenter ratio was calculated for each cell . ( See Figure S8 . ) Chromosome painting was performed as described previously [57] . Testes were fixed in Bouin ( Sigma ) and wax-embedded . Five-micron sections were stained with periodic acid–Schiff ( PAS ) . For the analysis of the sperm shedding delay , ten tubules of stage IX to XI were analyzed per mouse , for five mice per genotype . Silver staining of sperm smears obtained from the initial caput epididymis was performed as described previously [11] . In vitro fertilization ( IVF ) was performed with sperm from three sh367 transgenic males and three negative controls , using oocytes from B6D2F1 ( C57BL/6×DBA/2 ) hybrid and MF1 outbred females . Each male was tested in duplicate following initial semicastration . In each IVF session , sperm from each male were incubated in parallel with oocytes from the two types of females . The method for IVF has been reported before [28] . Briefly , sperm were released from a single epididymis directly into T6 medium and capacitated for 1 . 5 h , prior to addition of the oocytes-cumulus complexes obtained from hormonally stimulated females . The gametes were co-incubated for 4 h with sperm density ∼2–3×106/ml . After fertilization , the oocytes were washed and cultured; the number of two-cell embryos was recorded after 24 h . To analyze sperm number and motility , a small portion of sperm suspension was placed in a hemacytometer chamber . Three independent scorings were done per sample , and the final result was a mean of these scorings . The fertility of shSLY mice was assessed over a period of 7 mo by mating two sh367 transgenic males and two negative siblings with MF1 females . Mating was confirmed by the presence of copulatory plugs . For real-time reverse transcription-polymerase chain reaction ( RT-PCR ) , total testis RNA was extracted using Trizol and then DNaseI-treated ( Invitrogen ) . Reverse transcription of polyadenylated RNA was performed with Superscript Reverse Transcriptase II , according to the manufacturer's guidelines ( Invitrogen ) . Real-time PCR was performed using Absolute qPCR SYBR Green ROX mix ( ThermoFisher ) on an ABI PRISM 7500 machine ( Applied Biosystems ) . PCR reactions were incubated at 95°C for 15 min followed by 40 PCR cycles ( 5 s at 95°C , 20 s at 60°C , and 45 s at 68°C ) . Primer sequences are available in Table S3 . Samples from four transgenic mice and three nontransgenic siblings ( negative controls ) , all at 2 mo of age , were analyzed . All reactions were carried out in triplicate per assay , and β-actin was included on every plate as a loading control . The difference in PCR cycles with respect to β-actin ( ΔCt ) for a given experimental sample was subtracted from the mean ΔCt of the reference samples ( negative siblings ) ( ΔΔCt ) . For the quantification of Sly knock-down , values were further normalized to ΔΔCt values of the spermatid-specific control Acrv1 . This was to have a more robust analysis when compared with 2/3MSYq− mice , which have variability in spermatid content . For microarray analyses , absolute expression values were obtained by single-color hybridizations ( Illumina BeadChip , mouse whole-genome array , v2 ) for three sh367 transgenic individuals and matched littermate controls ( negative siblings ) , and RNA from each individual was hybridized separately . A similar analysis was performed on 2/3MSYq− , 9/10MSYq− , and MSYq− samples and appropriate age/strain-matched controls . In each case , pooled RNA from two or three individuals was used as the sample . Differentially expressed genes were grouped into five categories based on their expression ratios across all genotypes ( see Figure S5 ) . Similar microarray analyses were performed on juvenile testes ( 17 d postpartum ) from three sh367 males and three littermate controls ( negative siblings ) . There was no significant change of gene expression between the two groups . Microarray analyses were also performed on purified spermatid fractions from two groups of sh367 transgenic mice , two groups of sh367 negative siblings , and two groups of 2/3MSYq− . For comparisons of the incidence of sperm head abnormalities and of sperm motility ( after conversion of percentages to angles ) , and of the CBX1 and H3K9me3 PMSC/chromocenter intensity ratios , differences between genotypes were assessed by ANOVA using the Generalized Linear Model provided by NCSS statistical data analysis software . Chi-square analysis was used to compare sex ratio and IVF data; for the sex ratio , we used a one-tailed test of significance since we sought to test whether there was a sex ratio distortion in favor of females ( as seen in 2/3MSYq− mice ) . Student t-test was used to compare the data obtained for fecundity , sperm number , testis weight , Northern and Western blot quantification , and real-time PCR ( performed on the ΔΔCt values ) . For microarray analysis , quantile normalization of all expression data was performed using BeadStudio ( Illumina ) . Data for the normal/mutant sh367 animals were compared in BeadStudio , using the Illumina custom error model with a false discovery rate of 5% . For the cluster analysis , normalized data for all samples were imported into Inforsense Discovery Studio ( Inforsense ) , log2-transformed , and expression ratios calculated relative to the appropriate controls . Hierarchical clustering was then performed on the ratio values , using Pearson correlation as the distance metric . | During meiosis in the male mouse , the X and Y chromosomes are transcriptionally silenced , and retain a significant degree of repression after meiosis . Postmeiotically , X and Y chromosome–encoded genes are consequently expressed at a low level , with the exception of genes present in many copies , which can achieve a higher level of expression . Gene amplification is a notable feature of the X and Y chromosomes , and it has been proposed that this serves to compensate for the postmeiotic repression . The long arm of the mouse Y chromosome ( MSYq ) has multicopy genes organized in clusters over several megabases . On the basis of analysis of mice carrying MSYq deletions , we proposed that MSYq encodes genetic information that is crucial for postmeiotic repression of the sex chromosomes and for sperm differentiation . The gene ( s ) responsible for these functions were , however , unknown . In this study , using transgenically delivered small interfering RNA , we disrupted the function of Sly , a gene that is present in more than 100 copies on MSYq . Sly-deficient males have major sperm differentiation problems together with a remarkable postmeiotic derepression of genes encoded on the X and Y chromosomes . Furthermore , the epigenetic modifications normally associated with sex chromosome repression are altered . Our data thus show that the SLY protein is required to mediate postmeiotic repression of the X and Y chromosomes . It is likely that the sperm differentiation problems in Sly-deficient males are largely a consequence of the derepression of the sex chromosomes in spermatids . We propose that the postmeiotic repressive effect of Sly on genes encoded on the X and Y chromosomes drove their massive amplification in the mouse . | [
"Abstract",
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"Methods"
] | [
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] | 2009 | The Multicopy Gene Sly Represses the Sex Chromosomes in the Male Mouse Germline after Meiosis |
There is no point of care diagnostic test for infection with M . Leprae or for leprosy , although ELISA anti PGL-1 has been considered and sometimes used as a means to identify infection . A systematic review of all cohort studies , which classified healthy leprosy contacts , at entry , according to anti-PGL1 positivity , and had at least one year follow up . The outcome was clinical diagnosis of leprosy by an experienced physician . The meta-analysis used a fixed model to estimated OR for the association of PGL-1 positivity and clinical leprosy . A fixed model also estimated the sensibility of PGL-1 positivity and positive predictive value . Contacts who were anti PGL-1 positive at baseline were 3 times as likely to develop leprosy; the proportion of cases of leprosy that were PGL-1 positive at baseline varied but was always under 50% . Although there is a clear and consistent association between positivity to anti PGL-1 and development of leprosy in healthy contacts , selection of contacts for prophylaxis based on anti PGL1 response would miss more than half future leprosy cases . Should chemoprophylaxis of controls be incorporated into leprosy control programmes , PGL1 appears not to be a useful test in the decision of which contacts should receive chemoprophylaxis .
Leprosy remains a neglected disease , in some parts of the world [1] , with a high new case detection rate in spite of worldwide control efforts [2] . Most cases are concentrated in remote areas [3 , 4] . At primary care , leprosy diagnosis is clinical: presence of skin lesion ( s ) with altered or absent sensibility . Early detection and treatment would reduce transmission . More recently , the idea of leprosy prophylaxis is being promoted as a way of reducing transmission . As in the case of many other neglected diseases , new tools are needed for early detection of cases , if we are to achieve a marked reduction in incidence in a short timeframe . An accurate point of care test for the diagnosis of leprosy disease or infection could have a major impact in detection . Point of care diagnostic tests target biomarkers of infection or disease . In the early 1980’s , Brett et al . described an ELISA test to detect IgM and IgG antibodies against the phenolic glycolipid ( PGL ) component of Mycobacterium leprae . Earlier serological tests for M . leprae antigens had shown low specificity and the discovery of PGL test created a substantial expectation , given the high specificity reported initially [5] In 1998 a dipstick assay was developed to detected anti-PGL-1 [6] , as a convenient point of care test . The expectation at the time was that a positive anti-PGL-1 result would indicate infection , and a negative result absence of infection . Recent publications still offer this interpretation [7 , 8 , 9] . However , evidence produced since does not confirm such a straightforward interpretation , with variations reported in the validity of the test as a predictor of who will develop leprosy . In this paper , we present results of a systematic review and meta-analysis of the risk of developing leprosy , in leprosy contacts according to anti-PGL-1 test results . This could inform any decision of incorporating or not the dipstick assay for IgM anti-PGL1 in leprosy control programmes .
A systematic literature review protocol strategy was developed based on the ‘Preferred Reporting Items for Systematic reviews and Meta-Analyses’ ( PRISMA ) checklist . The protocol was published in Prospero International prospective register of systematic reviews before its implementation ( PROSPERO 2013:CDRD42013005285 ) . We aimed to include all cohort studies , which classified , at entry , healthy leprosy contacts according to anti-PGL1 positivity and had at least one year follow up . The outcome was clinical diagnosis of leprosy by an experienced physician . Studies with no leprosy cases in one of the groups , and those using any antigen other than PGL1 conjugated with bovine serum albumin ( BSA ) met the exclusion criteria . When more than one paper described the same cohort , we included the one with most information . We searched PUBMED , EMBASE , LILACS , IMSEAR , WPRIM , WHOLIS , IMEMR and INDMED from 1983 , when the technique for detection of anti-PGL-1 was published , to April 2015 . The electronic search strategy on PUBMED was: ( ( "Contact"[Journal] OR "contact"[All Fields] OR "Contact"[Journal] OR "contact"[All Fields] ) OR contacts[All Fields] ) AND ( ( "leprosy"[MeSH Terms] OR "leprosy"[All Fields] ) OR ( "leprosy"[MeSH Terms] OR "leprosy"[All Fields] OR ( "hansen"[All Fields] AND "disease"[All Fields] ) OR "hansen disease"[All Fields] ) ) AND ( anti-phenolic[All Fields] OR ( phenolic[All Fields] AND ( "glycolipids"[MeSH Terms] OR "glycolipids"[All Fields] OR "glycolipid"[All Fields] ) ) OR anti-PGL-1[All Fields] OR PGL-1[All Fields] OR ( "immunology"[Subheading] OR "immunology"[All Fields] OR "serology"[All Fields] OR "serology"[MeSH Terms] OR "serology"[All Fields] OR "serologic tests"[MeSH Terms] OR ( "serologic"[All Fields] AND "tests"[All Fields] ) OR "serologic tests"[All Fields] ) ) We decided to include papers written in English , French , Spanish or Portuguese . Endnote files kept all selected references and abstracts . Two authors ( SN and PI ) read the abstracts and selected the papers for inclusion in the review . When they disagreed , a third author ( MLFP ) reviewed it based on the paper’s full text . These three authors assessed the paper’s full text defining those to include in the systematic review . One of the authors ( MLFP ) abstracted the data and another ( SN ) checked it . Our main measure of association was the odds ratio ( OR ) and its log transformation ( LOR ) based on the number of patients at the beginning of follow up in each category ( anti_PGL1 positives and negatives ) and the number of cases in each category . We used the Tool to Assess Risk of Bias in Cohort
Studies from Cochrane Bias Methods Group to classify each paper . We did not apply items 4 and 5 since these items were about the presence and control of other prognostic factors , which was not relevant for this review . We also abstracted data about the site of the study , proposed time of follow up , type of antigen used , technique of the test , dilution used and cut off point . We estimated the summary LOR as the combined inverse-variance weighted LOR of the individual studies , i . e . , used a fixed effect model . As a measure of heterogeneity , we used Cochran’s Q ( this has the same distribution as chi square with n -1 degrees of freedom , where n is the number of studies ) . The set of studies was considered heterogeneous if p<0 . 1 . The inconsistency index was estimated ( I2 ) and if the index was 40% or less , we considered that the inconsistency was not important . A funnel plot evaluated publication bias Sensitivity analysis was based on the variation of the summary OR when one study was removed . We present a ROC plane plot with the results of each study . Ulrich et al . was excluded from the plot because in this study included all contacts with negative reactions to M leprae and only a sample of those with positive reaction . The study sample is not balanced in respect of all possible immunological response among contacts , although it has internal validity .
We retrieved abstracts of 462 papers and we selected 27 for full-text reading . From those , 9 were selected for the systematic review and 8 entered for the meta-analysis ( Fig 1 ) . We accepted the authors’ definition of household contacts and considered neighbourhood contact if the study selected their sample due to the presence of leprosy cases in an area . Table 1 presents some characteristics of the selected the studies [10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18] . Table 2 shows the extracted data for each study . Table 3 shows the bias assessment of the papers . Brasil et al . paper [14] was excluded from the meta-analysis because the follow up procedures were not the same in those who were PGL-1 positive and those who were negative: the anti-PGL-1 positive group had annual medical consultation scheduled during the four years follow up period , but the anti-PGL-1 negative group received the test result with information about leprosy signs and symptoms , and the PGL-1 results and interpretation , but there was no active follow up and leprosy diagnosis in this group depended on the individual demand for medical consultation . We considered this to be differential follow-up as the leprosy diagnosis strategy introduced severe ascertainment bias and thus excluded the study from the meta-analysis . We considered this differential follow-up . Leprosy diagnosis strategy introduced severe ascertainment bias and excluded the study . Fig 2 shows the forest plot of the included studies . The total number of contacts included in these studies was 18197 , with 4140 anti PGL1 positives and 14057 anti PGL1 negatives . The summary ORs with Brasil et al . ( 14 ) study removed varied from 2 . 72 to 3 . 53 , but all the 95% confidence interval included 3 . 11 , the fixed model point estimate . The summary measure with random effects estimate was 3 . 05 CI95% [1 . 99–4 . 67] . The point out of the confidence limit of the funnel plot ( Fig 3 ) represents the excluded paper that had an OR of 10 . 18 . Fig 4 graphically represents the sensitivity and 1-specificity of each study . The sensitivity varied from 2% [16] to 39% [17] and the specificity from 83% [13]to 98% [16] . Table 4 presents these values and the positive predictive value ( PPV ) of each study , i . e . , the proportion of positives results that developed clinical leprosy . Douglas 2004 is the study with higher PPV due to a high specificity and a moderate sensibility . Chanteau 1993 and Sinha 2004 had higher specificity but a very low sensibility .
Although there is a clear and consistent increase in risk of development of leprosy in anti PGL-1 positive healthy contacts , selection of cases for prophylaxis intervention based on anti PGL1 response would reach less than half of future leprosy cases , and result in much unnecessary treatment . Leprosy research must explore the role of antibody production in leprosy and it is similar to that in tuberculosis . | Contacts of leprosy cases are more likely to be infected and develop leprosy . But not everyone infected with M . Leprae develops clinical leprosy . into clinical disease . We examined and summarized all the eight studies that evaluated how well PGL-1 predicts which contacts of leprosy will become cases . PGL-1 positive contacts were 3 times more likely to develop leprosy; a variable proportion , but less than 30% of the cases were attributed to PGL-1 and less than 45% of the PGL-1 contacts developed leprosy . PGL1 would not be an appropriate test to decide which contacts of leprosy should receive preventive therapy if this was proposed in leprosy control programmes . | [
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] | 2016 | Anti-PGL-1 Positivity as a Risk Marker for the Development of Leprosy among Contacts of Leprosy Cases: Systematic Review and Meta-analysis |
There was a dengue epidemic in several regions of China in 2013 . No study has explored the dynamics of dengue transmission between different geographical locations with dengue outbreaks in China . The purpose of the study is to analyze the epidemiological characteristics and to explore the dynamic transmission of dengue in China , 2013 . Records of dengue cases of 2013 were obtained from the China Notifiable Disease Surveillance System . Full E-gene sequences of dengue virus detected from the outbreak regions of China were download from GenBank . Geographical Information System and heatmaps were used to describe the epidemiological characteristics . Maximum Likelihood phylogenetic and Bayesian phylogeographic analyses were conducted to explore the dengue dynamic transmission . Yunnan Province and Guangdong Province had the highest imported cases in the 2013 epidemic . In the locations with local dengue transmission , most of imported cases occurred from June to November 2013 while local dengue cases developed from July to December , 2013 . There were significant variations for the incidences of dengue , in terms of age distributions , among different geographic locations . However , gender differences were identified in Guangzhou , Foshan and Xishuangbanna . DENV 1–3 were detected in all locations with the disease outbreaks . Some genotypes were detected in more than one locations and more than one genotypes have been detected in several locations . The dengue viruses introduced to outbreak areas were predominantly from Southeast Asia . In Guangdong Province , the phylogeographical results indicated that dengue viruses of DENV 1 were transmitted to neighboring cities Foshan and Zhongshan from Guangzhou city , and then transmitted to Jiangmen city . The virus in DENV 3 was introduced to Guangzhou city , Guangdong Province from Xishuangbanna prefecture , Yunnan Province . Repeated dengue virus introductions from Southeast Asia and subsequent domestic dengue transmission within different regions may have contributed to the dengue epidemics in China , 2013 .
Dengue is a mosquito-borne viral infectious disease caused by the four antigently distinct serotypes ( DENV 1–4 ) , which are mainly transmitted by Aedes aegypti and Aedes albopicuts . Dengue is endemic in more than 100 countries in tropical and subtropical areas , especially in Southeast Asia , the Americas , the Western Pacific , Africa and Eastern Mediterranean regions [1] . Because of unprecedented population growth , uncontrolled urbanization , spread of the mosquito vectors and the population movement , the incidence of dengue has increased dramatically in the past 50 years [2] . It is estimated that 390 ( 95% CI: 284–528 ) million people have dengue virus infections with 96 ( 95% CI: 67–136 ) million cases annually worldwide [3] . The dynamics of dengue transmission depends on the interactions among hosts , viruses , vectors and environmental factors . Given the restricted range of mosquito flying distance [4 , 5] , population movement may play a critical role for dengue transmission . At broad spatial scales ( e . g . , national , international ) , human movements may make dengue virus being introduced and reintroduced into a region with lower herd immunity [6] . Travel acquired cases were repeatedly imported to Europe from Africa , Southeast Asia and the Americas [7–9] . Local dengue transmission has occurred in Europe for the first time in many decades , with indigenous cases reported in France and Croatia in 2010 [10 , 11] . In addition to sporadic cases , dengue outbreak also occurred in Europe . For example , an outbreak with more than 2 , 000 cases happened in Madeira , Portugal in 2012 , which was most probable origin of Venezuela [12] . Aware of the importance of air travel in dengue transmission , researchers developed simple models to estimate the importing risk of dengue or even the possible origin of importation in Europe [13 , 14] . In Asia , because of the reintroductions of dengue viruses from Southern Vietnam where dengue is endemic , Northern Vietnam had dengue epidemics occurred frequently [15] . At finer spatial scales ( regional , intra-urban , neighborhood ) , population movements associated with work and recreation are important for dengue transmission [6] , and house-to-house human movements may shape spatial patterns of dengue incidence , causing significant heterogeneity in dengue incidence [16] . There was no dengue case notified from 1949 to 1977 in China until an outbreak occurred in Guangdong Province in 1978 . Since then , dengue has been detected for nearly forty years in China . It was prevalent in Southern China including Guangdong Province , Hainan Province and Guangxi Province in 1980s . Since 1990 , dengue was predominantly occurred in Guangdong Province . Geographically , the dengue outbreaks have expanded gradually from Guangdong , Hainan and Guangxi in Southern coastal regions of China to the relatively Northern regions including Fujian , Zhejiang Provinces and to the relatively Western region Yunnan Province [17] . Neighboring to Myanmar , Laos and Vietnam , Yunnan Province had its first dengue outbreak with 56 cases reported in 2008 , of which most were imported cases from Myanmar [17] . In 2013 , Guangdong , Yunnan and Henan Provinces had dengue outbreaks . This was the first dengue outbreak in Henan Province , the most northern Province with dengue local transmission . The 2013 outbreak in Yunnan was the second outbreak and also the first severe dengue outbreak in the Province . Guangdong Province has the highest dengue incidence with cases reported every year since 1997 , with the most prevalent in 2013 . Our previous study has proven that dengue was still an imported disease in China [18] , and the epidemics were probable to be originated from overseas . Three individual studies have reported the dengue outbreaks occurred in Yunnan and Henan Provinces in 2013 [19–21] . However , no study tried to explore the transmission dynamics between locations . The purpose of the study is to describe the 2013 epidemiological characteristics and to explore the possible origins of the epidemics , and the dynamics of dengue viruses between epidemic focus , which are important to dengue control and prevention .
Ethical approval for the study was obtained from the Chinese Center for Control and Prevention Ethical Committee ( No . 201214 ) and patient data in the study were de-identified and analyzed in aggregated format . Records of notified dengue cases of 2013 were obtained from the China Notifiable Disease Surveillance System , including age , gender , occupation , date of onset , type of diagnosis , local case or not . At the study areas , a dengue case is defined as an imported case for which the patient had traveling history to a dengue affected area and reported being bitten by mosquitoes within 15 days of the onset of illness . In some cases , importation is defined based on laboratory results showing that the infecting dengue virus had a high sequence similarity in the preM/E region compared with viruses isolated from the putative source region where the patient had traveled to . Otherwise , the dengue case is considered to be a local case [22] . Henan , Yunnan and Guangdong Provinces had dengue outbreaks in 2013 . Full E-gene sequences of dengue virus detected in these Provinces in 2013 were downloaded from GenBank ( As of August 25th 2015 ) ( S1 Table ) . The sequences detected in China were compared with published sequences by using the nucleotide blast program in the NCBI . S2–S4 Tables were the references downloaded with the accession number , collection date and geographical region . The population data were from the Sixth National Population Census of China conducted by the National Bureau of Statistics of the People’s Republic of China in 2010 . The information of epidemiological investigation was downloaded from China Public Health Emergency Management Information System .
There were 4 , 779 dengue cases reported in the China Notifiable Disease Surveillance System in 2013 , including 543 imported cases and 4 , 236 local cases . No dengue case was notified in Tibet , Qinghai , Ningxia and Shanxi Provinces . Imported dengue cases were reported in other Provinces with the highest numbers in Yunnan and Guangdong Provinces ( Fig 1 ) . In Henan Province , outbreak occurred in central China , Xuchang city with the incidence of 0 . 70 per 100 , 000 . In Southwestern China , dengue outbreaks occurred in Dehong prefecture and Xishuangbanna prefecture locating in west and south of Yunnan Province with incidence of local cases 11 . 97 and 112 . 13 per 100 , 000 , respectively . Dengue outbreaks occurred in Central and South of Guangdong Province ( Southern China ) including Guangzhou city , Foshan city , Dongguan city , Zhongshan city , Zhuhai city and Jiangmen city . The incidence ranged from 0 . 12 to 25 . 92 per 100 , 000 , with Zhongshan city the highest and Guangzhou the second highest incidence ( Fig 2 ) . In the regions with local dengue transmission , imported dengue cases occurred almost all year around , with the most cases happening from June to November—accounting for 93 . 89% of cases ( 215/229 ) ( the number of imported cases in the regions with local dengue transmission was 229 ) . Dehong and Xishuangbanna had the most imported cases , accounting for 68 . 12% ( 156/229 ) ( S1 Fig ) . Local dengue cases occurred in July to December . The first local dengue case occurred in Zhongshan city , Guangdong Province ( S2A Fig ) . Gradually , the cities around Zhongshan city all had dengue outbreaks . Meanwhile , dengue outbreaks also hit Xuchang city , Henan Province and Dehong prefecture and Xishuangbanna prefecture , Yunnan Province ( S2B Fig ) . The local incidence among different age groups showed significant differences in each outbreak area and the overall incidence in different age groups had a gradual increase with the age ( Fig 3A ) . The local incidence between male and female was significantly different in Xishuangbanna , Guangzhou and Foshan , and the overall local incidence between male and female had significant difference , with more cases in females than in males ( Fig 3B ) . DENV 1–3 were detected in all regions with dengue outbreaks in 2013 ( S3–S5 Figs ) . Among these , DENV 1-I , DENV 1-V , DENV 3-II and DENV 3-III were detected in more than one outbreak regions . More than one genotypes have been detected in several locations ( Fig 4 ) . The dengue viruses detected in Guangdong Province in Clade 1 showed that Thailand strains were introduced to Guangzhou first , and then transmitted into its neighboring cities: Foshan and Zhongshan , and were then transmitted to Jiangmen from Zhongshan ( Figs 5A and 6A ) . The phylogeographical results indicated that dengue viruses detected in Guangzhou , located in Clade 2 , 3 , 11 , 12 , were probably imported from Thailand , Singapore , Malaysia , Indonesia ( Figs 5A , 7B , 6A and 6B ) , while Clade 13 showed that some Guangzhou strain was transmitted from Xishuangbanna , Yunnan Province ( Figs 8A and 6C ) . Dengue viruses detected in Dehong , Yunnan Province ( Clade 4 , 9 ) were probably introduced from its neighbor , Myanmar ( Figs 5A , 7A , 6A and 6B ) . Some strains detected in Jiangmen , the strain in Dongguan and some strains in Zhongshan , Guangdong Province , were probably imported from Singapore ( Clade 5 , 7 , 16; Figs 5A , 5B , 8B , 6A and 6C ) . The outbreak occurred in Zhuhai , Guangdong Province was probably caused by introduced viruses from Thailand ( Clade 6 , Figs 5A and 6A ) . The results indicated that some strains detected in Foshan , Guangdong Province were introduced from India , Cambodia and Laos ( Clade 8 , 10 , 14; Figs 5B , 7A , 8A and 6 ) . Fig 8A indicated that the dengue viruses in Xishuangbanna were probably introduced from Laos ( Clade 13; Fig 6C ) . In addition , the viruses detected in Henan Province were also probably imported from Laos ( Clade 15; Figs 8A and 6C )
Dengue has been detected in China for nearly 40 years , and has become more serious in these years with increased incidence and expanded outbreak regions . Our previous study has proven that dengue is still an imported disease in China [18] , and the results from this study suggested that the introduced dengue cases from overseas may have contributed to the dengue outbreaks . The 2013 outbreaks occurred in different locations of China were mainly due to the introduced viruses from Southeast Asia and domestic dengue transmission within different regions of China . The demographic characteristics of dengue cases showed that dengue mainly affects adults in China , which were different from that in dengue endemic countries , where adult acquired lifelong immunity [28] . Adults were vulnerable in China given they have more opportunities to expose to mosquitoes when there is lower herd immunity . The sex distribution of dengue varied in different countries , and even in different locations or different epidemic time periods within a country [29–32] . The sex distribution of dengue may be attributed to the relevant demand on the health services of different sexes . Located in Central China , Henan Province has a temperate climate . This was the first dengue outbreak in the Province . Geographically , it was the northernmost place with dengue outbreak in China , which is almost on the same latitude as Madeira , Portugal where dengue outbreak occurred in Europe . Generally , dengue occurs mainly in tropical and subtropical areas , where suitable climatic conditions play great roles in dengue transmission . Liu-Helmersson found that dengue epidemic potential in temperate was increased with increasing diurnal temperature range [33] . In addition , human movement plays an important role in dengue outbreak in temperate climate region . The dengue outbreak in Henan occurred in a small village with six porcelain factories . The epidemiological investigation showed that all dengue cases had neither travel history , nor collective activities . However , five people from the village worked in Laos where dengue was endemic . These five people returned to their home village in Henan for their vocational leave in May 2013 . Although none of them were reported having dengue , two family members of these five people reporting dengue infections , indicating that they may have picked up the virus from the Laos and transmitted to their family members . In addition , the products of porcelain factories were usually sold to Guangzhou and Yunnan Province where dengue was prevalent in 2013 [21] . While phylogenetic and phylogeograhical analyses showed that the Henan strains and Yunnan strains ( Xishuangbanna ) were separated into different clades with high posterior probability support ( Clade 13 , 15; Fig 8A ) . Dengue was epidemic in Laos in 2012–2013 and the epidemic was severe in 2013 , which might be attributed to the serotype switch from DENV 1 to DENV 3 , and genotype II of DENV 3 was the predominant genotype [34 , 35] . The strains detected in Henan Province were genotype II of DENV 3 ( Clade 15 ) and the phylogeographical analysis indicated that the dengue viruses were probably introduced from the Laos with high ancestral location state probability . Xishuangbanna prefecture , Yunnan Province , neighboring to Myanmar and Laos , had its first dengue outbreak in 2013 . The phylogenetic results showed genotype II of DENV 3 caused the outbreak ( Clade 13 ) . As mentioned before , the same genotype was prevalent in Laos in 2012–2013 . The epidemiological field investigation showed that there were imported cases from Laos and Myanmar at the beginning of the epidemic , and some businessmen has travel history to Laos . The phylogeographical analysis indicated that the viruses detected in Xishuangbanna were probably introduced from Laos . Therefore , because of the repeated introductions and lack of local herd immunity , the viruses imported from Laos probably contributed to the dengue outbreak in Xishuangbanna , Yunnan Province in 2013 . Dehong prefecture , Yunnan Province , bordering Myanmar also had a large dengue outbreak in 2013 . The epidemiological analysis showed that there were 245 dengue cases in Dehong , of which 101 cases were imported cases from Myanmar . Most local dengue cases engaged in trade activities with Burmese around . The strains detected in Dehong were from local dengue cases and imported cases , and the imported cases were all from Myanmar [20] . The phylogenetic analysis indicated that the dengue viruses detected in Dehong were classified into genotype I in DENV 1 and Asian I genotype in DENV 2 . The ML tree and MCC tree suggested the local cases and some imported cases from Myanmar were clustered together and the phylogeographical analysis indicated that the dengue outbreak occurred in Dehong were caused by two different genotypes , which were both introduced from Myanmar . Therefore , two genotypes co-circulated in Dehong invading from neighboring Myanmar and contributed to the dengue transmission . Dengue epidemics occurred in Dehong and Xishuangbanna , Yunnan Province were attributed to the dengue viruses transmission across the border , and the main dengue transmission vector Aede aegypti existed in these two areas played a critical role in local dengue transmission [36] . In Guangdong Province , local dengue cases occurred in Pearl River Delta of Guangdong ( PRD ) in 2013 , where is one of the most densely urbanized regions in the world [37] . The area is one of the homelands of overseas Chinese , especially to the those live in Southeast Asia , which brings about many travels from there each year . Dengue is endemic in Southeast Asia , with severe dengue outbreaks in Indonesia , Thailand , Singapore and Malaysia in 2013 [38–40] . These countries are tourism resorts and there have been intensive trade activities between these countries and the PRD . The dengue viruses may have imported to PRD frequently from these endemic areas because of the frequent population movement and probably contributed to the dengue outbreaks in these PRD areas . The phylogenetic and phylogeographical results indicated that the strains detected in the PRD areas , especially in Guangzhou showed diversity , and most strains were probably introduced from Singapore , Thailand , Malaysia , Indonesia , Cambodia and Laos ( Fig 6 ) . Neighboring to Guangzhou , Dongguan is characterized by its fast urbanization and manufacturers in southern China . Although dengue infections have been reported in Guangzhou almost every year with four serotypes being isolated , Dongguan was free of dengue until its first dengue outbreak in 2010 , which might be caused by viruses imported from Malaysia [41] . For the second dengue outbreak in Dongguan in 2013 , although the Dongguan strain , Singapore and Malaysia strain were clustered together , the phylogeographical result indicated that Singapore strain caused the dengue outbreak with high location probability . Because of the multiple introductions , dengue outbreaks occurred in epidemic seasons in Southern China in the context of suitable weather conditions [42] . Population movement at finer spatial scales contributed to the epidemic foci expansion , and may contribute dengue transmission from one epidemic city to another one within China [6] . The phylogenetic analyses indicated that the dengue epidemic transmitted to neighboring Foshan city southwestward and Zhongshan city southward from Guangzhou city , and then to Jiangmen city from Zhongshan city ( Fig 6A ) . Given the increased economic link and population movement , there existed the probability dengue virus dispersion to Foshan from Guangzhou , because the outbreak in Guangzhou was earlier than that in Foshan . The phylogenetic analysis showed that the outbreak in Zhongshan was caused by two serotypes , with epidemic two peaks . The cases in Zhongshan were located on the border with Guangzhou and the dengue outbreak in Zhongshan was earlier than that in Guangzhou . Therefore , there existed the probability that the first peak occurring earlier than that in Guangzhou was caused by introduced viruses from Singapore ( Fig 8B; Clade 16 ) and the second was probably attributed to the viruses transmitted from Guangzhou . The cases in Jiangmen city mainly occurred in the districts adjacent to Zhongshan city—this could be because that some of the residents working in Zhongshan while living in Jiangmen due to cheaper property price in Jiangmen and higher salary in Zhongshan , which made the dengue viruses transmitted to Jiangmen from Zhongshan feasible . The phylogeographical analyses showed that Guangzhou not only transmitted viruses to adjacent areas , but also received from other area nationally . The result showed that there existed the probability that dengue virus transmitted to Guangzhou from Xishuangbanna , Yunnan Province . The ML tree and MCC tree both showed the Guangzhou strain was embraced in Xishuangbanna clade . Being the centre for industry , finance , transportation and trade in southern China , there are many migrant workers in Guangzhou , nationally and internationally . Xishuangbanna is a tourism resort , which attracted many people including these from Guangzhou . Given dengue outbreak occurred earlier in Xishaungbanna than that in Guangzhou , the dengue virus in Xishuangbanna probably contributed to dengue transmission in Guangzhou . This is the first study exploring the dengue dynamics in China , which is critical to understand dengue transmission and will be helpful to prevent and control dengue occurrence in China . Because the E-gene data were downloaded from GenBank , we are unable to obtain the exact epidemiological information of each single sequence . Among the three Provinces with dengue outbreaks in 2013 , we tried to assess whether the cases in Yunnan Province were local cases or not from published papers regarding its outbreak [19 , 20] . For Henan and Guangdong Provinces , we contacted the Provincial CDCs , and confirmed that the sequences deposited to GenBank were all from local cases . However , we are not able to get the exact onset time for each case . Dengue is still an imported disease in China . Because of population movement and the close connections between Southern China and Southeast Asia , Southeast Asia is still the main sources of dengue viruses introduced to China . At the background of climate change and the existence of dengue vector , dengue epidemic is not restricted in Southern China any more . The Pearl River Delta of Guangdong are merging more and more close in recent years , which makes dengue virus disperse easily . Therefore , population movement plays a critical role in dengue dynamic transmission , which introduces dengue viruses to non-epidemic areas at broad or finer spatial scales . Apparently relevant dengue control and prevention strategies should be updated . | Dengue is the most prevalent and rapidly spreading mosquito-borne viral disease . As an imported disease in China , the imported cases play a vital role for the local dengue transmission . There were dengue outbreaks in three Provinces ( covering nine Cities/Prefectures ) of China in 2013 , with several regions had their first dengue outbreak in history including the one from central China . There has been no study so far to explore the dengue transmission dynamics between different regions in China . The purpose of the study is to describe the 2013 dengue epidemiological characteristics and to explore the transmission dynamics of dengue viruses between epidemic focus . The study results indicated that repeated dengue virus introductions from Southeast Asia and subsequent domestic dengue transmission within different regions may have contributed to the dengue epidemics in China , 2013 . Population movement could have played a critical role in dengue dynamic transmission , which introduced dengue viruses to non-epidemic areas at broad or finer spatial scales . Therefore , it should be considered in the design of mosquito eradication campaign for dengue control and prevention . | [
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] | 2016 | The Epidemiological Characteristics and Dynamic Transmission of Dengue in China, 2013 |
Revealing the patterns and determinants of the spread of dengue virus ( DENV ) at local scales is central to understanding the epidemiology and evolution of this major human pathogen . We performed a phylogenetic analysis of the envelope ( E ) genes of DENV-1 , -2 , -3 , and -4 isolates ( involving 97 , 23 , 5 , and 74 newly collected sequences , respectively ) sampled from school-based cohort and village-based cluster studies in Kamphaeng Phet , Thailand , between 2004 and 2007 . With these data , we sought to describe the spatial and temporal patterns of DENV spread within a rural population where a future vaccine efficacy trial is planned . Our analysis revealed considerable genetic diversity within the study population , with multiple lineages within each serotype circulating for various lengths of time during the study period . These results suggest that DENV is frequently introduced into both semi-urban and rural areas in Kamphaeng Phet from other populations . In contrast , the persistence of viral lineages across sampling years was observed less frequently . Analysis of phylogenetic clustering indicated that DENV transmission was highly spatially and temporally focal , and that it occurred in homes rather than at school . Overall , the strength of temporal clustering suggests that seasonal bottlenecks in local DENV populations facilitate the invasion and establishment of viruses from outside of the study area , in turn reducing the extent of lineage persistence .
Dengue is the leading cause of mosquito-borne viral disease worldwide , and dengue fever ( DF ) and dengue hemorrhagic fever ( DHF ) continue to increase in both incidence and geographic range . Recent estimates are that over 50 million DENV infections occur each year , including 500 , 000 hospitalizations for DHF , primarily among children [1] , [2] . Dengue viruses ( DENV ) are single-stranded , positive-sense RNA viruses ( family Flaviviridae , genus Flavivirus ) that are comprised of four antigenically distinct serotypes ( or viruses; DENV-1 , DENV-2 , DENV-3 , and DENV-4 ) that co-circulate in many endemic areas in the tropics and sub-tropics . The phenomenon of co-circulation of multiple DENV serotypes is referred to as hyperendemicity and is believed to increase the risk of severe disease in a population [3]–[5] . Hyperendemicity has also complicated vaccine development , and as yet there is no commercially available , licensed vaccine , although a number of vaccine prospects are currently being investigated in clinical trials [6] , [7] . An understanding of the spatial and temporal patterns of DENV spread at various scales is central to determining the factors responsible for both the emergence and the persistence of DENV , and may assist in predicting the spread of DENV following imperfect vaccination . Molecular epidemiological studies of global DENV populations play a key role in understanding the mechanisms of DENV evolution by elucidating critical aspects of epidemiological history , including virus population growth and decline , lineage replacement events , patterns of spatial migration , and rates of evolutionary change . The growing data base of full and partial DENV genome sequences has resulted in several detailed phylogeographic studies , but the focus of these has generally been on endemic and epidemic spread in urban and semi-urban areas [8]–[10] . To date , few studies have examined long-term DENV transmission dynamics in endemic rural areas , where epidemiological patterns may differ from those in more densely populated regions due to environment , social factors , and public health infrastructure . The prospect of vaccine trials and eventual large-scale vaccination to combat dengue warrants an examination of the dynamics of endemic DENV spread at various scales over multiple years . Long-term cohort studies of DENV infection in children in rural Kamphaeng Phet , Thailand , have greatly increased our understanding of the epidemiology and evolution of DENV in an endemic rural area , and provide a unique context to assess DENV transmission dynamics in rural areas [11]–[18] . In this study we examined the spatial and temporal patterns of DENV spread using sequence data from isolates obtained over several years in a longitudinal cohort undergoing school absence-based surveillance and a concurrent geographic cluster study [12] , [18] . The objectives of this study were to characterize the genetic diversity of DENV circulating within a rural population and to investigate whether DENV spread was spatially and temporally focal within this area of stable hyperendemic transmission . Characterizing the genetic diversity and patterns of spread in this population is particularly relevant given that Kamphaeng Phet is a planned site for upcoming dengue vaccine trials .
The study protocol was approved by the Institutional Review Boards of the Thai Ministry of Public Health ( MOPH ) , Walter Reed Army Institute of Research ( WRAIR ) , University of Massachusetts Medical School ( UMMS ) , University of California at Davis ( UCD ) and San Diego State University ( SDSU ) . Written informed consent and assent were obtained from all study participants and/or their parents . The study area and design have been described previously [11] , [12] , [18] . The epidemiology of dengue is well characterized in this region of Thailand due to long-term cohort studies and surveillance conducted since the 1980s by the Armed Forces Research Institute of Medical Sciences ( AFRIMS ) , a joint research endeavor of the U . S . Armed Forces and the Royal Thai Army Medical Departments . In brief , the study was conducted in Muang District , Kamphaeng Phet , north-central Thailand , a relatively sparsely populated region of Thailand with 233 , 033 residents in ∼63 , 500 housing structures located over 1962 km2 . DENV transmission in this region peaks during an annual ‘dengue season’ from June to November [16] . Eleven primary schools were selected for participation based on the presence of dengue cases among their students in the previous five years , proximity to the AFRIMS field station ( including road access ) , and interest of the school administrators . Selected schools were associated with 32 villages ( 8445 houses ) . During the first half of the study , twenty of these villages ( 4685 houses ) were selected for inclusion in the cluster study based on the density of houses , favoring those with houses in close proximity to one another ( <100 m ) . During the second half of the study , all houses within the villages were mapped and used during cluster investigations . Unique codes were assigned for each of the 8445 houses and the associated spatial coordinates and demographics of residents were entered into a Geographic Information Systems ( GIS ) database [MapInfo ( 2000 ) version 6•0; MapInfo Corporation , Troy , New York] . Primary school children in kindergarten through grade six were followed by active school absence-based surveillance during June to November of each year . An acute blood sample was drawn from cohort subjects who reported a fever in the previous seven days or who had a measured temperature ≥38°C . A convalescent blood sample was drawn 14 days later along with an evaluation of symptoms . Details on study design and overall results have been published previously [12] , [18] . Acute blood samples underwent testing , including semi-nested reverse transcriptase polymerase chain reaction ( RT-PCR ) for detection of DENV RNA according to the protocol of Lanciotti et al . [19] with the following modifications . Avian myeloblastosis virus ( AMV RT , Promega , Madison , WI ) reverse transcriptase was used in the first round RT-PCR . The concentrations of primers used in the RT-PCR and nested reactions were reduced from 50 pmol to 12 . 5 pmol per reaction and the nested PCR amplification cycles were increased to 25 . Cohort subjects who were dengue PCR-positive and negative from an acute blood sample drawn within three days of illness onset served as an “index” case for a positive cluster investigation; non-dengue acutely ill subjects served as index cases for negative control clusters . In each cluster , ten to 25 children aged six months to 15 years living within a 100-meter radius of the index case were enrolled regardless of symptomatology . These contact subjects were evaluated at days 0 , 5 , 10 , and 15 with temperature measurement and a symptom questionnaire covering the previous five days . Blood samples were collected on days 0 and 15 and tested for dengue by RT-PCR; virus isolation was attempted in C6/36 cells from all cohort and cluster dengue PCR-positive samples . Female adult Ae . aegypti were collected , after obtaining written permission from the residents , using backpack aspirators from inside and immediately surrounding each home within the cluster , [12] , [18] . Female Ae . aegypti were screened for DENV by RT-PCR using a modified protocol [20] . In brief , pools of ten mosquitoes were made by combining 14 µl from individual mosquito suspensions ( in 100 µl of RPMI containing 1% L-glutamine and 10% heat-inactivated FBS ) and clarified by centrifugation at 8000 rpm at 4°C for 20 min . From positive pools , individual mosquitoes were assayed by serotype-specific PCR using 14 µl of the original suspension in 126 µl of diluent . DENV from individual mosquitoes were amplified by intrathoracic inoculation in Toxorhynchites splendens mosquitoes and/or by passaging three times in C6/36 cells . For human DENV PCR-positive samples , virus isolation was performed in C6/36 cells and/or Toxorhynchites splendens mosquitoes as previously described [21] , [22] . Viral RNA was prepared for sequencing from 140 µl of human serum , mosquito suspension , or culture fluid using the QIAamp viral RNA mini kit ( QIAGEN , Germany ) according to the manufacturer's instructions . RT-PCR was performed using random hexamer oligonucleotides with the SuperScript first-strand synthesis system ( Invitrogen ) according to the manufacturer's instructions . Sequencing of all PCR-positive , isolation-positive samples was attempted but insufficient RNA and isolation failures resulted in some PCR positive cohort and cluster samples to be excluded in this study . The DNA fragments of the envelope gene region of 97 DENV-1 , 23 DENV-2 , 5 DENV-3 , and 74 DENV-4 were amplified by PCR using 5 µl of cDNA in a 50 µl reaction mixture containing 0 . 3 mM dNTPs , 2 . 5 U AmpliTaq DNA polymerase ( Applied Biosystems ) , 1× PCR buffer , 1 . 5 mM MgCl2 and 15 pmol of each forward and reverse primer . PCR reactions for DENV-1 , -3 , and 4 were subjected to 1 cycle of 95°C for 5 min , 35 cycles of 94°C for 30 sec , 50°C for 1 min , 72°C for 2 min , and 1 cycle of 72°C for 15 min . PCR reactions for DENV-2 were subjected to the same thermal conditions as the others , except the annealing temperature was changed to 55°C . The PCR-amplified DNA fragments were purified using QIAquick PCR purification kits ( QIAGEN ) according to the manufacturer's instructions . Purified DNA fragments were used for sequencing . Sequencing reactions were performed using the DYEnamic ET Dye Terminator sequencing kit ( GE Healthcare Bio-Sciences ) according to the manufacturer's instructions . Sequencing primers are available upon request . Sequencing products were cleaned by standard precipitation prior to sequencing in a MegaBACE 500 automated DNA sequencer ( GE Healthcare Bio-Sciences ) . Overlapping nucleic acid sequences were combined for analysis and edited using SEQUENCHER software ( Gene Code Corporation ) . A total of 97 DENV-1 , 23 DENV-2 , 5 DENV-3 , and 74 DENV-4 E gene sequences were obtained from blood samples of infected children included in the school-based cohort and from mosquitoes and infected children detected in village clusters in Kamphaeng Phet from 2004 to 2007 ( Table 1 ) . School-based surveillance accounted for the majority of DENV isolated in this study; 95 were not associated with cluster investigations and 47 served as cluster index cases ( Table 2 ) . From 50 positive and 53 negative cluster investigations initiated during the study period , 47 index case dengue sequences were available and19 clusters yielded at least one DENV E gene sequence from a village contact , while eight clusters yielded sequences obtained exclusively from local mosquitoes . There were an additional four sequences obtained from three negative clusters . To place these isolates within the context of global DENV evolution , complete E gene sequences of all four serotypes with known date and country of sampling were collected from GenBank . Sequences were manually aligned using Se-AL v2 . 0a11 ( available from http://tree . bio . ed . ac . uk/software/ ) , and initial trees were inferred in PAUP using a neighbor-joining algorithm using the HKY85 model of nucleotide substitution [23] . This enabled us to obtain a provisional estimate of the pattern of genetic diversity of DENV isolated within the study . All study isolates from each serotype were found to be of a single , predominantly Asian genotype ( DENV-1 , genotype I; DENV-2 , Asian I genotype; DENV-3 , genotype II; DENV-4 , genotype I; see Results ) . Consequently , global sequence data sets were sub-sampled to include only a subset of closely related sequences of Asian origin from each of the detected genotypes: 1161 DENV-1 ( 1485 nt ) , 531 DENV-2 ( 1485 nt ) , 304 DENV-3 ( 1479 nt ) , and 166 DENV-4 ( 1485 nt ) E gene sequences . Maximum likelihood ( ML ) phylogenetic trees of each genotype were then estimated using PAUP [23] , utilizing the GTR+I+Γ4 model of nucleotide substitution , which was determined by ModelTest [24] to be the best-fit to the data in hand , and employing tree bisection-reconnection ( TBR ) branch swapping . Bootstrap resampling ( 1000 replicate neighbor-joining trees under the substitution model described above ) was performed to assess phylogenetic support for individual nodes . To determine the extent of spatial and temporal structure of DENV within the study area , Bayesian Maximum Clade Credibility ( MCC ) phylogenetic trees were inferred for the E gene sequences of DENV-1 , DENV-2 , and DENV-4 collected in Kamphaeng Phet during 2004–2007 using a Bayesian Markov Chain Monte Carlo ( MCMC ) method implemented in the BEAST package ( v1 . 6 . 1 ) [25] . The small number of DENV-3 sequences collected during the study precluded their inclusion in this analysis . A strict molecular clock , a TN93+Γ4 model of nucleotide substitution ( determined by ModelTest [24] to be the best-fit model of nucleotide substitution for the Kamphaeng Phet-specific DENV data sets ) with two codon position divisions ( 1+2 , 3 ) , and a constant population size model were used for all analyses . Mixing under more complex evolutionary and demographic models ( including a Bayesian skyline plot model ) was poor , and Bayes Factor tests indicated this model to be the most appropriate for each of the four serotypes independently . We used the BaTS ( Bayesian Tip-association Significance testing ) program to assess the extent of spatial and temporal structure among DENV populations in Kamphaeng Phet from the posterior samples of trees returned by the BEAST analysis described above [25] , [26] . The BaTS program outputs an Association Index ( AI ) and a Parsimony Score ( PS ) , for which 0 indicates complete population subdivision and 1 suggests random mixing ( panmixis ) , as well as a Maximum Clade ( MC ) score for each character state ( location , school , year , etc . ) that indicates the extent of clustering for that state compared to all others . Sequences were coded and tested for clustering by; ( i ) home sub-district ( 5 sub-districts; KNT - Kon Tee , NBK - Na Bo Kham , NKC - Nakon Chum , NPL - Nong Pling , TNK - Thep Na Korn ) , ( ii ) home village ( 4 to 9 per district ) , ( iii ) school ( 11 primary schools included in the cohort study and school-based surveillance , isolates from 12 additional schools obtained in the cluster study ) , ( iv ) age ( 0–4 , 5–9 , to 10–15 years ) , ( v ) sex , ( vi ) clinical syndrome of the patient ( includes non-hospitalized DF , hospitalized DF , hospitalized DHFII , hospitalized DHFIII , unknown syndrome ) and ( vii ) year of sampling . For age , sex , and clinical syndrome , mosquitoes were included as a separate group , and unknown clinical traits were recorded as NA . In the case of the school-based analysis , two coding schemes were utilized: ( 1 ) subjects with no school listed on file ( primarily children younger than school-age ) and viruses isolated from mosquitoes were coded as NA and mosquito , respectively , and ( 2 ) mosquitoes and subjects associated with no school were coded with the school of the index case of the cluster in which they were identified . Results using scheme 2 are reported here . Results were generally similar , but weaker , using scheme 1 . The cluster design of the study involved focal sampling over very narrow intervals of time and space and , therefore , had the potential to bias the results of BaTS analysis . To ensure that this study design was not adversely affecting our inference of spatial and temporal patterns , Bayesian MCMC trees were also inferred for DENV-1 and DENV-4 isolates obtained only from index cases and through school-based surveillance . Of the isolates obtained through school-based surveillance and not treated as index cases in the cluster study , only a single sequence from each school per year was utilized to account for potential over- or under-sampling among schools . Phylogeny-trait association tests were then performed on a subset of 37 DENV-1 sequences and 28 DENV-4 sequences , considering the home sub-district , village , school of subjects , and year of sampling . Too few sequences were available to perform this analysis on the DENV-2 data set after subsampling . DENV E-gene sequences were deposited in GenBank with the following accession numbers: DENV-1 JQ993108-JQ993204 , DENV-2 JQ993205-JQ993227 DENV-3 JQ993228-JQ993232 DENV-4 JQ993233-JQ993306 .
To investigate the genetic diversity and structure of DENV populations circulating in Kamphaeng Phet ( Figure 1 ) from 2004 to 2007 , we sequenced and analyzed the E genes of viruses collected during school-based surveillance and geographically-based community cluster studies . At least three serotypes circulated in our study population during each year of the study , with all four serotypes detected only in 2005 ( Table 1 ) . This pattern generally reflects the relative proportions of DENV isolated through passive surveillance across the region during the period of sampling , although passive surveillance data from the Kamphaeng Phet Provincial Hospital detected all four serotypes circulating each year ( Figure 2 ) . Years in which a given serotype was not detected in our study population corresponded to instances in which that serotype was detected at less than 10% frequency during passive surveillance . This suggests that our sampling and sequencing protocol adequately captures the diversity of the viral population circulating in KPP in a given year . Only a single genotype was represented for each serotype in this data set ( DENV-1 , genotype I; DENV-2 , Asian I genotype; DENV-3 , genotype II; DENV-4 , genotype I ) . These four genotypes have been the dominant circulating genotypes in Thailand and much of mainland Southeast Asia for several years , and were all present among previous Thai E gene sequences from Thailand dating back to 2001 and earlier . Although other genotypes were detected in Thailand prior to our study , only DENV-1 genotype I , DENV-2 Asian I genotype , and DENV-3 genotype II were isolated in a study performed in Kamphaeng Phet in 2001; no DENV-4 sequences were isolated during the 2001 study [13] . Thus , it appears that these were the dominant circulating genotypes in Kamphaeng Phet , at least through 2007 . Phylogenetic analysis revealed considerable viral genetic diversity within genotypes in the Kamphaeng Phet region during this period , demonstrated by the presence of phylogenetically distinct lineages within each serotype . Among the DENV-1 isolates detected , five major lineages were present , suggestive of at least five separate virus introductions prior to or during the sampling period ( Figures 3 and 4 ) . One lineage was detected briefly in 2005 , but was not observed in the following years . Three lineages circulated in 2006 , the year in which DENV-1 incidence greatly increased in the region ( Figure 2 ) . All of these lineages persisted into the 2007 season , during which one additional lineage was detected . With the exception of Lineage 4 , these viral populations were most closely related to viruses isolated in Thailand in the previous decade . Lineage 4 may have been a recent introduction from Singapore ( the most closely related geographical region on the phylogenetic tree ) that invaded Na Bo Kham sub-district and persisted within that population for at least two years . In all of the lineages except Lineage 2 , viruses commonly mixed among sub-districts within a single season . Despite the small number of sequences obtained , the DENV-2 population also showed considerable genetic diversity . Four lineages were detected between 2004 and 2007 , with little mixing detected among sub-districts within a given season ( Figures 3 and 5 ) . Interestingly , however , Lineage 2 was first detected in rural Na Bo Kham in 2005 , and was not isolated again until in 2007 in the most cosmopolitan of our populations , Nong Pling . This suggests that in this environment viral populations may sometimes move from rural to more densely populated areas rather than the opposite , as has been reported for other locations [8] , [9] . Although DENV-3 exhibited the lowest incidence in the study population during this time period , one lineage was detected in 2004 and a different lineage was detected in 2005 ( Figures 3 and 6 ) . Both of these were derived from lineages that were circulating in Thailand in the previous decade , which suggests that rare lineages may persist in rural populations even when disease incidence is low . DENV-4 was the dominant serotype detected in Kamphaeng Phet during the first two years of this study . Two lineages were present in 2004; one was represented by a single isolate detected in Na Bo Kham ( Lineage 3 ) , and the other was represented by three distinct , primarily sub-district-specific clusters , showing limited mixing in a given year ( Lineage 1 ) ( Figures 3 and 7 ) . Lineage 1 appeared to persist as the dominant DENV-4 lineage in the population throughout the study , although multiple distinct populations were present through 2007 . Two additional introductions were observed in the Na Bo Kham and Nakon Chum sub-districts in 2006 ( Lineage 4 and Lineage 2 , respectively ) . Based on the sequences in this study , these introductions did not appear to result in extended transmission in the region . Interestingly , while most lineages detected appeared to have entered following long-term transmission within Thailand , a single sequence isolated in Na Bo Kham in 2006 ( Lineage 4 ) may have entered through the re-introduction of a Thai lineage following extended transmission elsewhere in Southeast Asia . It is interesting that many of recent introduction events occurred in Na Bo Kham sub-district; this is the most rural of the populations studied and more distantly positioned and isolated relative to the other study populations . A previous study performed in Kamphaeng Phet during a single , high incidence dengue season detected the greatest genetic diversity among viruses in the most densely populated areas [13] , while the current study indicates the opposite . Because most of these lineages did not appear to immediately move into the greater population of Kamphaeng Phet , it is possible that the DENV populations present in Na Bo Kham differ greatly from the rest of the area because there is less mixing with the other populations we sampled . Alternatively , Na Bo Kham may depend on a different population center through which viral variants are introduced . Differences among DENV from Na Bo Kham and the rest of the sub-districts studied indicate that geographically distinct patterns of human movement are important processes in the structure and dynamics of DENV populations [27] . Among village clusters initiated by dengue cases detected in the school-based cohort , nearly all of the DENV viruses sequenced from both humans and mosquitoes within a cluster during the 14-day period following the initiation of sampling exhibited identical or nearly identical E gene sequences to the index case of their respective clusters . All but three of these sampling clusters involved viruses originating from the same lineage within a single serotype . The three exceptions are described here . First , in the case of DENV-1 , one cluster of three samples included an index case and one village contact that possessed identical E genes in Lineage 2 and a second village contact that was infected with a virus in Lineage 1 , which was found to be circulating simultaneously in multiple sub-districts during the 2006 and 2007 seasons . Second , a single DENV-1 virus was isolated from a cluster investigation in which the index case and all other village contacts were infected with DENV-4 . This DENV-1 isolate fell within Lineage 1 and was the first virus collected within a large phylogenetic cluster of identical E gene sequences isolated in the same village for the following seven to 31 days , from elsewhere in Nong Pling sub-district on days 62 and 97 after the first isolation , and from Thep Na Korn and Kon Tee sub-districts in the following 97 and nine to 108 days , respectively , spanning 2006 to 2007 . Third , among DENV-4 isolates , one cluster in Na Bo Kham in 2004 ( Lineage 1 ) included a virus of a divergent lineage ( Lineage 3 ) . Multiple closely related viruses were isolated from both humans and mosquitoes , forming a well-supported clade with other viruses from the same sub-district in the same season . A single mosquito was found to be infected with a divergent virus , comprising the only isolate of DENV-4 Lineage 3 detected in this study . That this lineage was only detected in a mosquito and we did not detect evidence of further transmission within the study population , as well as the number of lineages represented by a single isolate in the data set , suggests that multiple DENV lineages commonly enter this population in a single season , some of which fade out without detection , or may persist at low levels with little to no detection in the human population . Both the sequence data presented here and the epidemiological data collected from the cohort and cluster studies [12] suggest that DENV transmission is indeed highly spatially and temporally focal rather than occurring via simultaneous circulation and mixing of multiple DENV lineages across the region [18] . For confirmation , we performed a statistical analysis of the strength of phylogenetic clustering of viruses sampled closely in space and time . At all levels of spatial aggregation ( sub-district , village , school , and sampling cluster ) and for all three serotypes investigated ( DENV-1 , DENV-2 , and DENV-4 ) , we detected a significant relationship between phylogeny and space; i . e . , there was more clustering by spatial variable than expected by chance alone ( Table 3 ) . Similarly , we found a strong phylogenetic clustering of viruses by year of sampling . Hence , we conclude that viral genetic diversity in this population tends to turnover on an annual basis , although lineages may occasionally persist over multiple seasons . Among the levels of spatial aggregation analyzed , the home sub-district and village of subjects showed stronger phylogenetic clustering ( sub-district association index ( AI ) : 0 . 09 to 0 . 31 , parsimony score ( PS ) : 0 . 3 to 0 . 51; village AI: 0 . 28 to 0 . 5 , PS: 0 . 55 to 0 . 61 ) for all three serotypes than did the school ( AI: 0 . 45 to 0 . 62 , PS: 0 . 63 to 0 . 65 ) and sampling clusters ( AI: 0 . 34 to 0 . 67 , PS: 0 . 68 to 0 . 77 ) ( Table 3 ) . This result is not unexpected because epidemiological data suggest that DENV transmission in this region primarily occurs at a person's home or the home of a friend or relative [12] , [18] . Our virological data , therefore , support these earlier results and indicate that these trends continued over the four-year time period . No significant phylogenetic clustering of DENV was observed for age , sex , or clinical syndrome , except in the case of DENV-2 , for which relatively weak phylogenetic structure was detected according to clinical syndrome ( Table 3 ) . This result was strongly influenced , however , by the existence of two clusters of unknown syndrome and non-hospitalized DF , as well as , by the small number of DENV-2 sequences isolated in this study . It does not appear to result from differences in virulence among viral populations . The use of coding scheme 1 , in which subjects associated with no school and mosquitoes were coded as NA and mosquito , respectively , produced weaker associations in the school-based analysis than when these were coded according to the school of the respective index case ( data not shown ) . Specifically , these sequences rarely showed clustering , but instead interrupted or reduced the size of school-based clusters . By restricting our analysis to those sequences obtained from index cases and through school-based surveillance , we were able to further assess whether highly focal sampling in the village-based cluster design was a primary factor influencing the spatial and temporal structure detected . Results of these analyses were generally similar to those obtained using the full set of sequences ( Table 4 ) . Significant clustering was observed at the sub-district , village , school and 100-meter radius cluster levels and within years for both DENV-1 and DENV-4 . The strength of village-level clustering was reduced , however , relative to that of sub-districts and schools ( Table 4 ) . Further , the diversity within this subsample was comparable to that observed in the full data set , with the loss of only a single lineage ( DENV-4 Lineage 3 ) in subsampling . These results indicate that distinct viral populations may be present in areas separated by only a few kilometers , and suggest that school-based virological surveillance alone captures much of the genetic diversity of DENV within a given area . As such , school-based surveillance may be a practical and efficient indicator of DENV circulating in communities in endemic areas . An important observation from our study is the strength of temporal DENV clustering by year . This suggests that seasonal bottlenecks commonly occur in this population and , hence , that only a subset of viral genetic diversity within a given year survives into the next . This may in turn result in regular reductions in population immunity and competition among viruses , thus allowing the introduction and dissemination of viruses from outside of the study area . While persistence of viral lineages and in situ evolution occur over multiple seasons in some cases , these processes are relatively limited compared to that of viral migration ( i . e . importation ) , which appears to occur via movement of infected people , play a dominant role in generating diversity within each serotype , and contribute to dynamics in patterns of DENV transmission . This finding is similar to that reported for Kamphaeng Phet in 2001 [13] , and further indicates that it is important to account for the entry and re-entry of DENV lineages from external populations when considering the potential response to vaccination programs . In the context of forthcoming vaccine trials , and given the potential for imperfect vaccination and complex immune interactions , these results imply that the risk of DENV transmission and severe disease within a community will not only be determined by the vaccination levels within that geographic area , but also by the vaccination status and movement of people in neighboring regions . | Long-term cohort studies of dengue virus ( DENV ) , the most common vector-borne viral disease of humans , are essential to understand the epidemiology and evolution of this important human pathogen , and may assist in predicting the evolutionary response to vaccination . We utilized DENV gene sequences and information on the locations and timing of infected children within a primary school-based cohort in Kamphaeng Phet , Thailand to investigate the spatial and temporal relationships among viruses isolated from 2004 to 2007 . We found that all four DENV serotypes circulated in the region during the study period , with the presence of multiple viral lineages within each serotype . Viruses sampled closely in time and space were generally very closely related . More genetic variation was observed across districts in a given year and within the same district across different years . The high genetic similarity among viruses during each season and the rare persistence of these lineages through multiple seasons suggest that seasonal reductions in the force of infection through changes in mosquito transmission and fluctuations in human population immunity are key factors shaping the genetic diversity of dengue virus diversity in this region . The importation of DENV by human movement from other populations is therefore an important generator of DENV diversity even in hyperendemic areas . | [
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] | 2013 | Frequent In-Migration and Highly Focal Transmission of Dengue Viruses among Children in Kamphaeng Phet, Thailand |
Carney complex ( CNC ) is an inherited neoplasia syndrome with endocrine overactivity . Its most frequent endocrine manifestation is primary pigmented nodular adrenocortical disease ( PPNAD ) , a bilateral adrenocortical hyperplasia causing pituitary-independent Cushing's syndrome . Inactivating mutations in PRKAR1A , a gene encoding the type 1 α-regulatory subunit ( R1α ) of the cAMP–dependent protein kinase ( PKA ) have been found in 80% of CNC patients with Cushing's syndrome . To demonstrate the implication of R1α loss in the initiation and development of PPNAD , we generated mice lacking Prkar1a specifically in the adrenal cortex ( AdKO ) . AdKO mice develop pituitary-independent Cushing's syndrome with increased PKA activity . This leads to autonomous steroidogenic genes expression and deregulated adreno-cortical cells differentiation , increased proliferation and resistance to apoptosis . Unexpectedly , R1α loss results in improper maintenance and centrifugal expansion of cortisol-producing fetal adrenocortical cells with concomitant regression of adult cortex . Our data provide the first in vivo evidence that loss of R1α is sufficient to induce autonomous adrenal hyper-activity and bilateral hyperplasia , both observed in human PPNAD . Furthermore , this model demonstrates that deregulated PKA activity favors the emergence of a new cell population potentially arising from the fetal adrenal , giving new insight into the mechanisms leading to PPNAD .
Primary pigmented nodular adrenocortical disease ( PPNAD ) is a rare form of bilateral micronodular adrenocortical hyperplasia leading to high morbidity due to ACTH ( adreno corticotropic hormone ) -independent Cushing's syndrome . PPNAD may be either sporadic or regarded as the most frequent endocrine manifestation of Carney complex ( CNC ) , an autosomal dominant multiple neoplasia syndrome characterized by cardiac myxomas , spotty skin pigmentation and endocrine overactivity [1] . Cushing's syndrome in PPNAD is most diagnosed in children and young adults . Both isolated PPNAD and CNC have been associated with inactivating mutations in PRKAR1A , the gene encoding the type 1α regulatory subunit ( R1α ) of the cAMP-dependent protein kinase ( PKA ) [2] , [3] . Among CNC patients with Cushing's syndrome , the frequency of PRKAR1A mutations is about 80% . Tumour-specific loss of heterozygosity within the chromosomal region harboring PRKAR1A is observed in tumours from CNC patients and isolated PPNAD , suggesting that PRKAR1A is a potential tumour suppressor gene [4] . Because general homozygous loss of Prkar1a is lethal in early mouse embryos , various haploinsufficiency and tissue-specific knock-out models have been engineered to demonstrate its tumour suppressor activity [5] , [6] . General down-regulation of R1α levels has been achieved either in mouse lines heterozygous for a null allele of Prkar1a [6] , [7] or in a transgenic line carrying an inducible antisense-construct [8] . Both approaches indicate that haploinsufficiency for Prkar1a predisposes to tumour formation in a spectrum of endocrine and non-endocrine tissues that are cAMP-responsive; the mouse phenotype partially overlaps with the human one . However haploinsufficiency in mouse models does not appear to be sufficient to promote tumour formation in a subset of tissues known for their propensity to develop neoplasms in CNC patients . Thus , complete loss of Prkar1a using heart- , Schwann cell- or pituitary-specific knockouts was required to induce tumours in these tissues [9]–[11] . To date , although PPNAD is the most frequent endocrine disorder observed in CNC patients [12] , little is known on its pathophysiology . No clear adrenal lesions nor Cushing's syndrome were observed in mouse models of haploinsufficiency , suggesting that complete loss of Prkar1a might be required to phenocopy human phenotype . To address directly this question and obtain a possible mouse model for PPNAD , we produced mice with targeted Prkar1a gene inactivation in adreno-cortical cells by mating Prkar1a floxed mice with Akr1b7-Cre mouse line , a Cre expressing line allowing specific gene ablation in the steroidogenic lineage of the adrenals [13] . Adrenal cortex-specific Prkar1a knockout mice ( AdKO ) develop pituitary-independent Cushing's syndrome and evident signs of deregulated adreno-cortical cells differentiation and hyperplasia . These defects lead to improper maintenance and expansion of foetal adrenal cells in adult adrenals and establishment of tumoural conditions . Deregulation of the inhibin-activin signalling pathway seems to be implicated in this improper maintenance in AdKO mice model and in the human pathology . Our data provide the first in vivo evidence that the absence of R1α subunit of PKA is sufficient to induce the autonomous adrenal hyper-activity and bilateral hyperplasia observed in PPNAD . They also strongly suggest that deregulated PKA activity positively affects the maintenance of foetal characteristics in adult adrenal glands .
To assess the impact of complete loss of Prkar1a on adreno-cortical function and initiation of PPNAD , we crossed the Akr1b7:Cre line [13] with mice carrying the conditional null allele Prkar1aloxP [6] to produce adrenal cortex-specific KO mice of the Akr1b7:Cre;Prkar1aloxP/loxP genotype ( Figure 1A ) . In this study , these mice were referred to as AdKO mice , and wild-type mice ( WT ) were of the Prkar1aloxP/loxP genotype . The Prkar1aΔ2 allele ( KO allele ) was detected by PCR in the DNA extracted from AdKO adrenals but absent from gonads and WT tissues ( Figure 1B ) . As expected the intact conditional allele was still detected in the adrenals since Cre-mediated recombination was not supposed to occur in the whole organ but only in the cortex . Western blot and RT-QPCR analyses confirmed that Prkar1a gene expression was impaired in the adrenal glands of AdKO mice at both the mRNA ( 50% decrease ) and protein levels ( 60% decrease ) ( Figure S1A and Figure 1C ) when compared to WT . The 60% loss of R1α protein in adrenal tissue lysate of AdKO mice was accompanied by a significant increase in accumulation of R2β and C PKA subunits ( Figure 1C ) , a phenomenon that is also observed in PPNAD [8] . By contrast , no significant changes were observed at the mRNA levels , indicating that upregulation of R2β and C subunits , involved a post-trancriptional mechanism ( Figure S1B ) . We performed mRNA in situ hybridization and immunostaining to confirm that the decrease of Prkar1a gene expression was due to Cre-mediated gene ablation within the cortical compartment . As shown in Figure 1D , R1α mRNA signal was unaffected in medulla but was lost in the vast majority of cortical cells . These observations were confirmed at the protein level by R1α immunostaining ( Figure 1D ) . AdKO mice were born at expected Mendelian frequency and no difference in viability , weight or blood glucose values was observed up to 18 months of age when compared to WT mice ( data not shown ) . Adrenal endocrine function and histological differentiation were explored in groups of mice of both sexes at 5 , 10 and 18 months of age . Visual examination of AdKO females from the age of 10 months onwards revealed neck humps formed of large accumulations of adipose tissue . This “buffalo hump” phenotype was never observed in WT females ( Figure 2A ) nor in males of both genotypes ( not shown ) . Alteration of the repartition of fat depots is one of the features of “classic” Cushing's syndrome and is observed in PPNAD patients with PRKAR1A inactivation . In agreement with these observations , basal corticosterone levels in plasma were at least 2-fold higher in 10- and 18-month-old AdKO females than in age-matched WT , while no difference could be detected at 5 months ( Figure 2B ) . Basal corticosterone levels were not affected in AdKO males of 5 months ( 8 . 9±6 . 3 ng/mL in WT vs 7 . 8±5 . 0 ng/mL in AdKO ) , 10 months ( 8 . 5±6 . 5 ng/mL in WT vs 7 . 8±2 . 1 ng/mL in AdKO ) or 18 months of age ( 7 . 0±4 . 6 ng/mL in WT vs 8 . 2±4 . 2 ng/mL in AdKO ) . ACTH levels were measured in plasma of 10 months females . Importantly , ACTH levels measured in AdKO females were at least unchanged or tended to decrease ( 21±7 pg/mL in WT vs 14±7 pg/mL in AdKO ) , indicating that their basal hypercorticosteronaemia was independent of pituitary and likely resulted from primary adrenal overactivity . To explore the mechanism of hypercorticosteronaemia , AdKO mice that had not declared frank Cushing's syndrome , i . e . 5 months females and 10 months males , were injected with dexamethasone to induce a complete blockade of the hypothalomo-pituitary-adrenal ( HPA ) axis and subsequent suppression of ACTH production ( Figure 2C–2E ) . Dexamethasone suppression test led to the expected decrease of adrenal weight ( measured in females ) as well as cortical atrophy in WT mice but had no effect on AdKO adrenals ( Figure 2C , 2D and S2 ) . Moreover , corticosterone levels were undetectable in plasma of WT mice after dexamethasone treatment whereas they remained unaltered in AdKO mice ( Figure 2E ) . Finally , ACTH replacement in dexamethasone-treated mice restored corticosterone levels in WT and led to a further increase in AdKO mice , indicating that lack of R1α did not impair ACTH inducibility of steroidogenesis ( Figure S3 ) . Altogether , these data demonstrated that the adrenal glands of AdKO mice acquired the ability to secrete corticosterone in an autonomous manner leading to frank ( in females ) or subclinical ( in males ) ACTH-independent Cushing's syndrome . Glucocorticoid biosynthesis depends on the continuous ACTH stimulation of adrenal steroidogenic and detoxification genes , through the cAMP/PKA signalling pathway . We thus studied the expression level of ACTH-dependent ( Star , Akr1b7 , Cyp11a1 , Cyp11b1 ) and -independent ( Cyp11b2 ) genes in WT and AdKO adrenal glands ( Figure S4 ) . RT-QPCR showed that basal hypercorticosteronaemia found in 10 months AdKO females correlated with a significant increase in Star mRNA levels ( Figure S4B ) . A corresponding rise of StAR protein accumulation was confirmed by western blot ( Figure S4B , inset ) . Consistent with their milder phenotype , males did not show any significant change in the expression of steroidogenic genes . By contrast , when AdKO mice with subclinical Cushing's syndrome ( 5-month-old females and 10-month-old males ) were submitted to dexamethasone suppression test , most of the ACTH-responsive genes remained upregulated when compared to WT ( Figure S4C ) . As expected , Cyp11b2 gene expression remained unchanged , showing that this response of mutant mice depended on ACTH signalling . We then checked whether Prkar1a ablation in AdKO mice led to the expected increase in PKA signalling , by measuring the PKA kinase activity and CREB phosphorylation on ser133 residue . Kinase assays demonstrated that basal PKA activity ( in the absence of cAMP ) was increased in mutant adrenals while total activity ( in the presence of cAMP ) remained unchanged ( Figure 3A ) . In agreement with an increase in basal PKA activity , the amount of P-CREB in AdKO adrenals was doubled when compared to WT ( Figure 3B ) . All these converging data demonstrated that the adrenal cortex-specific ablation of the Prkar1a gene led to primary pituitary-independent hypercorticosteronaemia through enhancement of PKA signalling . Histological abnormalities consisting of large eosinophilic fœtal-like cells emerging from the innermost part of the adrenal cortex were detected in 5-month-old AdKO mice ( Figure 4A and 4D ) . Eosinophilic cells appeared clearly hypertrophic when compared to WT spongiocytes ( 229±42 µm2 vs 110±15 , p<0 . 01 ) ( Figure 4H and 4K , insets ) . This hypertrophic cell population expanded centrifugally to represent more than 50% of the cortex at 10 months ( Figure 4B and 4E ) and most of the cortex by 18 months ( Figure 4C and 4F ) . Simultaneously , neighbouring zona fasciculata cells , still arranged in tighly packed cords in 5-month-old mutant mice , became gradually disorganized in 10-month-old adrenals and appeared completely atrophic at 18 months ( Figure 4G–4L ) . At this stage , the zona glomerulosa no longer appeared as a continuous layer of cells but as groups of glomeruli , isolated from each other by small hyperplastic spindle-shaped basophilic cells , arising from the subcapsular region ( Figure 4L ) . This region contains adrenal stem/progenitor cells ensuring the continuous renewal of the adult cortex [14]–[16] . We thus assessed possible changes in the contents of adrenocortical progenitors in AdKO females of 10 and 18 months of age by quantifying expression of the progenitor-specific marker Shh and Gli1 [16] and the potential stem cell marker Pod1 [17] . RT-QPCR analyses showed that expression of these genes were not affected by the genotype nor the age ( Figure S5 ) , suggesting that the number of adrenocortical progenitors may not be affected in AdKO mice . The different cell populations in this area were characterized by double immunostaining for Sf1 , a marker of steroidogenic lineage , and for β-catenin , a marker of subcapsular steroidogenic lineage mostly represented by zona glomerulosa cells [18] . Double immunostaining of WT adrenals from 18-month-old mice confirmed that both markers ( Sf1 staining in the nucleus and β-catenin mostly at the cell periphery ) colocalized in the cells of zona glomerulosa that formed a continuous layer in the outermost cortex ( Figure 4M ) . By contrast , in age-matched AdKO adrenals , the general disorganization of the innercortex ( Sf1-positive , β-catenin-negative cells ) and the discontinuous aspect of the subcapsular/glomerulosa zone ( Sf1/β-catenin-positive cells ) were obvious ( Figure 4N ) . A detailed view of this area showed hyperplastic spindle-shaped cells , both Sf1- and β-catenin-negative , were surrounding the Sf1- and β-catenin-positive glomeruli ( Figure 4O ) . In previous mouse models for adrenocortical tumours , the first signs of neoplastic transformation seemed to coincide with the emergence of Gata-4 positive cells growing centripetally from the subcapsular region [15] . Thus , we examined expression of this transcription factor by immunostaining on 18-month-old adrenal sections . As shown in Figure 4P-4Q , numerous cells with Gata-4 nuclear staining could be detected within the subcapsular hyperplastic region of AdKO adrenals while , as expected , very rare positive cells were observed in WT . To determine the mechanisms leading to early hyperplasia of the large eosinophilic cells and late hyperplasia of the small spindle-shaped cells , cellular proliferation within adrenal glands of 5–18 months mice was assessed by immunodetection of Ki67 ( Figure 5A ) . At 5 or 10 months , WT and AdKO adrenals did not show any difference in the number of Ki67 positive cells ( not shown ) . By contrast at 18 months , the number of proliferative cells was more than doubled in mutant adrenal cortex . All Ki67-positive cells were also and thus probably corresponded to the large eosinophilic cells but not to the Sf1-negative small spindle-shaped cells ( Figure 5B ) . The increased cell proliferation was only evident in late stage of the AdKO phenotype and then could not be sufficient to explain the hyperplasia of innercortex observed in 5-10-month-old mice . We thus tested the sensitivity of adrenocortical cells to apoptosis induced by dexamethasone injections in 5-month-old mice [19] . Although apoptotic cells , assessed by positive staining for cleaved caspase 3 , were detected within the cortex of both genotypes upon dexamethasone treatment , adrenal sections from AdKO mice showed 62% less apoptotic cells than WT ( Figure 5C ) . These data support the view that early hyperplasia observed in 5-10-month-old AdKO mice could be , at least in part , the result of decreased sensitivity to apoptosis . TGFβ superfamily members inhibin and activin play a critical role in the growth dynamics of transient zones in the developing adrenal of both human and mouse [20] , [21] . The expression of genes encoding the activin subunits ( Inhβa and Inhβb ) , the inhibin subunit ( Inhα ) and the activin-binding protein follistatin ( Fst ) were compared in WT and AdKO adrenals ( Figure 5D ) . The mRNA levels of inhibin subunit and follistatin were 2-fold higher in AdKO adrenals whereas expression of activin subunits remained unchanged . To assess the relevance of this observation in the human adrenal , we realised an immunostaining against INHIBIN-α on sections from normal ( 3 patients ) and PPNAD-affected adrenocortical tissues ( 5 patients ) ( Figure 5E and Figure S6 ) . Hypertrophic cells that form the nodules were strongly stained . By contrast , no significant signal could be detected in the surrounding tissue corresponding to zona fasciculata nor in sections of normal adrenal tissues . Hence , in both mice and humans , R1αdepletion in adrenal cortex led to increased expression of TGFβ members known for their antagonistic effects on apoptotic action of activins [22] . Adrenocortical-specific ablation of the Prkar1a gene led to expansion of hypertrophic eosinophilic cells emerging from the innermost part of the cortex , adjacent to adrenal medulla ( Figure 4 ) . We hypothesized that this cell population could originate from the X-zone , a transient zone of fœtal origin that regresses during the first pregnancy in female and at puberty in male mice [23] , [24] . Hence , we compared adrenal zonation in WT and mutant mice by using Akr1b7 and 20α-HSD immunostaining to delineate zona fasciculata and X-zone , respectively [25] , [26] ( Figure 6 and Figure S7 ) . Adrenal cortex from WT virgin females showed canonical concentric organization consisting of three adjacent zones: X-zone ( 20α-HSD-positive and Akr1b7-negative ) , zona fasciculata ( 20α-HSD-negative and Akr1b7-positive ) and zona glomerulosa ( 20α-HSD-negative and Akr1b7-negative ) ( Figure 6A ) . By contrast , in the adrenal gland of 5-month-old AdKO virgin females although the 20α-HSD-expressing cells remained adjacent to the medulla , the X-zone and zona fasciculata were now overlaping in the innermost part of the cortex , and some isolated cells co-expressed both 20α-HSD and Akr1b7 markers ( Figure 6B ) . As expected , in 10-month-old parous WT females , X-zone had completely regressed and 20α-HSD-expressing cells were no longer detected ( Figure 6C ) . By contrast , adrenal cortex from age-matched parous AdKO females showed a persistent large X-like-zone that has clearly expanded in a centrifugal direction ( Figure 6D ) . At this stage , most 20α-HSD positive cells of the X-like-zone also expressed Akr1b7 and the typical packed cords organization of zona fasciculata was no longer observed in the Akr1b7-positive/20α-HSD-negative remaining cortex . In 18-month-old females , the X-like-zone had further expanded and now represented most of the cortex . Akr1b7-positive/20α-HSD-negative cells were repelled to adrenal periphery ( Figure 6E ) . Interestingly , examination of the proliferative potential of the X-like-zone using double immunostaining showed that Ki67-positive/20α-HSD-positive cells could be found in both 10 and 18-month-old AdKO females ( Figure S8 ) . Many 20α-HSD positive cells were also detected in 18-month-old AdKO males although the observed phenotype was milder than in females ( Figure S9 ) . Indeed , most 20α-HSD-expressing cells were not Akr1b7-positive and the overlap of X-like-zone with zona fasciculata was limited to the innermost cortex . In mouse , natural X-zone regression at puberty can be suppressed by castration of pre-pubescent males . To explore the possible reasons for the less pronounced phenotype in AdKO males , we tested whether gonadectomy occuring at 3-weeks of age ( before natural X-zone regression ) could accelerate the onset of the adrenal defects in 3-month-old adult males ( Figure S10 ) . 20α-HSD/Akr1b7 co-immunostaining showed that castration allowed the maintenance of a classical X-zone in WT , and of a X-like-zone overlaping fasciculata in AdKO adult males . When compared to shame-operated AdKO males of the same age , gonadectomized AdKO males showed a high number of Akr1b7-positive/20α-HSD-positive cells , these cells being never observed in gonadectomized WT males ( Figure S10A ) . Consistently , corticosterone levels were more elevated in gonadectomized than in shame-operated AdKO males ( Figure S10B ) . By contrast , gonadectomy had no impact on corticosterone levels in WT males . These data suggested that gonadectomy was able to amplify the phenotype in male AdKO mice . The persistence of X-zone marker suggested that foetal characteristics were maintained throughout adult life of AdKO mice . Cyp17 is a steroidogenic enzyme involved in the biosynthesis of precursors of both sex steroids and cortisol . In rodents , however , Cyp17 is only transiently expressed in the foetal adrenal and is therefore considered a foetal marker [27] . RT-QPCR analyses showed that , as opposed to WT , most adult AdKO adrenal glands expressed high levels of Cyp17 transcripts ( Figure 7A ) . In addition , Cyp17 positive immunostaining was detected within the innercortex of AdKO adrenal glands ( Figure 7B ) . More importantly , this expression led to the production of a functional Cyp17 enzyme since AdKO mice produced detectable levels of cortisol ( Figure 7C ) . Because both corticosterone and cortisol production required the continuous expression of genes encoding Cyp21 and Cyp11b1 biosynthetic enzymes , we examined whether their expressions were maintained throughout the progression of the AdKO phenotype ( Figure S11 ) . RT-QPCR analyses demonstrated that Cyp21 and Cyp11b1 expression levels were unchanged with age in both WT or AdKO females . Altogether , these data demonstrate that adrenal-specific ablation of Prkar1a altered the differentiation program of the adult cortex by promoting the improper maintenance and centrifugal expansion of steroidogenic competent foetal-like cells , that had the capacity to proliferate and to produce glucocorticoids ( cortisol and corticosterone ) .
Here , we shown that the adrenal-specific ablation of Prkar1a , the Carney Complex gene 1 ( CNC1 ) , in mouse reproduced the essential features of PPNAD observed in humans carrying PRKAR1A mutations . AdKO mice developed ACTH-independent Cushing's syndrome and cortical hyperplasia combined with atrophic areas that are typical hallmarks of PPNAD [1] . This mouse model definitively proves the central role of PRKAR1A gene defects in the etiology of PPNAD . Furthermore , the discovery of an unexpected role of Prkar1a in the repression of foetal features in adrenal cortex provides novel mechanistic insight into the cellular dynamics leading to definitive adrenal tissue or , when disturbed , to morbid hyperplasia . AdKO mice phenocopied most of the features of adrenal overactivity seen in patients . From a clinical point of view , PPNAD is difficult to diagnose because Cushing's syndrome usually develops slowly . Hypercortisolism may be mild or even periodic , with no clear decrease in plasma ACTH levels [28]–[30] . Adrenal-specific disruption of Prkar1a triggered subclinical hypercorticosteronaemia revealed upon blockade of pituitary ACTH , in 5-month-old mice . Around one year of age , it evolved into frank Cushing's syndrome with low , but still detectable levels of plasma ACTH . Contrasting with PPNAD , we did not detect any paradoxical rise in corticosterone levels after dexamethasone injection in AdKO mice , but only a resistance to ACTH blockade . Paradoxical response would rely on increased expression of the glucocorticoid receptor ( GR ) that was shown to activate PKA in PPNAD nodules [31] , [32] . We did not find any AdKO-dependent increase in GR expression neither by measuring mRNA levels nor by immunohistochemical analyses ( not shown ) . This paradoxical response could likely be a feature of human cells since adrenal cultures from Prkar1a haploinsufficient mice did not show paradoxical dexamethasone response in perifusion experiments [32] . Another discrepancy between AdKO adrenals and PPNAD was the absence of pigmentation in the mice glands . Hyper-pigmentation in PPNAD nodules relies on the accumulation of lipofuscin and is a consequence of autophagic deficiency [33] , [34] . This decreased autophagy was thought to originate from the R1α loss and consecutive activation of mTOR signalling [35] . Lipofuscin is made of aldehyde-linked protein residues that make it non-degradable and that form under chronic mild oxidative stress conditions , at a rate inversely related to the average lifespan of species [36] . We thus speculate that more efficient enzymatic defenses against reactive aldehydes forming aducts [25] and/or shorter lifespan might preserve mice from adrenal lipofuscin accumulation under R1α depletion . The cytomegalic aspect of eosinophilic cells arising from the innercortex is a hallmark of hyperplasia seen in PPNAD patients or in AdKO mice , and could be linked to unbuffered mTOR activity [35] , [37] that is a prerequisite to increased cell size [38] . Works are in progress to explore the contribution of this pathway in the pathophysiology of the AdKO model . Although they were attributed to the lack of R1α regulatory subunit of PKA , until now , the mechanisms leading to adrenal overactivity in PPNAD were not clear . The PKA heterotetrameric holoenzyme is composed of a dimer of regulatory subunits combined with two catalytic subunits . When the regulatory subunits bind cAMP , they dissociate from the catalytic subunits , which in turn , exhibit their kinase activity [39] . In previous knockout mouse models with general loss of R1α , basal PKA activity ( linked to free catalytic subunits only ) measured in embryos was found increased whereas total PKA activity ( cAMP-stimulated ) was decreased [5] . When measured in mouse embryonic fibroblasts both activities were increased [40] . The net impacts of R1α depletion on PKA activity could therefore depend on the cell type or tissue context . Consistent with these studies , specific depletion of R1α in adrenals mainly triggered a rise in basal PKA activity , attested both by increased catalytic activity and CREB phosphorylation . This resulted in a net gain of Star gene expression and therefore increased basal steroidogenesis . In agreement with our findings , StAR gene expression was found upregulated in a serial analysis of gene expression ( SAGE ) of PPNAD tissues [41] . The most intriguing phenotype observed during the follow-up of AdKO mice was an atypical hyperplasia of foetal-like cortex emerging at the corticomedullary junction which , over time , extended to the periphery . However , concomitant atrophy of the adult cortex resulted in net adrenal size equivalent to WT . These results were reminiscent of early histopathological studies of PPNAD , showing that micronodules seemed to arise from the medulla-cortex boundary . These were composed of eosinophilic giant cells that were surrounded by mostly atrophic cortex resulting in an otherwise normal-sized gland [1] , [42] , [43] . In mice , a transient cell layer , termed the X-zone , is adjacent to the medulla and regresses at puberty in males and at the first pregnancy in females [23] . Pioneer work from Morohashi‘s laboratory provided unequivocal genetic proofs that the X-zone was a remnant of foetal cortex forming before the definitive cortex and that distinct pools of precursor cells within the foetal cortex contributed to either the definitive cortex or the transient X-zone [24] , [44] . One pool of precursors activated transiently the foetal adrenal-specific enhancer of Ad4BP/Sf1 ( FAdE ) and contributed to the definitive cortex while the second pool maintained FAdE activated and contributed to the formation of the transient X-zone [24] . We showed previously that the developmental pattern of Akr1b7-Cre mediated recombination was reminiscent of that observed with the FAdE construct and that it occurred in both foetal and definitive adrenocortical cells [13] . Here , we provided evidence that loss of R1α during adrenal development resulted in two major abnormalities: unbuffered PKA activity leading to endocrine overactivity , and persistence of foetal-like cells that expanded across the adult cortex . In human , INHIBIN-α is more expressed in foetal than in adult adrenals [45] . In PPNAD , besides the hypertrophic aspect of the cells , the overexpression of INHIBIN-α specifically in the nodules could be interpreted as another sign of foetal origin of these cells . In mice , the foetal character of these hyperplastic and hypertrophic cells was attested both by persistent expression of 20α-HSD , an X-zone marker , and by re-expression of Cyp17 , an enzyme otherwise restricted to the embryonic period in rodent adrenals [26] , [27] . However , in contrast to natural X-zone cells that had no reported steroidogenic potential , the foetal-like hyperplastic cells of AdKO adrenals had acquired full steroidogenic competence of zona fasciculata cells and produced both corticosterone and cortisol . It is tempting to speculate that this ( hyper ) cortisolism , hitherto never described in mouse , could participate to the Cushing's syndrome of AdKO mice . Our data demonstrated that AdKO adrenals were less sensitive to apoptosis than WT . Apoptosis mediated by TGFβ family members largely contributed to the regression of both human foetal zone and mice X-zone in which both inhibin and follistatin opposed to the apoptotic signal triggered by activins [20] , [21] . This is in good agreement with our observation that inhibin-follistatin/activin transcripts ratio was augmented in AdKO adrenal glands and could therefore contribute to strengthen anti-apoptotic paracrine signals . In addition , R1α depletion could render foetal cells less sensitive to TGFβsignalling . This mechanism likely occured in human cells since we recently demonstrated that R1α knockdown in NCI-H295R adrenocortical cells enhanced their resistance to TGFβ-stimulated apoptosis [46] . Consistent with all these observations , here we showed an increased immunostaining for INHIBIN-α specifically in the nodules of PPNAD samples . Converging data in the literature highlight the importance of inhibin/activin system as a paracrine mediator of cAMP/ACTH signalling in both foetal and adult tissues [20] , [47]–[49] . Accordingly , maintaining derepression of PKA activity in the adrenal glands of AdKO mice from the foetal period to adulthood would favour the maintenance of high levels of inhibin . Interestingly , in a murine cell line postulated to originate from the X-zone [50] , inhibin was shown to counteract the repressive effect of activin on the Cyp17 gene [51] . This could provide a plausible but yet-non-demonstrated mechanism for re-expression of Cyp17 in the adult mutant gland of AdKO mice . Most adrenocortical tumours and Cushing's syndrome are more frequent in females than in males [52] . PPNAD does not escape this rule [12] . By the age of 40 years , more than 70% of the female carriers of PRKAR1A defects had clinical evidence of this disease , whereas only 45% of the male carriers were concerned . In addition , PPNAD was diagnosed at a younger age in females than in males . These clinical outcomes were strikingly reminiscent of the phenotype of AdKO mice that was earlier and more severe ( Cushing's syndrome and hyperplasia ) in females than in males . Although the influence of sex-specific hormones cannot be ruled out as suggested by the ( permissive ) aggravating effect of gonadectomy in AdKO males , some gender specificities of foetal cortex cells could account for these differences in mouse . First , foetal expression of Cyp17 was nearly completely down-regulated at E14 . 5 in males , whereas down-regulation occurred only at birth in females [53] . Second , post-natal foetal cortex , the X-zone , regressed at puberty in males but only during first pregnancy in females . Thus , foetal cells ( and among them , foetal/X-zone precursor cells ) remained for a longer time in the female cortex . Since our mouse model showed that R1α loss contributed to foetal-like cortex persistence and expansion , it is reasonable to assume that enrichment in foetal cells ( or foetal precursor cells ) predisposes AdKO females to manifest more severe PPNAD . To our knowledge , possible gender differences in foetal cortex dynamic changes have never been addressed in human adrenals . According to the centripetal model , cell renewal in the adrenal cortex depends on a common pool of stem/progenitor cells located in the periphery ( within the fibroblastic capsule and/or the glomerulosa ) which migrate centripetally from this zone , differenciate to successively adopt all the cortical fates and finally enter into apoptosis in the innermost cortex ( Figure 8 ) . According to cell lineage-tracing studies , two populations of progenitors contributed to the formation of the adult cortex , one located in the capsule expressed Gli1 and the second in a subcapsular position expressed Shh [16] . Although the role of capsule/subcapsule in the centripetal renewal of the adult definitive cortex is now fully established , there are also genetic evidences that in developing adrenal , the definitive cortex and the transient X-zone originate from different foetal precursors [24] , [44] . According to the centripetal model , dividing cells are essentially present at the periphery while apoptotic cells preferentially concentrate at the inner cortex ( [54] , reviewed in [17] ) . The balance between these two opposing gradients could be essential for homeostatic maintenance of the adrenal cortex i . e . the establishment of a centripetal differentiation . In AdKO mice , the centrifugal expansion of foetal-like cells with proliferative potential emerging from the inner cortex and the progressive atrophy of zona fasciculata could indicate that this balance is perturbed . A possible model may be proposed to illustrate these observations ( Figure 8 ) . Indeed , loss of R1α allowed the maintenance of cells of foetal features , which otherwise were transient . This maintenance could result from both an improvement of their proliferative capacity and from their decreased sensitivity to apoptotis and/or alteration of the apoptotic gradient ( as suggested by the increased expression of Inhα and Follistatin genes known to antagonise activins signalling ) . In addition , loss of R1α induced a progressive atrophy of the zona fasciculata in AdKO adrenals that was reminiscent to defects in cell renewal . Indeed , whereas the foetal-like cells undergo a continuous centrifugal expansion across the cortex , no gain in adrenal size was detected and no increase in cell apoptosis accompanied the concomitant atrophy of zona fasciculata . On the other hand , hyperplasia of non-steroidogenic subcapsular spindle-shaped cells was observed in elder mice ( 18 months ) and their accumulation eventually affected the integrity of zona glomerulosa . At least two non-exclusive mechanisms could account for defective cell renewal in the definitive cortex of AdKO mice: depletion of progenitor cells or impaired capacity of these progenitors to undergo centripetal differentiation and clonal replenishment of the cortex . The latter mechanism seems more likely in our model . Indeed , the expression levels of markers for stem/progenitor cells ( Figure S5 ) were unaltered in AdKO mice suggesting that progressive atrophy of the definitive cortex was not due to their depletion . Similar to observations made in most mouse adrenal tumour models , with age , adrenal glands of AdKO mice accumulated subcapsular Gata-4-positive spindle-shaped cells that are supposed to descend from multipotent progenitors capable to engage toward adrenal or gonadal fates [15] , [55] , [56] . In AdKO mice , late accumulation of Gata-4-positive cells could reflect an incapacity for the progenitor cells to properly differenciate into adrenocortical cells . This would prevent efficient renewal of the definitive cortex , which as a result , would become atrophic over time . An other hypothesis emerges from a recent report of Hammer and colleagues showing that inhibin-α prevented aberrant proliferation and differentiation of subcapsular adrenocortical progenitor cells [57] . Indeed , as opposed to Inhα−/− mice , adrenal glands of AdKO mice have increased inhibin-α expression . This is consistent with PPNAD samples and could therefore decrease proliferation/differentiation of progenitors . The possible dual role for inhibin-α in enhancing survival of foetal cells and impeding renewal of definitive cortex will have to be demonstrated in AdKO mice in an Inhα−/− context . In a symmetric point of view , the slow but continuous centrifugal expansion of hypertrophic foetal-like cells , would imply that a reservoir of foetal cortex precursor cells could lay in the juxtamedullary region and that differentiating steroidogenic foetal cells could emerge and replenish the cortex ( Figure 8 ) . Lineage tracing experiments will be required to confirm this hypothesis . By developing a mouse model of PPNAD , we established for the first time that Prkar1a , the Carney Complex gene 1 , not only controls adrenocortical endocrine activity but also prevents the maintenance of foetal remnants . The loss of R1α acts , at least , by increasing PKA activity and possibly by PKA independent effects mediated through alteration of protein interactions that remain to be deciphered [35] , [58] . The data existing in the literature and our present results strongly suggest a role for the inhibin-activin signalling pathway in the progression of the disease . Adrenal hyperplasia observed in PPNAD is classified as a neoplastic lesion . Although we showed that R1α loss induced tumoural conditions in adrenal glands ( resistance to apoptosis , cell hypertrophy , mild proliferation ) , profound alterations in zonal differentiation and cell renewal suggest that PPNAD should also be considered as a developmental disease .
Informed signed consent for the analysis of adrenal tissue and for genetic diagnosis was obtained from the patients and the study was approved by an institutional review board ( Comité Consultatif de Protection des Personnes dans la Recherche Biomédicale , Cochin Hospital , Paris ) . PPNAD paraffin sections were performed from adrenal samples of patients with isolated PPNAD or PPNAD with Carney complex who underwent bilateral adrenalectomy for ACTH-independent Cushing's syndrome . All patients carried germline inactivating mutations of the PRKAR1A gene . Animal studies were done in agreement with standards described by the NIH Guide for Care and Use of Laboratory Animals as well as with the local laws and regulations applicable to animal manipulations in France . For all analyses , groups of 17–20 mice of each genotype ( WT and AdKO ) and each age ( 5 , 10 and 18 months ) were constituted . Adult mice were injected s . c . with vehicle ( sesame oil ) , dexamethasone acetate for 4 days ( 75 µg twice daily; Sigma-Aldrich , L'Isle d'Abeau Chesnes , France ) and injected i . m . with long-acting ACTH ( 1 . 2 U , Synacthene , Novartis Pharma S . A . , Rueil-Malmaison , France ) the day before and in the morning of the experiment . Mouse genomic DNA ( from tail , adrenal or gonad ) was extracted and analyzed by PCR . Genotyping for the 0 . 5 Akr1b7-Cre transgene was carried out using the following conditions: 94°C , 45 s; 55°C , 45 s; 72°C , 45 s for a total of 40 cycles ( primers: 5′-CCTGGAAAATGCTTCTGTCCG-3′; 5′-CAGGGTGTTATAAGCAATCCC-3′ ) and Prkar1aloxP/loxP intact or knockout allele were genotyped using the following conditions: 94°C , 90 s; 58°C , 90 s; 72°C , 90 s for a total of 35 cycles ( primer a: 5′- CACTGCAGGGGCCTATTTTA -3′; primer b: 5′-TGTCTAGCTTGGGGTGGACT-3′ , primer c: 5′-CATCCATCTCCTATCCCCTTT-3′ ) . Mice were sacrified by decapitation at 8–9 am with minimum handling ( within 1 min ) , trunk blood was collected in eppendorf tubes containing 5 µL EDTA 0 . 5 M and placed immediately at 4°C . Samples were spun down at 4000 g for 5 min at 4°C and the resultant plasma was stored at −20°C for corticosterone or cortisol analysis , or at −80°C for ACTH analysis . Corticosterone concentrations in plasma were determined by radioimmunoassay ( RIA ) using a commercially available kit ( ICN Biomedicals , Orsay , France ) . ACTH dosage in plasma were performed by solid-phase , two-site sequentiel chemiluminescent immunometric assay ( Siemens Healthcare Diagnostic SAS , Saint-Denis , France ) using an Immulite 2000 analyzer . Cortisol concentrations were determined by electrochemiluminescence immunoassay ( Roche Diagnostic , Meylan , France ) using a Modular Analytics E170 analyzer . PKA activity was quantified in freshly dissected adrenals using the following commercial kit: PepTag assay for non-radioactive detection of cAMP-dependant protein kinase ( Promega Corp . , Charbonnière , France ) . Total RNA and DNA ( for genotype confirmation ) were isolated from tissue with the Qiagen DNA/RNA Mini kit ( Qiagen , Courtaboeuf , France ) . Total RNAs ( 1 µg ) were reverse-transcribed by Moloney murine leukaemia virus reverse transcriptase ( Promega Corp . , Charbonnière , France ) according to the manufacturer's instructions . Quantitative real-time PCR was performed using the iCycler BioRad system and BioRad IQ5 optical system software ( BioRad , Marnes-la-Coquette , France ) under standard conditions ( 40 cycles of 95°C for 15 seconds and 60°C for 60 seconds ) . All primer/probe sets were obtained from Applied Biosystems: Prkar1a , Prkar1b , Prkar2a , Prkar2b , Prkaca , Star , Akr1b7 , Cyp11a1 , Cyp11b1 , Cyp11b2 , Cyp21 , Shh , Gli-1 , Pod-1 , Ppib , Cyp17 , Inhα , Inhβa , Inhβb , Fst ( Applied Biosystems , Courtaboeuf , France ) . For quantification of transcripts , all PCR were performed in triplicate and the DCt method was used to calculate mRNA levels relative to a Peptidylprolyl isomerase B ( Ppib ) standard . A 442 bp 3′untranslated part of the Prkar1a cDNAs was amplified using the following primers: 5′-GGGCGTTGGAATTACTGAGA-3′; 5′-CTCCCAAATAGAACCCGACA -3′; and subcloned in pGEM-T easy vector ( Promega Corp . , Charbonnière , France ) . Antisense riboprobes were synthesized and labelled with digoxigenin ( Boehringer Mannheim , Mannheim , Germany ) . Adrenals were fixed in 4% paraformaldehyde overnight , embedded in paraffin and sectioned . Sections were treated for 15 min with proteinase K ( 3 µg/ml ) at room temperature and washed with glycine ( 2 mg/ml ) and then with PBS . They were fixed with 4% paraformaldehyde for 5 min and washed with PBS . Samples were incubated in hybridization mix ( 50% formamide; 4x SSC; 10% Dextran sulphate; 1x Denhart's; Salmon sperm DNA 250 µg/ml; tRNA 250 µg/ml ) for 1 h at 42°C . Digoxygenin labelled probe was added to the hybridization mix and incubated overnight at 42°C . Slides were then treated to a series of washes in 2x SSC and 1x SSC at 42°C and 0 . 2x SSC at room temperature . Sections were washed in buffer 1 ( 150 mM NaCl; 100 mM Tris , pH 7 . 5 ) , blocked by Boehringer blocking reagent in buffer 1 then incubated 1 h at room temperature with peroxidase-conjugated anti-digoxygenin antibody . After several washes in buffer 2 ( 150 mM NaCl; 100 mM Tris , pH 9 . 5; 5 mM MgCl2 ) , peroxidase activity was detected by incubation with 0 . 18 mg/ml BCIP and 0 . 34 mg/ml NBT in buffer 2 . In situ hybridization slides were observed and photographed on an Axiophot microscope ( Carl Zeiss , Zurich , Switzerland ) . Adrenals were fixed overnight in 4% PFA and embedded in paraffin . Sections were then cut and deparaffinized in Histoclear . For general morphology , sections were stained with haematoxylin and eosin . For mouse-anti-human-INHIBIN-α immunodectection , unmasking solution was sodium citrate buffer 10 mM pH 6 , Tween 0 . 05% . For co-localisation experiments of Akr1b7/Ki67 with 20α-HSD , the following protocol of limit detection was used: deparaffinised sections were incubated for 20 min at 95°C with Unmasking Solution ( Vector Laboratories , Peterborough England ) . For the first detection , rabbit-anti-Akr1b7 antibody [59] ( 1/1000 ) or rabbit-anti-Ki67 ( 1/500 , Thermo Fischer Scientific , Elancourt , France ) was revealed using a secondary biotinylated goat anti-rabbit antibody , Vectastain ABC amplification kit ( Vector Laboratories , Peterborough England ) and TSA fluorescein HRP substrate ( Perkin Elmer , Courtaboeuf , France ) . For the second detection , slides were incubated with a rabbit-anti-20α-HSD antibody at 1/2000 ( kind gift from Dr Y . Weinstein , Ben-Gurion University , Israël ) revealed by goat anti-rabbit Alexa 555 at 1/1000 ( Molecular probes , Cergy Pointoise , France ) . Sections were then incubated 5 min with Hoechst at 1 µg/ml ( Sigma-Aldrich , L'Isle d'Abeau Chesnes , France ) , rinsed , mounted in PBS-glycerol , and photographed on an Axiophot microscope ( Carl Zeiss , Zurich , Switzerland ) . The following antibodies: Mouse-anti-R1α ( 1/50 , BD Biosciences , Le pont de Claix , France ) ; rabbit-anti-Sf1 ( 1/1000 , kind gift from Dr K . Morohashi , Kyushu University , Japan ) , rabbit-anti-Cyp17 ( 1/5000 , kind gift from Dr A . Conley , University of California , USA ) ; rabbit-anti-cleaved-Caspase-3 ( 1/400 , Cell signalling , Saint-Quentin-en-Yvelines , France ) ; goat-anti-GATA-4 ( 1/100 , Tebu-Santa Cruz , Le Perray en Yvelines , France ) , mouse-anti-β-catenin ( 1/500 , BD Biosciences , Le pont de Claix , France ) , mouse-anti-human-INHIBIN-α ( 1/75 , AbD Serotec , Oxford , UK ) were detected using the same protocol as Akr1b7 . The secondary biotinylated antibodies were donkey anti-goat to detect Gata-4 and sheep anti-mouse to detect R1α and β-catenin . Gata-4 detection was performed using the Novared Kit ( Abcys , Paris , France ) . For the double staining β-catenin/Sf1 , 20 min in 0 . 02% HCl are necessary to abolish the rest of the peroxidase activity after the first immuno-reaction . Detection of Cyp17 was done without incubation in unmasking solution . An InSitu Pro VSi ( Intavis AG ) automated processor was used for immunodetection . Adrenal samples and western blotting were done as described previously [60] . The Primary antibodies were used at the following dilutions: rabbit-anti-StAR ( 1/5000 , kind gift from Dr Stocco , Texas Tech University Health Sciences Center , USA ) ; mouse-anti-R1α ( 1/500 ) ; mouse-anti-R2α ( 1/1000 ) ; mouse-anti-R2β ( 1/1000 ) ; mouse-anti-Cαβ ( 1/1000 , BD Biosciences , Le Pont de Claix , France ) , rabbit-anti-CREB ( 1/1000 ) , rabbit-anti-P-CREB ( 1∶1000 , Cell signalling , Saint-Quentin-en-Yvelines , France ) ; rabbit-anti-βTubulin ( 1/1000 , Sigma-Aldrich , L'Isle d'Abeau Chesnes , France ) . Quantification of western blot signals was performed using the Quantity One software ( Biorad , Marnes la Coquette , France ) . For statistical analysis , a Student t test was performed to determine whether there were differences between the two groups . Mann-Whitney test was used in Figure 7A . A P value of 0 . 05 was considered significant . | Carney complex is a rare familial disease characterized by a predisposition to develop multiple endocrine tumors and highly morbid syndromes due to endocrine overactivities . Its most frequent endocrine manifestation , hypersecretion of glucocorticoids i . e . Cushing's syndrome , is caused by micronodular adrenal gland hyperplasia , an unusual neoplasia which combines both hyperplastic and atrophic areas . Inactivating mutations of the gene encoding the regulatory subunit 1α ( R1α ) of the cAMP–dependent protein kinase were frequently found in these patients , but the causal link between loss of R1α and onset of this adrenal disorder had not yet been established . Here , we describe the first mouse model mimicking this disease and provide mechanistic insights into endocrine overactivity and neoplastic transformation . Indeed , we show that lack of R1α induces autonomous expression of genes involved in steroid biosynthesis and resurgence of hyperplastic fetal-like cells with concomitant defects in cell renewal of the adult cortex . Our data therefore represent a substantial conceptual advance on the cellular dynamics involved in adrenal gland homeostasis . They suggest that regression of fetal structures may be important to establish normal endocrine functions and to allow cell renewal in the definitive cortex . Failure to clear out cells of fetal features in R1α-deficient adrenals leads to morbid hyperplasia . | [
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"diabetes",
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] | 2010 | Cushing's Syndrome and Fetal Features Resurgence in Adrenal Cortex–Specific Prkar1a Knockout Mice |
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